Gemma4.java

///usr/bin/env jbang "$0" "$@" ; exit $?
//JAVA 21+
//PREVIEW
//COMPILE_OPTIONS --add-modules=jdk.incubator.vector
//RUNTIME_OPTIONS --add-modules=jdk.incubator.vector -Djdk.incubator.vector.VECTOR_ACCESS_OOB_CHECK=0
//MAIN com.llama4j.Gemma4

// Gemma 4 inference in pure Java
// Author: Alfonso² Peterssen
// Based on Andrej Karpathy's llama2.c and minbpe projects
// Related project: https://github.com/mukel/llama3.java
//
// Supports GGUF models and multiple tensor formats
// Matrix-vector kernels use Java's Vector API
// CLI modes: --chat and --instruct
//
// Run:
// jbang Gemma4.java --help
package com.llama4j;

import jdk.incubator.vector.ByteVector;
import jdk.incubator.vector.FloatVector;
import jdk.incubator.vector.ShortVector;
import jdk.incubator.vector.VectorOperators;
import jdk.incubator.vector.VectorShape;
import jdk.incubator.vector.VectorSpecies;

import java.lang.reflect.Field;

import java.io.IOException;
import java.io.PrintStream;
import java.lang.foreign.Arena;
import java.lang.foreign.MemorySegment;
import java.lang.foreign.ValueLayout;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.FloatBuffer;
import java.nio.channels.FileChannel;
import java.nio.channels.ReadableByteChannel;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.StandardOpenOption;
import java.util.*;
import java.util.concurrent.TimeUnit;
import java.util.function.IntConsumer;
import java.util.function.IntFunction;
import java.util.random.RandomGenerator;
import java.util.random.RandomGeneratorFactory;
import java.util.stream.Collectors;
import java.util.stream.IntStream;

final class GGUF {
    private static final int GGUF_MAGIC = 0x46554747;
    private static final int DEFAULT_ALIGNMENT = 32; // must be a power of 2
    private static final int PARSE_BUFFER_SIZE = 1 << 20;
    private static final List<Integer> SUPPORTED_GGUF_VERSIONS = List.of(2, 3);
    private int magic;
    private int version;
    private int tensorCount; // uint64_t
    private int alignment;
    private int metadata_kv_count; // uint64_t
    private Map<String, Object> metadata;
    private Map<String, GGUFTensorInfo> tensorInfos;
    private long tensorDataOffset;

    public Map<String, Object> getMetadata() {
        return metadata;
    }

    public long getTensorDataOffset() {
        return tensorDataOffset;
    }

    public Map<String, GGUFTensorInfo> getTensorInfos() {
        return tensorInfos;
    }

    private static final class ChannelReader {
        private final ReadableByteChannel channel;
        private final ByteBuffer buffer;
        private long position;

        private ChannelReader(ReadableByteChannel channel, int bufferSize) {
            this.channel = channel;
            this.buffer = ByteBuffer.allocateDirect(bufferSize).order(ByteOrder.LITTLE_ENDIAN);
            this.buffer.limit(0);
            this.position = 0L;
        }

        long position() {
            return position;
        }

        private void ensure(int required) throws IOException {
            if (required > buffer.capacity()) {
                throw new IllegalArgumentException("Requested read " + required + " exceeds buffer capacity " + buffer.capacity());
            }
            if (buffer.remaining() >= required) {
                return;
            }
            buffer.compact();
            while (buffer.position() < required) {
                int read = channel.read(buffer);
                if (read < 0) {
                    throw new IOException("Unexpected EOF while reading GGUF metadata");
                }
            }
            buffer.flip();
        }

        byte readByte() throws IOException {
            ensure(Byte.BYTES);
            position += Byte.BYTES;
            return buffer.get();
        }

        short readShort() throws IOException {
            ensure(Short.BYTES);
            position += Short.BYTES;
            return buffer.getShort();
        }

        int readInt() throws IOException {
            ensure(Integer.BYTES);
            position += Integer.BYTES;
            return buffer.getInt();
        }

        long readLong() throws IOException {
            ensure(Long.BYTES);
            position += Long.BYTES;
            return buffer.getLong();
        }

        float readFloat() throws IOException {
            return Float.intBitsToFloat(readInt());
        }

        double readDouble() throws IOException {
            return Double.longBitsToDouble(readLong());
        }

        byte[] readBytes(int length) throws IOException {
            byte[] bytes = new byte[length];
            int copied = 0;
            while (copied < length) {
                if (!buffer.hasRemaining()) {
                    ensure(1);
                }
                int chunk = Math.min(length - copied, buffer.remaining());
                buffer.get(bytes, copied, chunk);
                copied += chunk;
                position += chunk;
            }
            return bytes;
        }

        void skipBytes(int length) throws IOException {
            int remaining = length;
            while (remaining > 0) {
                if (!buffer.hasRemaining()) {
                    ensure(1);
                }
                int chunk = Math.min(remaining, buffer.remaining());
                buffer.position(buffer.position() + chunk);
                remaining -= chunk;
                position += chunk;
            }
        }
    }

    public static Map<String, GGMLTensorEntry> loadTensors(FileChannel fileChannel, long tensorDataOffset, Map<String, GGUFTensorInfo> tensorInfos) throws IOException {
        Arena arena = Arena.global();
        MemorySegment tensorData = fileChannel.map(FileChannel.MapMode.READ_ONLY, tensorDataOffset, fileChannel.size() - tensorDataOffset, arena);
        Map<String, GGMLTensorEntry> tensorEntries = HashMap.newHashMap(tensorInfos.size());
        for (Map.Entry<String, GGUFTensorInfo> entry : tensorInfos.entrySet()) {
            GGUFTensorInfo ti = entry.getValue();
            long numberOfElements = FloatTensor.numberOfElementsLong(ti.dimensions());
            long sizeInBytes = ti.ggmlType().byteSizeFor(numberOfElements);
            MemorySegment memorySegment = tensorData.asSlice(ti.offset(), sizeInBytes);
            tensorEntries.put(ti.name(), new GGMLTensorEntry(tensorData, ti.name(), ti.ggmlType(), ti.dimensions(), memorySegment));
        }
        return tensorEntries;
    }

    public static GGUF loadModel(Path modelPath) throws IOException {
        try (FileChannel fileChannel = FileChannel.open(modelPath)) {
            return loadModel(fileChannel, modelPath.toString());
        }
    }

    public static GGUF loadModel(FileChannel fileChannel, String modelLabel) throws IOException {
        try (var ignored = Timer.log("Parse " + modelLabel)) {
            fileChannel.position(0L);
            GGUF gguf = new GGUF();
            ChannelReader reader = new ChannelReader(fileChannel, PARSE_BUFFER_SIZE);
            gguf.loadModelImpl(reader);
            return gguf;
        }
    }

    enum MetadataValueType {
        UINT8, INT8, UINT16, INT16, UINT32, INT32, FLOAT32, BOOL, STRING, ARRAY, UINT64, INT64, FLOAT64;

        private static final MetadataValueType[] VALUES = values();

        public static MetadataValueType fromIndex(int index) {
            return VALUES[index];
        }
    }

    private void loadModelImpl(ChannelReader reader) throws IOException {
        readHeader(reader);
        this.tensorInfos = HashMap.newHashMap(tensorCount);
        for (int i = 0; i < tensorCount; ++i) {
            GGUF.GGUFTensorInfo ti = readTensorInfo(reader);
            assert !tensorInfos.containsKey(ti.name);
            tensorInfos.put(ti.name, ti);
        }
        long position = reader.position();
        int padding = (int) ((getAlignment() - (position % getAlignment())) % getAlignment());
        skipBytes(reader, padding);
        this.tensorDataOffset = reader.position();
    }

    public record GGUFTensorInfo(String name, int[] dimensions, GGMLType ggmlType, long offset) {
    }

    private GGMLType readGGMLType(ChannelReader reader) throws IOException {
        int ggmlTypeId = readInt(reader);
        return GGMLType.fromId(ggmlTypeId);
    }

    private GGUF.GGUFTensorInfo readTensorInfo(ChannelReader reader) throws IOException {
        String name = readString(reader);
        assert name.length() <= 64;
        int n_dimensions = readInt(reader);
        assert n_dimensions <= 4;
        int[] dimensions = new int[n_dimensions];
        for (int i = 0; i < n_dimensions; ++i) {
            dimensions[i] = Math.toIntExact(readLong(reader));
        }
        GGMLType ggmlType = readGGMLType(reader);
        long offset = readLong(reader);
        assert offset % getAlignment() == 0;
        return new GGUF.GGUFTensorInfo(name, dimensions, ggmlType, offset);
    }

    private String readString(ChannelReader reader) throws IOException {
        int len = Math.toIntExact(readLong(reader));
        return new String(readBytes(reader, len), StandardCharsets.UTF_8);
    }

    private Pair<String, Object> readKeyValuePair(ChannelReader reader) throws IOException {
        String key = readString(reader);
        assert key.length() < (1 << 16);
        assert key.codePoints().allMatch(cp -> ('a' <= cp && cp <= 'z') || ('0' <= cp && cp <= '9') || cp == '_' || cp == '.');
        Object value = readMetadataValue(reader);
        return new Pair<>(key, value);
    }

    private Object readMetadataValue(ChannelReader reader) throws IOException {
        MetadataValueType valueType = readMetadataValueType(reader);
        return readMetadataValueOfType(valueType, reader);
    }

    void readHeader(ChannelReader reader) throws IOException {
        this.magic = readInt(reader);
        if (magic != GGUF_MAGIC) {
            throw new IllegalArgumentException("unsupported header.magic " + magic);
        }
        this.version = readInt(reader);
        if (!SUPPORTED_GGUF_VERSIONS.contains(version)) {
            throw new IllegalArgumentException("unsupported header.version " + version);
        }
        this.tensorCount = Math.toIntExact(readLong(reader));
        this.metadata_kv_count = Math.toIntExact(readLong(reader));
        this.metadata = HashMap.newHashMap(metadata_kv_count);
        for (int i = 0; i < metadata_kv_count; ++i) {
            Pair<String, Object> keyValue = readKeyValuePair(reader);
            assert !metadata.containsKey(keyValue.first());
            metadata.put(keyValue.first(), keyValue.second());
        }
    }

    private Object readArray(ChannelReader reader) throws IOException {
        MetadataValueType valueType = readMetadataValueType(reader);
        int len = Math.toIntExact(readLong(reader));
        switch (valueType) {
            case UINT8, INT8 -> {
                return readBytes(reader, len);
            }
            case UINT16, INT16 -> {
                short[] shorts = new short[len];
                for (int i = 0; i < len; ++i) {
                    shorts[i] = readShort(reader);
                }
                return shorts;
            }
            case UINT32, INT32 -> {
                int[] ints = new int[len];
                for (int i = 0; i < len; ++i) {
                    ints[i] = readInt(reader);
                }
                return ints;
            }
            case FLOAT32 -> {
                float[] floats = new float[len];
                for (int i = 0; i < len; ++i) {
                    floats[i] = readFloat(reader);
                }
                return floats;
            }
            case BOOL -> {
                boolean[] booleans = new boolean[len];
                for (int i = 0; i < len; ++i) {
                    booleans[i] = readBoolean(reader);
                }
                return booleans;
            }
            case STRING -> {
                String[] strings = new String[len];
                for (int i = 0; i < len; ++i) {
                    strings[i] = readString(reader);
                }
                return strings;
            }
            case ARRAY -> {
                Object[] arrays = new Object[len];
                for (int i = 0; i < len; ++i) {
                    arrays[i] = readArray(reader);
                }
                return arrays;
            }
            default -> throw new UnsupportedOperationException("read array of " + valueType);
        }
    }

    private Object readMetadataValueOfType(MetadataValueType valueType, ChannelReader reader) throws IOException {
        return switch (valueType) {
            case UINT8, INT8 -> readByte(reader);
            case UINT16, INT16 -> readShort(reader);
            case UINT32, INT32 -> readInt(reader);
            case FLOAT32 -> readFloat(reader);
            case UINT64, INT64 -> readLong(reader);
            case FLOAT64 -> readDouble(reader);
            case BOOL -> readBoolean(reader);
            case STRING -> readString(reader);
            case ARRAY -> readArray(reader);
        };
    }

    private MetadataValueType readMetadataValueType(ChannelReader reader) throws IOException {
        int index = readInt(reader);
        return MetadataValueType.fromIndex(index);
    }

    private byte[] readBytes(ChannelReader reader, int length) throws IOException {
        return reader.readBytes(length);
    }

    private void skipBytes(ChannelReader reader, int length) throws IOException {
        reader.skipBytes(length);
    }

    private byte readByte(ChannelReader reader) throws IOException {
        return reader.readByte();
    }

    private boolean readBoolean(ChannelReader reader) throws IOException {
        return readByte(reader) != 0;
    }

    private short readShort(ChannelReader reader) throws IOException {
        return reader.readShort();
    }

    private int readInt(ChannelReader reader) throws IOException {
        return reader.readInt();
    }

    private long readLong(ChannelReader reader) throws IOException {
        return reader.readLong();
    }

    private float readFloat(ChannelReader reader) throws IOException {
        return reader.readFloat();
    }

    private double readDouble(ChannelReader reader) throws IOException {
        return reader.readDouble();
    }

    public int getAlignment() {
        if (alignment != 0) {
            return alignment;
        }
        alignment = (int) metadata.getOrDefault("general.alignment", DEFAULT_ALIGNMENT);
        assert Integer.bitCount(alignment) == 1 : "alignment must be a power of two";
        return alignment;
    }
}

interface Timer extends AutoCloseable {
    @Override
    void close(); // no Exception

    static Timer log(String label) {
        return log(label, TimeUnit.MILLISECONDS);
    }

    static Timer log(String label, TimeUnit timeUnit) {
        return new Timer() {
            final long startNanos = System.nanoTime();

            @Override
            public void close() {
                long elapsedNanos = System.nanoTime() - startNanos;
                System.err.println(label + ": "
                        + timeUnit.convert(elapsedNanos, TimeUnit.NANOSECONDS) + " "
                        + timeUnit.toChronoUnit().name().toLowerCase());
            }
        };
    }
}

final class ModelLoader {

    private static Vocabulary loadVocabulary(Map<String, Object> metadata) {
        String[] tokens = (String[]) metadata.get("tokenizer.ggml.tokens");
        float[] scores = (float[]) metadata.get("tokenizer.ggml.scores");
        return new Vocabulary(tokens, scores);
    }

    public static Llama loadModel(Path ggufPath, int contextLength) throws IOException {
        try (var ignored = Timer.log("Load Gemma4 model")) {
            try (FileChannel fileChannel = FileChannel.open(ggufPath, StandardOpenOption.READ)) {
                GGUF gguf = GGUF.loadModel(fileChannel, ggufPath.toString());
                return loadModel(fileChannel, gguf, contextLength, true);
            }
        }
    }

    static Llama loadModel(FileChannel fileChannel, GGUF gguf, int contextLength, boolean loadWeightsFlag) throws IOException {
        Map<String, Object> metadata = gguf.getMetadata();

        Vocabulary vocabulary = loadVocabulary(metadata);
        GemmaTokenizer tokenizer = createTokenizer(metadata, vocabulary);

        int modelContextLength = (int) metadata.get("gemma4.context_length");
        if (contextLength < 0 || modelContextLength < contextLength) {
            contextLength = modelContextLength;
        }

        int embeddingLength = (int) metadata.get("gemma4.embedding_length");
        int numberOfHeads = (int) metadata.get("gemma4.attention.head_count");
        int numberOfLayers = (int) metadata.get("gemma4.block_count");

        int headSizeFull = (int) metadata.get("gemma4.attention.key_length");
        int headSizeSWA = (int) metadata.get("gemma4.attention.key_length_swa");
        int slidingWindow = (int) metadata.get("gemma4.attention.sliding_window");
        float logitSoftcapping = (float) metadata.getOrDefault("gemma4.final_logit_softcapping", 0f);
        float rmsNormEps = (float) metadata.getOrDefault("gemma4.attention.layer_norm_rms_epsilon", 1e-6f);
        float ropeTheta = (float) metadata.getOrDefault("gemma4.rope.freq_base", 1000000f);
        float ropeThetaSWA = (float) metadata.getOrDefault("gemma4.rope.freq_base_swa", 10000f);

        // MoE parameters
        int expertCount = (int) metadata.getOrDefault("gemma4.expert_count", 0);
        int expertUsedCount = (int) metadata.getOrDefault("gemma4.expert_used_count", 0);
        int expertFeedForwardLength = (int) metadata.getOrDefault("gemma4.expert_feed_forward_length", 0);

        // Per-layer feed forward lengths (scalar for uniform, array for variable)
        int[] feedForwardLength;
        Object ffnRaw = metadata.get("gemma4.feed_forward_length");
        if (ffnRaw instanceof int[] arr) {
            feedForwardLength = arr;
        } else {
            feedForwardLength = new int[numberOfLayers];
            Arrays.fill(feedForwardLength, (int) ffnRaw);
        }

        Map<String, GGUF.GGUFTensorInfo> tensorInfos = gguf.getTensorInfos();

        // Derive isSWA per layer from Q norm weight size (256 = SWA, 512 = full attention)
        boolean[] isSWA;
        Object swaRaw = metadata.get("gemma4.attention.sliding_window_pattern");
        if (swaRaw instanceof boolean[] arr) {
            isSWA = arr;
        } else {
            // Derive from tensor shapes: check Q norm size per layer
            isSWA = new boolean[numberOfLayers];
            for (int i = 0; i < numberOfLayers; i++) {
                GGUF.GGUFTensorInfo qNorm = tensorInfos.get("blk." + i + ".attn_q_norm.weight");
                if (qNorm != null) {
                    long qNormSize = FloatTensor.numberOfElementsLong(qNorm.dimensions());
                    isSWA[i] = (qNormSize == headSizeSWA);
                } else {
                    isSWA[i] = (i % 5 != 4); // fallback
                }
            }
        }

        // Derive per-layer KV head count from K weight shapes
        int[] numberOfKeyValueHeadsPerLayer = new int[numberOfLayers];
        for (int i = 0; i < numberOfLayers; i++) {
            GGUF.GGUFTensorInfo kWeight = tensorInfos.get("blk." + i + ".attn_k.weight");
            int headSize = isSWA[i] ? headSizeSWA : headSizeFull;
            if (kWeight != null) {
                long kDim = kWeight.dimensions()[1];
                numberOfKeyValueHeadsPerLayer[i] = (int) (kDim / headSize);
            } else {
                numberOfKeyValueHeadsPerLayer[i] = numberOfHeads; // fallback
            }
        }

        // Shared KV layers: last N layers reuse KV from earlier layers
        int sharedKvLayers = (int) metadata.getOrDefault("gemma4.attention.shared_kv_layers", 0);
        int nLayerKvFromStart = numberOfLayers - sharedKvLayers;

        int embeddingLengthPerLayer = (int) metadata.getOrDefault("gemma4.embedding_length_per_layer_input", 0);

        Llama.Configuration config = new Llama.Configuration(
                embeddingLength,
                feedForwardLength,
                numberOfLayers,
                numberOfHeads,
                numberOfKeyValueHeadsPerLayer,
                vocabulary.size(),
                contextLength,
                rmsNormEps,
                ropeTheta,
                ropeThetaSWA,
                headSizeFull,
                headSizeSWA,
                slidingWindow,
                logitSoftcapping,
                isSWA,
                nLayerKvFromStart,
                embeddingLengthPerLayer,
                expertCount,
                expertUsedCount,
                expertFeedForwardLength
        );

        if (!loadWeightsFlag) {
            return new Llama(config, tokenizer, null);
        }

        Map<String, GGMLTensorEntry> tensorEntries = GGUF.loadTensors(fileChannel, gguf.getTensorDataOffset(), tensorInfos);
        Llama.Weights qw = loadWeights(tensorEntries, config);
        return new Llama(config, tokenizer, qw);
    }

    public static Llama.Weights loadWeights(Map<String, GGMLTensorEntry> tensorEntries, Llama.Configuration config) {
        Pair<float[], float[]> ropeFreqsSWA = RoPE.precomputeFreqsCis(config.contextLength, config.headSizeSWA, config.ropeThetaSWA);
        FloatBuffer ropeFreqsBuf = toFloatBuffer(tensorEntries.get("rope_freqs.weight"));
        float[] modelRopeFreqs = new float[ropeFreqsBuf.remaining()];
        ropeFreqsBuf.get(modelRopeFreqs);
        Pair<float[], float[]> ropeFreqsFull = RoPE.precomputeFreqsCisFromFreqs(config.contextLength, config.headSizeFull, config.ropeTheta, modelRopeFreqs);
        return loadWeightsWithRoPE(tensorEntries, config, ropeFreqsSWA, ropeFreqsFull);
    }

    public static Llama.Weights loadWeightsWithRoPE(Map<String, GGMLTensorEntry> tensorEntries, Llama.Configuration config,
                                                     Pair<float[], float[]> ropeFreqsSWA, Pair<float[], float[]> ropeFreqsFull) {
        int numberOfLayers = config.numberOfLayers;

        FloatTensor tokenEmbeddingTable = loadQuantized(tensorEntries.get("token_embd.weight"));

        // Load per-layer output scale (scalar per layer)
        float[] layerOutputScale = new float[config.numberOfLayers];
        for (int i = 0; i < config.numberOfLayers; i++) {
            GGMLTensorEntry scaleEntry = tensorEntries.get("blk." + i + ".layer_output_scale.weight");
            if (scaleEntry != null) {
                layerOutputScale[i] = toFloatBuffer(scaleEntry).get(0);
            } else {
                layerOutputScale[i] = 1.0f;
            }
        }

        // Load per-layer embedding weights (if present)
        FloatTensor perLayerTokenEmbd = null;
        FloatTensor perLayerModelProj = null;
        FloatBuffer perLayerProjNorm = null;
        FloatTensor[] perLayerInpGate = null;
        FloatTensor[] perLayerProj = null;
        FloatBuffer[] perLayerPostNorm = null;

        if (config.embeddingLengthPerLayer > 0 && tensorEntries.containsKey("per_layer_token_embd.weight")) {
            perLayerTokenEmbd = loadQuantized(tensorEntries.get("per_layer_token_embd.weight"));
            perLayerModelProj = loadQuantized(tensorEntries.get("per_layer_model_proj.weight"));
            perLayerProjNorm = toFloatBuffer(tensorEntries.get("per_layer_proj_norm.weight"));
            perLayerInpGate = loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".inp_gate.weight"));
            perLayerProj = loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".proj.weight"));
            perLayerPostNorm = loadArrayOfFloatBuffer(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".post_norm.weight"));
        }

        // Load V weights (nullable: layers without V use K as V)
        FloatTensor[] wv = new FloatTensor[numberOfLayers];
        for (int i = 0; i < numberOfLayers; i++) {
            GGMLTensorEntry vEntry = tensorEntries.get("blk." + i + ".attn_v.weight");
            wv[i] = vEntry != null ? loadQuantized(vEntry) : null;
        }

        // Load MoE weights (if present)
        FloatTensor[] ffnGateInp = null;
        FloatBuffer[] ffnGateInpScale = null;
        FloatTensor[] ffnGateUpExps = null;
        FloatTensor[] ffnDownExps = null;
        FloatBuffer[] ffnDownExpsScale = null;
        FloatBuffer[] ffnPostNorm1 = null;
        FloatBuffer[] preFfwNorm2 = null;
        FloatBuffer[] ffnPostNorm2 = null;

        if (config.isMoE()) {
            ffnGateInp = loadArrayOfQuantized(numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_gate_inp.weight"));
            ffnGateInpScale = loadArrayOfFloatBuffer(numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_gate_inp.scale"));
            ffnGateUpExps = loadArrayOfQuantized(numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_gate_up_exps.weight"));
            ffnDownExps = loadArrayOfQuantized(numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_down_exps.weight"));
            ffnDownExpsScale = loadArrayOfFloatBuffer(numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_down_exps.scale"));
            ffnPostNorm1 = loadArrayOfFloatBuffer(numberOfLayers, i -> tensorEntries.get("blk." + i + ".post_ffw_norm_1.weight"));
            preFfwNorm2 = loadArrayOfFloatBuffer(numberOfLayers, i -> tensorEntries.get("blk." + i + ".pre_ffw_norm_2.weight"));
            ffnPostNorm2 = loadArrayOfFloatBuffer(numberOfLayers, i -> tensorEntries.get("blk." + i + ".post_ffw_norm_2.weight"));
        }

        return new Llama.Weights(
                tokenEmbeddingTable,
                loadArrayOfFloatBuffer(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".attn_norm.weight")),
                loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".attn_q.weight")),
                loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".attn_k.weight")),
                wv,
                loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".attn_output.weight")),
                loadArrayOfFloatBuffer(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".attn_q_norm.weight")),
                loadArrayOfFloatBuffer(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".attn_k_norm.weight")),
                loadArrayOfFloatBuffer(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".post_attention_norm.weight")),
                loadArrayOfFloatBuffer(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_norm.weight")),
                loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_gate.weight")),
                loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_down.weight")),
                loadArrayOfQuantized(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".ffn_up.weight")),
                loadArrayOfFloatBuffer(config.numberOfLayers, i -> tensorEntries.get("blk." + i + ".post_ffw_norm.weight")),
                toFloatBuffer(tensorEntries.get("output_norm.weight")),
                layerOutputScale,
                FloatBuffer.wrap(ropeFreqsFull.first()),
                FloatBuffer.wrap(ropeFreqsFull.second()),
                FloatBuffer.wrap(ropeFreqsSWA.first()),
                FloatBuffer.wrap(ropeFreqsSWA.second()),
                tensorEntries.containsKey("output.weight")
                        ? loadQuantized(tensorEntries.get("output.weight"))
                        : tokenEmbeddingTable,
                perLayerTokenEmbd, perLayerModelProj, perLayerProjNorm,
                perLayerInpGate, perLayerProj, perLayerPostNorm,
                ffnGateInp, ffnGateInpScale, ffnGateUpExps, ffnDownExps, ffnDownExpsScale,
                ffnPostNorm1, preFfwNorm2, ffnPostNorm2
        );
    }

    private static GemmaTokenizer createTokenizer(Map<String, Object> metadata, Vocabulary vocabulary) {
        int[] tokenTypes = (int[]) metadata.get("tokenizer.ggml.token_type");
        return new GemmaTokenizer(vocabulary, tokenTypes);
    }

    public static FloatTensor loadQuantized(GGMLTensorEntry entry) {
        GGMLType ggmlType = entry.ggmlType();
        long numElements = FloatTensor.numberOfElementsLong(entry.shape());
        return switch (ggmlType) {
            case Q8_0 -> new Q8_0FloatTensor(numElements, entry.memorySegment());
            case Q4_0 -> new Q4_0FloatTensor(numElements, entry.memorySegment());
            case Q4_1 -> new Q4_1FloatTensor(numElements, entry.memorySegment());
            case Q5_1 -> new Q5_1FloatTensor(numElements, entry.memorySegment());
            case Q4_K -> new Q4_KFloatTensor(numElements, entry.memorySegment());
            case Q5_K -> new Q5_KFloatTensor(numElements, entry.memorySegment());
            case Q6_K -> new Q6_KFloatTensor(numElements, entry.memorySegment());
            case F32 -> new F32FloatTensor(numElements, entry.memorySegment());
            case F16 -> new F16FloatTensor(numElements, entry.memorySegment());
            case BF16 -> new BF16FloatTensor(numElements, entry.memorySegment());
            case MXFP4 -> new MXFP4FloatTensor(numElements, entry.memorySegment());
            default -> throw new UnsupportedOperationException("Quantization format " + ggmlType);
        };
    }

    public static FloatTensor[] loadArrayOfQuantized(int size, IntFunction<GGMLTensorEntry> getTensorEntry) {
        FloatTensor[] array = new FloatTensor[size];
        for (int i = 0; i < size; i++) {
            array[i] = loadQuantized(getTensorEntry.apply(i));
        }
        return array;
    }

    public static FloatBuffer[] loadArrayOfFloatBuffer(int size, IntFunction<GGMLTensorEntry> getTensorEntry) {
        FloatBuffer[] array = new FloatBuffer[size];
        for (int i = 0; i < size; i++) {
            array[i] = toFloatBuffer(getTensorEntry.apply(i));
        }
        return array;
    }

    public static FloatBuffer toFloatBuffer(GGMLTensorEntry tensorEntry) {
        GGMLType ggmlType = tensorEntry.ggmlType();
        return switch (ggmlType) {
            case F32 -> tensorEntry.memorySegment().asByteBuffer().order(ByteOrder.LITTLE_ENDIAN).asFloatBuffer();
            default -> throw new UnsupportedOperationException("Conversion to " + ggmlType);
        };
    }
}

record Llama(Configuration configuration, GemmaTokenizer tokenizer, Weights weights) {
    public State createNewState() {
        State state = new State(configuration());
        state.latestToken = tokenizer.getSpecialTokens().get("<bos>");
        return state;
    }

    public static final class Configuration {
        public final int embeddingLength;
        public final int[] feedForwardLength; // per-layer (shared MLP)
        public final int numberOfLayers;
        public final int numberOfHeads;
        public final int[] numberOfKeyValueHeadsPerLayer; // per-layer KV head count
        public final int vocabularySize;
        public final int contextLength;
        public final float rmsNormEps;
        public final float ropeTheta;       // full attention RoPE theta
        public final float ropeThetaSWA;    // SWA RoPE theta
        public final int headSizeFull;      // head size for full attention layers
        public final int headSizeSWA;       // head size for SWA layers
        public final int slidingWindow;
        public final float logitSoftcapping;
        public final boolean[] isSWA;       // per-layer: true = SWA, false = full attention
        public final int nLayerKvFromStart; // first N layers have own KV cache, rest reuse
        public final int embeddingLengthPerLayer; // per-layer embedding dim (0 = disabled)
        // MoE fields
        public final int expertCount;           // 0 = dense model (no MoE)
        public final int expertUsedCount;       // top-k experts per token
        public final int expertFeedForwardLength; // expert FFN hidden dim

        public Configuration(int embeddingLength, int[] feedForwardLength, int numberOfLayers,
                             int numberOfHeads, int[] numberOfKeyValueHeadsPerLayer, int vocabularySize,
                             int contextLength, float rmsNormEps, float ropeTheta, float ropeThetaSWA,
                             int headSizeFull, int headSizeSWA, int slidingWindow,
                             float logitSoftcapping, boolean[] isSWA, int nLayerKvFromStart,
                             int embeddingLengthPerLayer,
                             int expertCount, int expertUsedCount, int expertFeedForwardLength) {
            this.embeddingLength = embeddingLength;
            this.feedForwardLength = feedForwardLength;
            this.numberOfLayers = numberOfLayers;
            this.numberOfHeads = numberOfHeads;
            this.numberOfKeyValueHeadsPerLayer = numberOfKeyValueHeadsPerLayer;
            this.vocabularySize = vocabularySize;
            this.contextLength = contextLength;
            this.rmsNormEps = rmsNormEps;
            this.ropeTheta = ropeTheta;
            this.ropeThetaSWA = ropeThetaSWA;
            this.headSizeFull = headSizeFull;
            this.headSizeSWA = headSizeSWA;
            this.slidingWindow = slidingWindow;
            this.logitSoftcapping = logitSoftcapping;
            this.isSWA = isSWA;
            this.nLayerKvFromStart = nLayerKvFromStart;
            this.embeddingLengthPerLayer = embeddingLengthPerLayer;
            this.expertCount = expertCount;
            this.expertUsedCount = expertUsedCount;
            this.expertFeedForwardLength = expertFeedForwardLength;
        }

        public boolean isMoE() { return expertCount > 0; }

        // For layers without own KV, return the layer whose cache to reuse
        public int kvSourceLayer(int layer) {
            if (layer < nLayerKvFromStart) return layer; // has own KV
            // Reuse the last KV layer of the same attention type
            return nLayerKvFromStart - (isSWA[layer] ? 2 : 1);
        }

        public boolean hasKv(int layer) {
            return layer < nLayerKvFromStart;
        }

        public int headSize(int layer) {
            return isSWA[layer] ? headSizeSWA : headSizeFull;
        }

        public int numberOfKeyValueHeads(int layer) {
            return numberOfKeyValueHeadsPerLayer[layer];
        }

        public int kvDim(int layer) {
            return numberOfKeyValueHeadsPerLayer[layer] * headSize(layer);
        }

        public int queryDim(int layer) {
            return numberOfHeads * headSize(layer);
        }

        public int maxHiddenDim() {
            return Arrays.stream(feedForwardLength).max().orElseThrow();
        }

        public Configuration withContextLength(int newContextLength) {
            return new Configuration(embeddingLength, feedForwardLength, numberOfLayers,
                    numberOfHeads, numberOfKeyValueHeadsPerLayer, vocabularySize,
                    newContextLength, rmsNormEps, ropeTheta, ropeThetaSWA,
                    headSizeFull, headSizeSWA, slidingWindow,
                    logitSoftcapping, isSWA, nLayerKvFromStart,
                    embeddingLengthPerLayer,
                    expertCount, expertUsedCount, expertFeedForwardLength);
        }
    }

    public static final class Weights {
        public final FloatTensor token_embedding_table;
        public final FloatBuffer[] rms_att_weight;       // (layer, dim)
        public final FloatTensor[] wq;                   // (layer, queryDim, dim)
        public final FloatTensor[] wk;                   // (layer, kvDim, dim)
        public final FloatTensor[] wv;                   // (layer, kvDim, dim) - null entry if V=K
        public final FloatTensor[] wo;                   // (layer, dim, queryDim)
        public final FloatBuffer[] attn_q_norm;          // (layer, headSize)
        public final FloatBuffer[] attn_k_norm;          // (layer, headSize)
        public final FloatBuffer[] post_attention_norm;  // (layer, dim)
        public final FloatBuffer[] rms_ffn_weight;       // (layer, dim) - shared MLP norm
        public final FloatTensor[] w1;                   // ffn_gate (layer, hiddenDim, dim)
        public final FloatTensor[] w2;                   // ffn_down (layer, dim, hiddenDim)
        public final FloatTensor[] w3;                   // ffn_up (layer, hiddenDim, dim)
        public final FloatBuffer[] post_ffw_norm;        // (layer, dim) - overall post-FFW norm
        public final FloatBuffer rms_final_weight;
        public final float[] layerOutputScale;
        // Full attention RoPE
        public final FloatBuffer freq_cis_real_full;
        public final FloatBuffer freq_cis_imag_full;
        // SWA RoPE
        public final FloatBuffer freq_cis_real_swa;
        public final FloatBuffer freq_cis_imag_swa;
        public final FloatTensor wcls;
        // Per-layer embedding weights
        public final FloatTensor perLayerTokenEmbd;
        public final FloatTensor perLayerModelProj;
        public final FloatBuffer perLayerProjNorm;
        public final FloatTensor[] perLayerInpGate;
        public final FloatTensor[] perLayerProj;
        public final FloatBuffer[] perLayerPostNorm;
        // MoE weights (null if dense model)
        public final FloatTensor[] ffnGateInp;        // router weight (layer, n_experts, n_embd)
        public final FloatBuffer[] ffnGateInpScale;   // router input scale (layer, n_embd)
        public final FloatTensor[] ffnGateUpExps;     // fused gate+up expert (layer, n_experts * 2*expert_ff, n_embd)
        public final FloatTensor[] ffnDownExps;       // down expert (layer, n_experts * n_embd, expert_ff)
        public final FloatBuffer[] ffnDownExpsScale;  // expert output scale (layer, n_experts)
        public final FloatBuffer[] ffnPostNorm1;      // shared MLP post norm (layer, dim) - MoE only
        public final FloatBuffer[] preFfwNorm2;       // MoE pre-norm (layer, dim)
        public final FloatBuffer[] ffnPostNorm2;      // MoE post norm (layer, dim)

        public Weights(FloatTensor token_embedding_table,
                       FloatBuffer[] rms_att_weight,
                       FloatTensor[] wq, FloatTensor[] wk, FloatTensor[] wv, FloatTensor[] wo,
                       FloatBuffer[] attn_q_norm, FloatBuffer[] attn_k_norm,
                       FloatBuffer[] post_attention_norm,
                       FloatBuffer[] rms_ffn_weight,
                       FloatTensor[] w1, FloatTensor[] w2, FloatTensor[] w3,
                       FloatBuffer[] post_ffw_norm,
                       FloatBuffer rms_final_weight,
                       float[] layerOutputScale,
                       FloatBuffer freq_cis_real_full, FloatBuffer freq_cis_imag_full,
                       FloatBuffer freq_cis_real_swa, FloatBuffer freq_cis_imag_swa,
                       FloatTensor wcls,
                       FloatTensor perLayerTokenEmbd, FloatTensor perLayerModelProj,
                       FloatBuffer perLayerProjNorm,
                       FloatTensor[] perLayerInpGate, FloatTensor[] perLayerProj,
                       FloatBuffer[] perLayerPostNorm,
                       FloatTensor[] ffnGateInp, FloatBuffer[] ffnGateInpScale,
                       FloatTensor[] ffnGateUpExps, FloatTensor[] ffnDownExps,
                       FloatBuffer[] ffnDownExpsScale,
                       FloatBuffer[] ffnPostNorm1, FloatBuffer[] preFfwNorm2,
                       FloatBuffer[] ffnPostNorm2) {
            this.token_embedding_table = token_embedding_table;
            this.rms_att_weight = rms_att_weight;
            this.wq = wq;
            this.wk = wk;
            this.wv = wv;
            this.wo = wo;
            this.attn_q_norm = attn_q_norm;
            this.attn_k_norm = attn_k_norm;
            this.post_attention_norm = post_attention_norm;
            this.rms_ffn_weight = rms_ffn_weight;
            this.w1 = w1;
            this.w2 = w2;
            this.w3 = w3;
            this.post_ffw_norm = post_ffw_norm;
            this.rms_final_weight = rms_final_weight;
            this.layerOutputScale = layerOutputScale;
            this.freq_cis_real_full = freq_cis_real_full;
            this.freq_cis_imag_full = freq_cis_imag_full;
            this.freq_cis_real_swa = freq_cis_real_swa;
            this.freq_cis_imag_swa = freq_cis_imag_swa;
            this.wcls = wcls;
            this.perLayerTokenEmbd = perLayerTokenEmbd;
            this.perLayerModelProj = perLayerModelProj;
            this.perLayerProjNorm = perLayerProjNorm;
            this.perLayerInpGate = perLayerInpGate;
            this.perLayerProj = perLayerProj;
            this.perLayerPostNorm = perLayerPostNorm;
            this.ffnGateInp = ffnGateInp;
            this.ffnGateInpScale = ffnGateInpScale;
            this.ffnGateUpExps = ffnGateUpExps;
            this.ffnDownExps = ffnDownExps;
            this.ffnDownExpsScale = ffnDownExpsScale;
            this.ffnPostNorm1 = ffnPostNorm1;
            this.preFfwNorm2 = preFfwNorm2;
            this.ffnPostNorm2 = ffnPostNorm2;
        }
    }

    public static final class State {
        public final FloatTensor x;      // activation at current time stamp (embeddingLength,)
        public final FloatTensor xb;     // same, but inside a residual branch (embeddingLength,)
        public final FloatTensor xb_k;   // attention output before wo projection (max queryDim,)
        public final FloatTensor xb2;    // an additional buffer (embeddingLength,)
        public final FloatTensor hb;     // buffer for hidden dimension in the ffn (maxHiddenDim,)
        public final FloatTensor hb2;    // buffer for hidden dimension in the ffn (maxHiddenDim,)
        public final FloatTensor q;      // query (max queryDim,)
        public final FloatTensor k;      // key (max kvDim,)
        public final FloatTensor v;      // value (max kvDim,)
        public final FloatTensor att;    // buffer for scores/attention values (n_heads, seq_len)
        public final FloatTensor logits; // output logits
        // kv cache - variable sizes per layer
        public final FloatTensor[] keyCache;   // (n_layer, seq_len, kvDim_per_layer)
        public final FloatTensor[] valueCache; // (n_layer, seq_len, kvDim_per_layer)
        // per-layer embedding buffers
        public final FloatTensor perLayerInputs;
        public final FloatTensor plGate;
        public final FloatTensor plProj;
        // MoE buffers
        public final FloatTensor routerLogits;    // (n_experts,)
        public final FloatTensor moeInput;        // (n_embd,) pre-normed MoE input
        public final FloatTensor moeOutput;       // (n_embd,) accumulated expert output
        public final FloatTensor expertGateUp;    // (2 * expert_ff,)
        public final FloatTensor expertDown;      // (n_embd,) single expert output

        public int latestToken;

        State(Configuration config) {
            int maxQueryDim = config.numberOfHeads * config.headSizeFull;
            int maxKVDim = IntStream.range(0, config.numberOfLayers).map(config::kvDim).max().orElse(0);
            int maxHiddenDim = config.maxHiddenDim();
            this.x = ArrayFloatTensor.allocate(config.embeddingLength);
            this.xb = ArrayFloatTensor.allocate(config.embeddingLength);
            this.xb_k = ArrayFloatTensor.allocate(maxQueryDim);
            this.xb2 = ArrayFloatTensor.allocate(config.embeddingLength);
            this.hb = ArrayFloatTensor.allocate(maxHiddenDim);
            this.hb2 = ArrayFloatTensor.allocate(maxHiddenDim);
            this.q = ArrayFloatTensor.allocate(maxQueryDim);
            this.k = ArrayFloatTensor.allocate(maxKVDim);
            this.v = ArrayFloatTensor.allocate(maxKVDim);
            this.att = ArrayFloatTensor.allocate(config.numberOfHeads, config.contextLength);
            this.logits = ArrayFloatTensor.allocate(config.vocabularySize);
            int plDim = config.embeddingLengthPerLayer;
            this.perLayerInputs = plDim > 0 ? ArrayFloatTensor.allocate(plDim * config.numberOfLayers) : null;
            this.plGate = plDim > 0 ? ArrayFloatTensor.allocate(plDim) : null;
            this.plProj = plDim > 0 ? ArrayFloatTensor.allocate(config.embeddingLength) : null;
            // MoE buffers
            if (config.isMoE()) {
                this.routerLogits = ArrayFloatTensor.allocate(config.expertCount);
                this.moeInput = ArrayFloatTensor.allocate(config.embeddingLength);
                this.moeOutput = ArrayFloatTensor.allocate(config.embeddingLength);
                this.expertGateUp = ArrayFloatTensor.allocate(2 * config.expertFeedForwardLength);
                this.expertDown = ArrayFloatTensor.allocate(config.embeddingLength);
            } else {
                this.routerLogits = null;
                this.moeInput = null;
                this.moeOutput = null;
                this.expertGateUp = null;
                this.expertDown = null;
            }
            // Only allocate KV caches for layers that have their own KV (not shared)
            this.keyCache = new FloatTensor[config.nLayerKvFromStart];
            this.valueCache = new FloatTensor[config.nLayerKvFromStart];
            for (int l = 0; l < config.nLayerKvFromStart; l++) {
                int kvDim = config.kvDim(l);
                keyCache[l] = ArrayFloatTensor.allocate(config.contextLength, kvDim);
                valueCache[l] = ArrayFloatTensor.allocate(config.contextLength, kvDim);
            }
        }
    }

    static float gelu(float x) {
        return (float) (0.5 * x * (1 + Math.tanh(Math.sqrt(2 / Math.PI) * (x + 0.044715 * Math.pow(x, 3)))));
    }

    static void rmsnorm(FloatTensor out, FloatTensor x, FloatBuffer weight, int size, float rmsNormEps) {
        float ss = x.reduce(0, size, 0f, (acc, xi) -> acc + xi * xi);
        ss /= size;
        ss += rmsNormEps;
        ss = (float) (1.0 / Math.sqrt(ss));
        final float finalss = ss;
        out.mapWithIndexInPlace(0, size, (value, index) -> weight.get(index) * (finalss * x.getFloat(index)));
    }

    static void rmsnorm(FloatTensor out, int outOffset, FloatTensor x, int xOffset, FloatBuffer weight, int size, float rmsNormEps) {
        float ss = 0f;
        for (int i = 0; i < size; i++) {
            float xi = x.getFloat(xOffset + i);
            ss += xi * xi;
        }
        ss /= size;
        ss += rmsNormEps;
        ss = (float) (1.0 / Math.sqrt(ss));
        for (int i = 0; i < size; i++) {
            out.setFloat(outOffset + i, weight.get(i) * ss * x.getFloat(xOffset + i));
        }
    }

    // Bare RMS norm without learned weights (just normalize to unit RMS)
    static void rmsnormNoWeight(FloatTensor out, int outOffset, FloatTensor x, int xOffset, int size, float rmsNormEps) {
        float ss = 0f;
        for (int i = 0; i < size; i++) {
            float xi = x.getFloat(xOffset + i);
            ss += xi * xi;
        }
        ss /= size;
        ss += rmsNormEps;
        ss = (float) (1.0 / Math.sqrt(ss));
        for (int i = 0; i < size; i++) {
            out.setFloat(outOffset + i, ss * x.getFloat(xOffset + i));
        }
    }

    static FloatTensor forward(Llama model, Llama.State state, int token, int position) {
        Llama.Configuration config = model.configuration();
        Llama.Weights weights = model.weights();
        int dim = config.embeddingLength;
        float sqrtDim = (float) Math.sqrt(dim);

        // copy the token embedding into x
        weights.token_embedding_table.copyTo(token * dim, state.x, 0, dim);
        state.x.mapInPlace(v -> v * sqrtDim);

        // Compute per-layer inputs (if model has per-layer embeddings)
        int plDim = config.embeddingLengthPerLayer;
        int plTotal = plDim * config.numberOfLayers;
        if (plDim > 0 && weights.perLayerTokenEmbd != null) {
            float sqrtPlDim = (float) Math.sqrt(plDim);
            float projScale = (float) (1.0 / Math.sqrt(dim));
            float inputScale = (float) (1.0 / Math.sqrt(2.0));

            // Project x through perLayerModelProj, scale, and RMS norm per chunk
            weights.perLayerModelProj.matmul(state.x, state.perLayerInputs, plTotal, dim);
            state.perLayerInputs.mapInPlace(0, plTotal, v -> v * projScale);
            for (int l = 0; l < config.numberOfLayers; l++) {
                rmsnorm(state.perLayerInputs, l * plDim, state.perLayerInputs, l * plDim,
                        weights.perLayerProjNorm, plDim, config.rmsNormEps);
            }

            // Add per-layer token embedding scaled by sqrt(plDim)
            long tokEmbOffset = (long) token * plTotal;
            for (int i = 0; i < plTotal; i++) {
                float tokEmb = weights.perLayerTokenEmbd.getFloat(tokEmbOffset + i) * sqrtPlDim;
                state.perLayerInputs.setFloat(i, state.perLayerInputs.getFloat(i) + tokEmb);
            }

            // Scale combined input by 1/sqrt(2)
            state.perLayerInputs.mapInPlace(0, plTotal, v -> v * inputScale);
        }

        // forward all the layers
        for (int l = 0; l < config.numberOfLayers; l++) {
            boolean layerIsSWA = config.isSWA[l];
            int headSize = config.headSize(l);
            int halfHead = headSize / 2;
            int queryDim = config.queryDim(l);
            int kvDim = config.kvDim(l);
            int hiddenDim = config.feedForwardLength[l];

            // attention rmsnorm
            rmsnorm(state.xb, state.x, weights.rms_att_weight[l], dim, config.rmsNormEps);

            // Q projection + per-head RMS norm
            weights.wq[l].matmul(state.xb, state.q, queryDim, dim);
            for (int h = 0; h < config.numberOfHeads; h++) {
                rmsnorm(state.q, h * headSize, state.q, h * headSize, weights.attn_q_norm[l], headSize, config.rmsNormEps);
            }

            // RoPE (NeoX style: SWA layers use different frequencies than full attention)
            FloatBuffer freqsReal = layerIsSWA ? weights.freq_cis_real_swa : weights.freq_cis_real_full;
            FloatBuffer freqsImag = layerIsSWA ? weights.freq_cis_imag_swa : weights.freq_cis_imag_full;
            for (int h = 0; h < config.numberOfHeads; ++h) {
                int poffset = h * headSize;
                for (int i0 = 0; i0 < headSize; i0 += 2) {
                    int ic = i0 / 2;
                    float fcr = freqsReal.get(position * halfHead + ic);
                    float fci = freqsImag.get(position * halfHead + ic);
                    float v0 = state.q.getFloat(poffset + ic);
                    float v1 = state.q.getFloat(poffset + ic + halfHead);
                    state.q.setFloat(poffset + ic, v0 * fcr - v1 * fci);
                    state.q.setFloat(poffset + ic + halfHead, v0 * fci + v1 * fcr);
                }
            }

            // KV projection (shared KV: later layers reuse earlier layer's cache)
            int kvLayer = config.kvSourceLayer(l);
            int nKvHeads = config.numberOfKeyValueHeads(l);
            int kvMul = config.numberOfHeads / nKvHeads;
            if (config.hasKv(l)) {
                weights.wk[l].matmul(state.xb, state.k, kvDim, dim);
                // V = wv @ xb if V weight exists, otherwise V = K
                if (weights.wv[l] != null) {
                    weights.wv[l].matmul(state.xb, state.v, kvDim, dim);
                } else {
                    state.k.copyTo(0, state.v, 0, kvDim);
                }

                // Per-head K norm (learned weights) and V norm (bare RMS)
                for (int h = 0; h < nKvHeads; h++) {
                    rmsnorm(state.k, h * headSize, state.k, h * headSize, weights.attn_k_norm[l], headSize, config.rmsNormEps);
                    rmsnormNoWeight(state.v, h * headSize, state.v, h * headSize, headSize, config.rmsNormEps);
                }

                // RoPE for K
                for (int h = 0; h < nKvHeads; ++h) {
                    int poffset = h * headSize;
                    for (int i0 = 0; i0 < headSize; i0 += 2) {
                        int ic = i0 / 2;
                        float fcr = freqsReal.get(position * halfHead + ic);
                        float fci = freqsImag.get(position * halfHead + ic);
                        float v0 = state.k.getFloat(poffset + ic);
                        float v1 = state.k.getFloat(poffset + ic + halfHead);
                        state.k.setFloat(poffset + ic, v0 * fcr - v1 * fci);
                        state.k.setFloat(poffset + ic + halfHead, v0 * fci + v1 * fcr);
                    }
                }

                state.k.copyTo(0, state.keyCache[kvLayer], position * kvDim, kvDim);
                state.v.copyTo(0, state.valueCache[kvLayer], position * kvDim, kvDim);
            }

            // Attention (scale=1.0, no 1/sqrt(headSize))
            int attStart = layerIsSWA ? Math.max(0, position - config.slidingWindow + 1) : 0;
            int finalKvLayer = kvLayer;
            int finalKvDim = kvDim;
            int finalAttStart = attStart;

            Parallel.parallelFor(0, config.numberOfHeads, h -> {
                int qOffset = h * headSize;
                int attOffset = h * config.contextLength;
                for (int t = finalAttStart; t <= position; t++) {
                    int keyCacheOffset = t * finalKvDim + (h / kvMul) * headSize;
                    float score = state.q.dot(qOffset, state.keyCache[finalKvLayer], keyCacheOffset, headSize);
                    state.att.setFloat(attOffset + t, score);
                }

                state.att.softmaxInPlace(attOffset + finalAttStart, position - finalAttStart + 1);
                int xbOffset = h * headSize;
                state.xb_k.fillInPlace(xbOffset, headSize, 0f);
                for (int t = finalAttStart; t <= position; t++) {
                    int vOffset = t * finalKvDim + (h / kvMul) * headSize;
                    float a = state.att.getFloat(attOffset + t);
                    state.xb_k.saxpyInPlace(xbOffset, state.valueCache[finalKvLayer], vOffset, headSize, a);
                }
            });

            // Output projection + post-attention norm + residual
            weights.wo[l].matmul(state.xb_k, state.xb2, dim, queryDim);
            rmsnorm(state.xb2, state.xb2, weights.post_attention_norm[l], dim, config.rmsNormEps);
            state.x.addInPlace(state.xb2);

            // FFN
            boolean isMoELayer = config.isMoE() && weights.ffnGateInp[l] != null;
            if (isMoELayer) {
                // === MoE FFN: shared MLP + expert MoE ===

                // Shared MLP path: ffn_norm -> gate/up/down -> post_norm_1
                rmsnorm(state.xb, state.x, weights.rms_ffn_weight[l], dim, config.rmsNormEps);
                weights.w1[l].matmul(state.xb, state.hb, hiddenDim, dim);
                weights.w3[l].matmul(state.xb, state.hb2, hiddenDim, dim);
                state.hb.mapInPlace(0, hiddenDim, Llama::gelu);
                state.hb.multiplyInPlace(0, state.hb2, 0, hiddenDim);
                weights.w2[l].matmul(state.hb, state.xb, dim, hiddenDim);
                rmsnorm(state.xb, state.xb, weights.ffnPostNorm1[l], dim, config.rmsNormEps);
                // state.xb now holds shared MLP output

                // Expert MoE path: pre_norm_2 -> routing -> expert FFN -> post_norm_2
                rmsnorm(state.moeInput, state.x, weights.preFfwNorm2[l], dim, config.rmsNormEps);

                // Router: rms_norm(x) -> scale(1/sqrt(dim)) -> elementwise_mul(gate_inp_s) -> matmul(gate_inp) -> softmax -> top-k
                {
                    float ss = state.x.reduce(0, dim, 0f, (acc, xi) -> acc + xi * xi);
                    ss /= dim;
                    ss += config.rmsNormEps;
                    float rmsScale = (float) (1.0 / Math.sqrt(ss)) / (float) Math.sqrt(dim);
                    for (int i = 0; i < dim; i++) {
                        state.xb2.setFloat(i, state.x.getFloat(i) * rmsScale * weights.ffnGateInpScale[l].get(i));
                    }
                }
                weights.ffnGateInp[l].matmul(state.xb2, state.routerLogits, config.expertCount, dim);

                // Softmax over router logits
                state.routerLogits.softmaxInPlace(0, config.expertCount);

                // Find top-k experts
                int nExperts = config.expertCount;
                int topK = config.expertUsedCount;
                int[] topExperts = new int[topK];
                float[] topProbs = new float[topK];
                for (int ki = 0; ki < topK; ki++) {
                    int bestIdx = 0;
                    float bestVal = Float.NEGATIVE_INFINITY;
                    for (int ei = 0; ei < nExperts; ei++) {
                        float val = state.routerLogits.getFloat(ei);
                        if (val > bestVal) {
                            bestVal = val;
                            bestIdx = ei;
                        }
                    }
                    topExperts[ki] = bestIdx;
                    topProbs[ki] = bestVal;
                    state.routerLogits.setFloat(bestIdx, Float.NEGATIVE_INFINITY); // mask for next iteration
                }

                // Run selected experts and accumulate
                int expertFF = config.expertFeedForwardLength;
                int gateUpDim = 2 * expertFF;
                state.moeOutput.fillInPlace(0, dim, 0f);

                for (int ki = 0; ki < topK; ki++) {
                    int expertIdx = topExperts[ki];
                    float prob = topProbs[ki];
                    float downScale = weights.ffnDownExpsScale[l].get(expertIdx);

                    // gate_up = ffn_gate_up_exps[expert] @ moeInput -> (2*expertFF,)
                    int gateUpOffset = expertIdx * gateUpDim * dim;
                    weights.ffnGateUpExps[l].matmul(state.moeInput, state.expertGateUp, gateUpDim, dim, gateUpOffset);

                    // gate = gelu(gate_up[0:expertFF]), up = gate_up[expertFF:2*expertFF]
                    state.expertGateUp.mapInPlace(0, expertFF, Llama::gelu);
                    for (int i = 0; i < expertFF; i++) {
                        state.expertGateUp.setFloat(i, state.expertGateUp.getFloat(i) * state.expertGateUp.getFloat(expertFF + i));
                    }

                    // down = ffn_down_exps[expert] @ (gate * up) -> (dim,)
                    int downOffset = expertIdx * dim * expertFF;
                    weights.ffnDownExps[l].matmul(state.expertGateUp, state.expertDown, dim, expertFF, downOffset);

                    // Accumulate: moeOutput += prob * downScale * expertDown
                    float finalWeight = prob * downScale;
                    state.moeOutput.saxpyInPlace(0, state.expertDown, 0, dim, finalWeight);
                }

                // Post-norm for MoE output
                rmsnorm(state.moeOutput, state.moeOutput, weights.ffnPostNorm2[l], dim, config.rmsNormEps);

                // Combine shared MLP + MoE: xb += moeOutput
                state.xb.addInPlace(0, state.moeOutput, 0, dim);

                // Overall post-FFW norm + residual
                rmsnorm(state.xb, state.xb, weights.post_ffw_norm[l], dim, config.rmsNormEps);
                state.x.addInPlace(state.xb);
            } else {
                // Standard dense FFN: w2(GELU(w1(x)) * w3(x))
                rmsnorm(state.xb, state.x, weights.rms_ffn_weight[l], dim, config.rmsNormEps);
                weights.w1[l].matmul(state.xb, state.hb, hiddenDim, dim);
                weights.w3[l].matmul(state.xb, state.hb2, hiddenDim, dim);
                state.hb.mapInPlace(0, hiddenDim, Llama::gelu);
                state.hb.multiplyInPlace(0, state.hb2, 0, hiddenDim);
                weights.w2[l].matmul(state.hb, state.xb, dim, hiddenDim);
                rmsnorm(state.xb, state.xb, weights.post_ffw_norm[l], dim, config.rmsNormEps);
                state.x.addInPlace(state.xb);
            }

            // Per-layer embedding: GELU-gated projection
            if (plDim > 0 && weights.perLayerInpGate != null) {
                weights.perLayerInpGate[l].matmul(state.x, state.plGate, plDim, dim);
                state.plGate.mapInPlace(0, plDim, Llama::gelu);
                int plOffset = l * plDim;
                for (int i = 0; i < plDim; i++) {
                    state.plGate.setFloat(i, state.plGate.getFloat(i) * state.perLayerInputs.getFloat(plOffset + i));
                }
                weights.perLayerProj[l].matmul(state.plGate, state.plProj, dim, plDim);
                rmsnorm(state.plProj, state.plProj, weights.perLayerPostNorm[l], dim, config.rmsNormEps);
                state.x.addInPlace(state.plProj);
            }

            // Layer output scale
            float scale = weights.layerOutputScale[l];
            if (scale != 1.0f) {
                state.x.mapInPlace(0, dim, v -> v * scale);
            }
        }

        // Final norm + logits
        rmsnorm(state.x, state.x, weights.rms_final_weight, dim, config.rmsNormEps);
        weights.wcls.matmul(state.x, state.logits, config.vocabularySize, dim);
        if (config.logitSoftcapping > 0) {
            float cap = config.logitSoftcapping;
            state.logits.mapInPlace(v -> cap * (float) Math.tanh(v / cap));
        }
        return state.logits;
    }

    private static final String ANSI_CYAN = "\033[36m";
    private static final String ANSI_RESET = "\033[0m";

    public static List<Integer> generateTokens(Llama model, Llama.State state, int startPosition, List<Integer> promptTokens, Set<Integer> stopTokens, int maxTokens, Sampler sampler, boolean echo,
                                               boolean color, IntConsumer onTokenGenerated) {
        long startNanos = System.nanoTime();
        long startGen = 0;
        if (maxTokens < 0 || model.configuration().contextLength < maxTokens) {
            maxTokens = model.configuration().contextLength;
        }
        List<Integer> generatedTokens = new ArrayList<>(maxTokens);
        int token = state.latestToken; // current token (initialized to BOS)
        int nextToken;
        int promptIndex = 0;
        for (int position = startPosition; position < maxTokens; ++position) {
            forward(model, state, token, position);
            if (promptIndex < promptTokens.size()) {
                nextToken = promptTokens.get(promptIndex++);
                if (echo) {
                    System.err.print(GemmaTokenizer.replaceControlCharacters(model.tokenizer().decode(List.of(nextToken))));
                }
                if (promptIndex >= promptTokens.size()) {
                    startGen = System.nanoTime();
                }
            } else {
                nextToken = sampler.sampleToken(state.logits);
                if (echo) {
                    System.err.print(GemmaTokenizer.replaceControlCharacters(model.tokenizer().decode(List.of(nextToken))));
                }
                generatedTokens.add(nextToken);
                if (onTokenGenerated != null) {
                    onTokenGenerated.accept(nextToken);
                }
                if (stopTokens.contains(nextToken)) {
                    break;
                }
            }
            state.latestToken = token = nextToken;
        }

        long elapsedNanos = System.nanoTime() - startNanos;
        long promptNanos = startGen - startNanos;
        long genNanos = elapsedNanos - startGen + startNanos;
        String timingPrefix = color ? ANSI_CYAN : "";
        String timingSuffix = color ? ANSI_RESET : "";
        System.err.printf("%n%scontext: %d/%d prompt: %.2f tokens/s (%d) generation: %.2f tokens/s (%d)%s%n",
                timingPrefix,
                startPosition + promptIndex + generatedTokens.size(), model.configuration().contextLength,
                promptTokens.size() / (promptNanos / 1_000_000_000.0), promptTokens.size(),
                generatedTokens.size() / (genNanos / 1_000_000_000.0), generatedTokens.size(),
                timingSuffix);

        return generatedTokens;
    }
}

class GemmaTokenizer {
    private final Vocabulary vocabulary;
    private final Map<String, Integer> specialTokens;
    private final int[] tokenType;
    private final int byte0;

    public Map<String, Integer> getSpecialTokens() {
        return specialTokens;
    }

    public boolean isSpecialToken(int tokenIndex) {
        return tokenType[tokenIndex] != 1;
    }

    public GemmaTokenizer(Vocabulary vocabulary, int[] tokenType) {
        this.vocabulary = vocabulary;
        this.tokenType = tokenType.clone();
        int endOfTurn = vocabulary.getIndex("<turn|>").orElseThrow();
        for (int i = 0; i <= endOfTurn; ++i) {
            if (this.tokenType[i] == 1) {
                this.tokenType[i] = 6;
            }
        }
        this.byte0 = vocabulary.getIndex("<0x00>").orElseThrow();
        this.specialTokens = buildSpecialTokens(this.tokenType)
                .stream()
                .collect(Collectors.toMap(t -> vocabulary.get(t), t -> t));
    }

    private static List<Integer> buildSpecialTokens(int[] tokenType) {
        return IntStream.range(0, tokenType.length)
                .filter(t -> tokenType[t] != 1)
                .boxed()
                .toList();
    }

    List<Integer> encode(String text) {
        return encodeImpl(text.replace(' ', '\u2581'));
    }

    private List<Integer> encodeImpl(String text) {
        List<Integer> tokens = new ArrayList<>();

        for (int i = 0, cpi; i < text.length(); i += Character.charCount(cpi)) {
            cpi = text.codePointAt(i);

            String singleCodepoint = Character.toString(cpi);
            int id = vocabulary.getIndex(singleCodepoint).orElse(-1);

            if (id != -1) {
                tokens.add(id);
            } else {
                for (byte b : singleCodepoint.getBytes(StandardCharsets.UTF_8)) {
                    tokens.add(Byte.toUnsignedInt(b) + byte0);
                }
            }
        }

        while (true) {
            float best_score = -1e10f;
            int best_id = -1;
            int best_idx = -1;

            for (int i = 0; i < tokens.size() - 1; ++i) {
                String str_buffer = vocabulary.get(tokens.get(i)) + vocabulary.get(tokens.get(i + 1));
                int id = vocabulary.getIndex(str_buffer).orElse(-1);
                if (id != -1 && vocabulary.getScore(id) > best_score) {
                    best_score = vocabulary.getScore(id);
                    best_id = id;
                    best_idx = i;
                }
            }

            if (best_idx == -1) {
                break;
            }

            tokens.set(best_idx, best_id);
            tokens.remove(best_idx + 1);
        }

        return tokens;
    }

    public String decode(List<Integer> tokens) {
        StringBuilder sb = new StringBuilder();
        for (int token : tokens) {
            String tokenString = vocabulary.get(token);
            if (isSpecialToken(token)) {
                String prefix = "<0x";
                String suffix = ">";
                if (tokenString.length() == 6 && tokenString.startsWith(prefix) && tokenString.endsWith(suffix)) {
                    String code = tokenString.substring(prefix.length(), tokenString.length() - suffix.length());
                    int cp = Integer.parseInt(code, 16);
                    tokenString = Character.toString(cp);
                }
            } else {
                tokenString = tokenString.replace('\u2581', ' ');
            }
            sb.append(tokenString);
        }
        return sb.toString();
    }

    public static String replaceControlCharacters(int[] codePoints) {
        StringBuilder chars = new StringBuilder();
        for (int cp : codePoints) {
            if (Character.getType(cp) == Character.CONTROL && cp != '\n') {
                chars.append("\\u").append(HexFormat.of().toHexDigits(cp, 4));
            } else {
                chars.appendCodePoint(cp);
            }
        }
        return chars.toString();
    }

    public static String replaceControlCharacters(String str) {
        return replaceControlCharacters(str.codePoints().toArray());
    }

}

final class Parallel {
    public static void parallelFor(int startInclusive, int endExclusive, IntConsumer action) {
        IntStream.range(startInclusive, endExclusive).parallel().forEach(action);
    }
}

record Pair<First, Second>(First first, Second second) {
}

record GGMLTensorEntry(MemorySegment mappedFile, String name, GGMLType ggmlType, int[] shape,
                       MemorySegment memorySegment) {
}

final class Float16 {
    public static final int BYTES = 2;
}

enum GGMLType {
    F32(Float.BYTES),          // 0
    F16(Float16.BYTES),        // 1
    Q4_0(Float16.BYTES + 16 * Byte.BYTES, 32),  // 2
    Q4_1(2 * Float16.BYTES + 16 * Byte.BYTES, 32), // 3
    UNSUPPORTED_Q4_2(Integer.MAX_VALUE), // 4 - removed
    UNSUPPORTED_Q4_3(Integer.MAX_VALUE), // 5 - removed
    Q5_0(Integer.MAX_VALUE),   // 6
    Q5_1(2 * Float16.BYTES + Integer.BYTES + 16 * Byte.BYTES, 32),   // 7
    Q8_0(Float16.BYTES + 32 * Byte.BYTES, 32),  // 8
    Q8_1(32 * Byte.BYTES + 2 * Float.BYTES, 32), // 9
    Q2_K(Integer.MAX_VALUE),   // 10
    Q3_K(Integer.MAX_VALUE),   // 11
    Q4_K(2 * Float16.BYTES + ((GGMLType.QK_K / 16) / 8 * 6) + GGMLType.QK_K / 2, GGMLType.QK_K), // 12
    Q5_K(2 * Float16.BYTES + ((GGMLType.QK_K / 16) / 8 * 6) + GGMLType.QK_K / 8 + GGMLType.QK_K / 2, GGMLType.QK_K), // 13
    Q6_K(GGMLType.QK_K / 2 + GGMLType.QK_K / 4 + GGMLType.QK_K / 16 + Float16.BYTES, GGMLType.QK_K), // 14
    Q8_K(Integer.MAX_VALUE),   // 15
    IQ2_XXS(Integer.MAX_VALUE), // 16
    IQ2_XS(Integer.MAX_VALUE),  // 17
    IQ3_XXS(Integer.MAX_VALUE), // 18
    IQ1_S(Integer.MAX_VALUE),   // 19
    IQ4_NL(Integer.MAX_VALUE),  // 20
    IQ3_S(Integer.MAX_VALUE),   // 21
    IQ2_S(Integer.MAX_VALUE),   // 22
    IQ4_XS(Integer.MAX_VALUE),  // 23
    I8(Byte.BYTES),             // 24
    I16(Short.BYTES),           // 25
    I32(Integer.BYTES),         // 26
    I64(Long.BYTES),            // 27
    F64(Double.BYTES),          // 28
    IQ1_M(Integer.MAX_VALUE),   // 29
    BF16(Float16.BYTES),        // 30
    UNSUPPORTED_Q4_0_4_4(Integer.MAX_VALUE), // 31 - removed from gguf files
    UNSUPPORTED_Q4_0_4_8(Integer.MAX_VALUE), // 32
    UNSUPPORTED_Q4_0_8_8(Integer.MAX_VALUE), // 33
    TQ1_0(Integer.MAX_VALUE),   // 34
    TQ2_0(Integer.MAX_VALUE),   // 35
    UNSUPPORTED_IQ4_NL_4_4(Integer.MAX_VALUE), // 36
    UNSUPPORTED_IQ4_NL_4_8(Integer.MAX_VALUE), // 37
    UNSUPPORTED_IQ4_NL_8_8(Integer.MAX_VALUE), // 38
    MXFP4(Byte.BYTES + GGMLType.QK_MXFP4 / 2, GGMLType.QK_MXFP4), // 39
    NVFP4(Integer.MAX_VALUE);   // 40

    private static final GGMLType[] VALUES = values();

    private final int typeSize;

    private final int blockSize;

    public int getTypeSize() {
        return typeSize;
    }

    public int getBlockSize() {
        return blockSize;
    }

    public static GGMLType fromId(int id) {
        if (0 <= id && id < VALUES.length) {
            return VALUES[id];
        }
        throw new UnsupportedOperationException("Unsupported GGML tensor type id: " + id);
    }

    GGMLType(int typeSize) {
        this(typeSize, 1);
    }

    public long byteSizeFor(long numberOfElements) {
        long t = numberOfElements * (long) getTypeSize();
        assert t % getBlockSize() == 0;
        return t / getBlockSize();
    }

    public static final int QK_K = 256;
    public static final int QK_MXFP4 = 32;

    GGMLType(int typeSize, int blockSize) {
        assert blockSize > 0;
        assert typeSize > 0;
        assert isPowerOf2(blockSize);
        this.typeSize = typeSize;
        this.blockSize = blockSize;
    }

    private static boolean isPowerOf2(int n) {
        return n > 0 && (n & (n - 1)) == 0;
    }
}

abstract class FloatTensor {
    static final int VECTOR_BIT_SIZE = Integer.getInteger("llama.VectorBitSize", VectorShape.preferredShape().vectorBitSize());
    static final boolean USE_VECTOR_API = VECTOR_BIT_SIZE != 0;

    static final VectorSpecies<Float> F_SPECIES;
    static final VectorSpecies<Integer> I_SPECIES;
    static final VectorSpecies<Short> S_SPECIES_HALF;

    static {
        if (USE_VECTOR_API) {
            F_SPECIES = VectorShape.forBitSize(VECTOR_BIT_SIZE).withLanes(float.class);
            I_SPECIES = F_SPECIES.withLanes(int.class);
            S_SPECIES_HALF = VectorShape.forBitSize(F_SPECIES.vectorBitSize() / 2).withLanes(short.class);
            assert F_SPECIES.length() == S_SPECIES_HALF.length();
        } else {
            F_SPECIES = null;
            I_SPECIES = null;
            S_SPECIES_HALF = null;
        }
    }

    static final sun.misc.Unsafe UNSAFE;

    static {
        try {
            Field f = sun.misc.Unsafe.class.getDeclaredField("theUnsafe");
            f.setAccessible(true);
            UNSAFE = (sun.misc.Unsafe) f.get(null);
        } catch (NoSuchFieldException | IllegalAccessException e) {
            throw new RuntimeException(e);
        }
    }

    static short readShort(MemorySegment memorySegment, long offset) {
        return UNSAFE.getShort(memorySegment.address() + offset);
    }

    static float readFloat16(MemorySegment memorySegment, long offset) {
        return Float.float16ToFloat(readShort(memorySegment, offset));
    }

    static byte readByte(MemorySegment memorySegment, long offset) {
        return UNSAFE.getByte(memorySegment.address() + offset);
    }

    static float readFloat(MemorySegment memorySegment, long offset) {
        return UNSAFE.getFloat(memorySegment.address() + offset);
    }

    abstract long size();

    abstract float getFloat(long index);

    abstract void setFloat(int index, float value);

    abstract FloatVector getFloatVector(VectorSpecies<Float> species, int offset);

    abstract GGMLType type();

    public static int numberOfElements(int... dimensions) {
        assert Arrays.stream(dimensions).allMatch(i -> i > 0);
        return Arrays.stream(dimensions).reduce(Math::multiplyExact).orElseThrow();
    }

    public static long numberOfElementsLong(int... dimensions) {
        long result = 1;
        for (int d : dimensions) {
            assert d > 0;
            result = Math.multiplyExact(result, d);
        }
        return result;
    }

    static float scalarDot(FloatTensor thiz, int thisOffset, FloatTensor that, int thatOffset, int size) {
        float result = 0f;
        for (int j = 0; j < size; j++) {
            result += thiz.getFloat(thisOffset + j) * that.getFloat(thatOffset + j);
        }
        return result;
    }

    float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        return scalarDot(this, thisOffset, that, thatOffset, size);
    }

    void matmul(FloatTensor that, FloatTensor out, int dim0, int dim1) {
        Parallel.parallelFor(0, dim0, i -> out.setFloat(i, dot(i * dim1, that, 0, dim1)));
    }

    // matmul with offset into this tensor (for expert weight slicing in 3D tensors)
    void matmul(FloatTensor that, FloatTensor out, int dim0, int dim1, int thisOffset) {
        Parallel.parallelFor(0, dim0, i -> out.setFloat(i, dot(thisOffset + i * dim1, that, 0, dim1)));
    }

    @FunctionalInterface
    interface AggregateFunction {
        float apply(float acc, float value);
    }

    float reduce(int thisOffset, int size, float seed, AggregateFunction reduce) {
        float result = seed;
        for (int i = 0; i < size; ++i) {
            result = reduce.apply(result, getFloat(thisOffset + i));
        }
        return result;
    }

    float sum(int thisOffset, int size) {
        return reduce(thisOffset, size, 0f, Float::sum);
    }

    float max(int thisOffset, int size) {
        return reduce(thisOffset, size, Float.NEGATIVE_INFINITY, Float::max);
    }

    void copyTo(int thisOffset, FloatTensor that, int thatOffset, int size) {
        that.mapWithIndexInPlace(thatOffset, size, (value, index) -> this.getFloat(index - thatOffset + thisOffset));
    }

    int argmax(int thisOffset, int size) {
        assert size > 0;
        int maxIndex = thisOffset;
        float maxValue = this.getFloat(maxIndex);
        int endIndex = thisOffset + size;
        for (int i = thisOffset; i < endIndex; ++i) {
            float f = this.getFloat(i);
            if (f > maxValue) {
                maxValue = f;
                maxIndex = i;
            }
        }
        return maxIndex;
    }

    int argmax() {
        return argmax(0, Math.toIntExact(size()));
    }

    @FunctionalInterface
    interface MapFunction {
        float apply(float value);
    }

    @FunctionalInterface
    interface MapWithIndexFunction {
        float apply(float value, int index);
    }

    FloatTensor mapInPlace(int thisOffset, int size, MapFunction mapFunction) {
        int endIndex = thisOffset + size;
        for (int i = thisOffset; i < endIndex; ++i) {
            setFloat(i, mapFunction.apply(getFloat(i)));
        }
        return this;
    }

    FloatTensor mapInPlace(MapFunction mapFunction) {
        return mapInPlace(0, Math.toIntExact(size()), mapFunction);
    }

    FloatTensor mapWithIndexInPlace(int thisOffset, int size, FloatTensor.MapWithIndexFunction mapWithIndexFunction) {
        int endOffset = thisOffset + size;
        for (int i = thisOffset; i < endOffset; ++i) {
            setFloat(i, mapWithIndexFunction.apply(getFloat(i), i));
        }
        return this;
    }

    FloatTensor addInPlace(int thisOffset, FloatTensor that, int thatOffset, int size) {
        return mapWithIndexInPlace(thisOffset, size, (value, index) -> value + that.getFloat(index - thisOffset + thatOffset));
    }

    FloatTensor addInPlace(FloatTensor that) {
        return addInPlace(0, that, 0, Math.toIntExact(size()));
    }

    FloatTensor multiplyInPlace(int thisOffset, FloatTensor that, int thatOffset, int size) {
        return mapWithIndexInPlace(thisOffset, size, (value, index) -> value * that.getFloat(index - thisOffset + thatOffset));
    }

    FloatTensor divideInPlace(int thisOffset, int size, float value) {
        return mapInPlace(thisOffset, size, f -> f / value);
    }

    FloatTensor fillInPlace(int thisOffset, int size, float value) {
        return mapInPlace(thisOffset, size, unused -> value);
    }

    FloatTensor softmaxInPlace(int thisOffset, int size) {
        float maxVal = max(thisOffset, size);
        mapInPlace(thisOffset, size, f -> (float) Math.exp(f - maxVal));
        float sum = sum(thisOffset, size);
        return divideInPlace(thisOffset, size, sum);
    }

    FloatTensor saxpyInPlace(int thisOffset, FloatTensor that, int thatOffset, int size, float a) {
        for (int i = 0; i < size; ++i) {
            setFloat(thisOffset + i, a * that.getFloat(thatOffset + i) + this.getFloat(thisOffset + i));
        }
        return this;
    }
}

final class Q4_0FloatTensor extends FloatTensor {

    final long size;
    final MemorySegment memorySegment;

    public Q4_0FloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override
    long size() {
        return size;
    }

    @Override
    public void setFloat(int index, float value) {
        throw new UnsupportedOperationException("setFloat");
    }

    @Override
    FloatVector getFloatVector(VectorSpecies<Float> species, int index) {
        throw new UnsupportedOperationException("getFloatVector");
    }

    @Override
    public GGMLType type() {
        return GGMLType.Q4_0;
    }

    @Override
    public float getFloat(long index) {
        assert 0 <= index && index < size;
        long blockIndex = index / GGMLType.Q4_0.getBlockSize();
        long blockOffset = blockIndex * GGMLType.Q4_0.getTypeSize();
        float scale = readFloat16(memorySegment, blockOffset);
        byte quant;
        int modIndex = (int) (index % GGMLType.Q4_0.getBlockSize());
        if (modIndex < GGMLType.Q4_0.getBlockSize() / 2) {
            quant = (byte) (readByte(memorySegment, blockOffset + Float16.BYTES + modIndex) & 0x0F);
        } else {
            quant = (byte) ((readByte(memorySegment, blockOffset + Float16.BYTES + modIndex - GGMLType.Q4_0.getBlockSize() / 2) >>> 4) & 0x0F);
        }
        quant -= 8;
        return quant * scale;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float vectorDot(Q4_0FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        int j = 0;

        assert Integer.bitCount(GGMLType.Q4_0.getBlockSize()) == 1 : "power of 2";
        int alignmentBound = Math.min(size, -thisOffset & (GGMLType.Q4_0.getBlockSize() - 1));
        if (alignmentBound > 0) {
            result += FloatTensor.scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j += alignmentBound;
        }
        assert (thisOffset + j) % GGMLType.Q4_0.getBlockSize() == 0;

        FloatVector val = FloatVector.zero(F_SPECIES);
        long blockOffset = (long) (thisOffset + j) / GGMLType.Q4_0.getBlockSize() * GGMLType.Q4_0.getTypeSize();
        int upperBound = j + (size - j) / GGMLType.Q4_0.getBlockSize() * GGMLType.Q4_0.getBlockSize();
        for (; j < upperBound; j += GGMLType.Q4_0.getBlockSize(), blockOffset += GGMLType.Q4_0.getTypeSize()) {
                    float wScaleValue = readFloat16(thiz.memorySegment, blockOffset);
            var wScale = FloatVector.broadcast(F_SPECIES, wScaleValue);
            var wBytes = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment, blockOffset + Float16.BYTES, ByteOrder.LITTLE_ENDIAN);
            var loBytes = wBytes.and((byte) 0xF).sub((byte) 8);
            var hiBytes = wBytes.lanewise(VectorOperators.LSHR, 4).sub((byte) 8);
            switch (F_SPECIES.vectorBitSize()) {
                case 512 -> {
                    var s0 = that.getFloatVector(F_SPECIES, thatOffset + j).mul(loBytes.castShape(F_SPECIES, 0));
                    var s1 = that.getFloatVector(F_SPECIES, thatOffset + j + F_SPECIES.length()).mul(hiBytes.castShape(F_SPECIES, 0));
                    val = s0.add(s1).fma(wScale, val);
                }
                case 256 -> {
                    var s0 = that.getFloatVector(F_SPECIES, thatOffset + j).mul(loBytes.castShape(F_SPECIES, 0));
                    var s1 = that.getFloatVector(F_SPECIES, thatOffset + j + 2 * F_SPECIES.length()).mul(hiBytes.castShape(F_SPECIES, 0));
                    s0 = that.getFloatVector(F_SPECIES, thatOffset + j + F_SPECIES.length()).fma(loBytes.castShape(F_SPECIES, 1), s0);
                    s1 = that.getFloatVector(F_SPECIES, thatOffset + j + 3 * F_SPECIES.length()).fma(hiBytes.castShape(F_SPECIES, 1), s1);
                    val = s0.add(s1).fma(wScale, val);
                }
                case 128 -> {
                    for (int i = 0; i < 2; ++i) {
                        var tmp = i == 0 ? loBytes : hiBytes;
                        var s0 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4) * F_SPECIES.length()).mul(tmp.castShape(F_SPECIES, 0));
                        var s1 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4 + 2) * F_SPECIES.length()).mul(tmp.castShape(F_SPECIES, 2));
                        s0 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4 + 1) * F_SPECIES.length()).fma(tmp.castShape(F_SPECIES, 1), s0);
                        s1 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4 + 3) * F_SPECIES.length()).fma(tmp.castShape(F_SPECIES, 3), s1);
                        val = s0.add(s1).fma(wScale, val);
                    }
                }
                default -> throw new UnsupportedOperationException(F_SPECIES.toString());
            }
        }
        result += val.reduceLanes(VectorOperators.ADD);

        if (j < size) {
            result += FloatTensor.scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }

        return result;
    }
}

final class Q4_1FloatTensor extends FloatTensor {

    final long size;
    final MemorySegment memorySegment;

    public Q4_1FloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.Q4_1; }

    @Override
    public float getFloat(long index) {
        assert 0 <= index && index < size;
        long blockIndex = index / GGMLType.Q4_1.getBlockSize();
        long blockOffset = blockIndex * GGMLType.Q4_1.getTypeSize();
        float delta = readFloat16(memorySegment, blockOffset);
        float min = readFloat16(memorySegment, blockOffset + Float16.BYTES);
        int modIndex = (int) (index % GGMLType.Q4_1.getBlockSize());
        int quant;
        if (modIndex < 16) {
            quant = Byte.toUnsignedInt(readByte(memorySegment, blockOffset + 2 * Float16.BYTES + modIndex)) & 0x0F;
        } else {
            quant = (Byte.toUnsignedInt(readByte(memorySegment, blockOffset + 2 * Float16.BYTES + modIndex - 16)) >>> 4) & 0x0F;
        }
        return delta * quant + min;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float vectorDot(Q4_1FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        int j = 0;

        assert Integer.bitCount(GGMLType.Q4_1.getBlockSize()) == 1 : "power of 2";
        int alignmentBound = Math.min(size, -thisOffset & (GGMLType.Q4_1.getBlockSize() - 1));
        if (alignmentBound > 0) {
            result += FloatTensor.scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j += alignmentBound;
        }
        assert (thisOffset + j) % GGMLType.Q4_1.getBlockSize() == 0;

        FloatVector val = FloatVector.zero(F_SPECIES);
        long blockOffset = (long) (thisOffset + j) / GGMLType.Q4_1.getBlockSize() * GGMLType.Q4_1.getTypeSize();
        int upperBound = j + (size - j) / GGMLType.Q4_1.getBlockSize() * GGMLType.Q4_1.getBlockSize();
        for (; j < upperBound; j += GGMLType.Q4_1.getBlockSize(), blockOffset += GGMLType.Q4_1.getTypeSize()) {
            float deltaValue = readFloat16(thiz.memorySegment, blockOffset);
            float minValue = readFloat16(thiz.memorySegment, blockOffset + Float16.BYTES);
            var wDelta = FloatVector.broadcast(F_SPECIES, deltaValue);
            var wMin = FloatVector.broadcast(F_SPECIES, minValue);
            var wBytes = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment, blockOffset + 2 * Float16.BYTES, ByteOrder.LITTLE_ENDIAN);
            var loBytes = wBytes.and((byte) 0xF);
            var hiBytes = wBytes.lanewise(VectorOperators.LSHR, 4);
            switch (F_SPECIES.vectorBitSize()) {
                case 512 -> {
                    var that0 = that.getFloatVector(F_SPECIES, thatOffset + j);
                    var that1 = that.getFloatVector(F_SPECIES, thatOffset + j + F_SPECIES.length());
                    var s0 = that0.mul(loBytes.castShape(F_SPECIES, 0));
                    var s1 = that1.mul(hiBytes.castShape(F_SPECIES, 0));
                    val = s0.add(s1).fma(wDelta, val);
                    val = that0.add(that1).fma(wMin, val);
                }
                case 256 -> {
                    var that0 = that.getFloatVector(F_SPECIES, thatOffset + j);
                    var that1 = that.getFloatVector(F_SPECIES, thatOffset + j + F_SPECIES.length());
                    var that2 = that.getFloatVector(F_SPECIES, thatOffset + j + 2 * F_SPECIES.length());
                    var that3 = that.getFloatVector(F_SPECIES, thatOffset + j + 3 * F_SPECIES.length());
                    var s0 = that0.mul(loBytes.castShape(F_SPECIES, 0));
                    var s1 = that2.mul(hiBytes.castShape(F_SPECIES, 0));
                    s0 = that1.fma(loBytes.castShape(F_SPECIES, 1), s0);
                    s1 = that3.fma(hiBytes.castShape(F_SPECIES, 1), s1);
                    val = s0.add(s1).fma(wDelta, val);
                    val = that0.add(that1).add(that2).add(that3).fma(wMin, val);
                }
                case 128 -> {
                    for (int i = 0; i < 2; ++i) {
                        var tmp = i == 0 ? loBytes : hiBytes;
                        var s0 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4) * F_SPECIES.length()).mul(tmp.castShape(F_SPECIES, 0));
                        var s1 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4 + 2) * F_SPECIES.length()).mul(tmp.castShape(F_SPECIES, 2));
                        s0 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4 + 1) * F_SPECIES.length()).fma(tmp.castShape(F_SPECIES, 1), s0);
                        s1 = that.getFloatVector(F_SPECIES, thatOffset + j + (i * 4 + 3) * F_SPECIES.length()).fma(tmp.castShape(F_SPECIES, 3), s1);
                        val = s0.add(s1).fma(wDelta, val);
                    }
                    // vectorized min contribution
                    var thatSum = FloatVector.zero(F_SPECIES);
                    for (int k = 0; k < GGMLType.Q4_1.getBlockSize(); k += F_SPECIES.length()) {
                        thatSum = thatSum.add(that.getFloatVector(F_SPECIES, thatOffset + j + k));
                    }
                    val = thatSum.fma(wMin, val);
                }
                default -> throw new UnsupportedOperationException(F_SPECIES.toString());
            }
        }
        result += val.reduceLanes(VectorOperators.ADD);

        if (j < size) {
            result += FloatTensor.scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }

        return result;
    }
}

final class Q5_1FloatTensor extends FloatTensor {

    private final long size;
    private final MemorySegment memorySegment;

    Q5_1FloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.Q5_1; }

    @Override
    public float getFloat(long index) {
        assert 0 <= index && index < size;
        long blockIndex = index / GGMLType.Q5_1.getBlockSize();
        int inBlockIndex = (int) (index % GGMLType.Q5_1.getBlockSize());
        long blockOffset = blockIndex * GGMLType.Q5_1.getTypeSize();

        float d = readFloat16(memorySegment, blockOffset);
        float m = readFloat16(memorySegment, blockOffset + Float16.BYTES);
        int qh = readInt32LE(memorySegment, blockOffset + 2L * Float16.BYTES);

        int j;
        int nibble;
        int xh;
        if (inBlockIndex < GGMLType.Q5_1.getBlockSize() / 2) {
            j = inBlockIndex;
            nibble = Byte.toUnsignedInt(readByte(memorySegment, blockOffset + 2L * Float16.BYTES + Integer.BYTES + j)) & 0x0F;
            xh = ((qh >> j) << 4) & 0x10;
        } else {
            j = inBlockIndex - GGMLType.Q5_1.getBlockSize() / 2;
            nibble = (Byte.toUnsignedInt(readByte(memorySegment, blockOffset + 2L * Float16.BYTES + Integer.BYTES + j)) >>> 4) & 0x0F;
            xh = (qh >> (j + 12)) & 0x10;
        }

        int q = nibble | xh;
        return q * d + m;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (that instanceof ArrayFloatTensor aft) {
            if (FloatTensor.USE_VECTOR_API) {
                return vectorDot(this, thisOffset, aft, thatOffset, size);
            }
            return scalarDot(this, thisOffset, aft, thatOffset, size);
        }
        return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
    }

    private static float vectorDot(Q5_1FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        assert Integer.bitCount(GGMLType.Q5_1.getBlockSize()) == 1 : "power of 2";
        int j = 0;
        float result = 0f;

        int alignmentBound = Math.min(size, -thisOffset & (GGMLType.Q5_1.getBlockSize() - 1));
        if (alignmentBound > 0) {
            result += scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j = alignmentBound;
        }

        float[] decoded = new float[GGMLType.Q5_1.getBlockSize()];
        int upperBound = j + (size - j) / GGMLType.Q5_1.getBlockSize() * GGMLType.Q5_1.getBlockSize();
        int vecUpper = F_SPECIES.loopBound(GGMLType.Q5_1.getBlockSize());
        for (; j < upperBound; j += GGMLType.Q5_1.getBlockSize()) {
            assert (thisOffset + j) % GGMLType.Q5_1.getBlockSize() == 0;
            long blockOffset = (long) (thisOffset + j) / GGMLType.Q5_1.getBlockSize() * GGMLType.Q5_1.getTypeSize();
            float d = readFloat16(thiz.memorySegment, blockOffset);
            float m = readFloat16(thiz.memorySegment, blockOffset + Float16.BYTES);
            int qh = readInt32LE(thiz.memorySegment, blockOffset + 2L * Float16.BYTES);
            long qsBase = blockOffset + 2L * Float16.BYTES + Integer.BYTES;

            for (int p = 0; p < GGMLType.Q5_1.getBlockSize() / 2; p++) {
                int packed = Byte.toUnsignedInt(readByte(thiz.memorySegment, qsBase + p));
                int x0 = (packed & 0x0F) | ((((qh >> p) << 4) & 0x10));
                int x1 = ((packed >>> 4) & 0x0F) | ((qh >> (p + 12)) & 0x10);
                decoded[p] = x0 * d + m;
                decoded[p + GGMLType.Q5_1.getBlockSize() / 2] = x1 * d + m;
            }

            FloatVector acc = FloatVector.zero(F_SPECIES);
            for (int i = 0; i < vecUpper; i += F_SPECIES.length()) {
                FloatVector w = FloatVector.fromArray(F_SPECIES, decoded, i);
                FloatVector x = that.getFloatVector(F_SPECIES, thatOffset + j + i);
                acc = w.fma(x, acc);
            }
            result += acc.reduceLanes(VectorOperators.ADD);

            for (int i = vecUpper; i < GGMLType.Q5_1.getBlockSize(); i++) {
                result += decoded[i] * that.values[thatOffset + j + i];
            }
        }

        if (j < size) {
            result += scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }
        return result;
    }

    private static float scalarDot(Q5_1FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        for (int i = 0; i < size; i++) {
            result += thiz.getFloat(thisOffset + i) * that.values[thatOffset + i];
        }
        return result;
    }

    private static int readInt32LE(MemorySegment memorySegment, long offset) {
        int b0 = Byte.toUnsignedInt(readByte(memorySegment, offset));
        int b1 = Byte.toUnsignedInt(readByte(memorySegment, offset + 1));
        int b2 = Byte.toUnsignedInt(readByte(memorySegment, offset + 2));
        int b3 = Byte.toUnsignedInt(readByte(memorySegment, offset + 3));
        return b0 | (b1 << 8) | (b2 << 16) | (b3 << 24);
    }
}

final class Q4_KFloatTensor extends FloatTensor {

    static final int BLOCK_SIZE = GGMLType.QK_K;
    static final int TYPE_SIZE = GGMLType.Q4_K.getTypeSize();

    final long size;
    final MemorySegment memorySegment;

    public Q4_KFloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.Q4_K; }

    // Decode scale or min for sub-block j (0..7) from the 12-byte scales array
    static int getScaleMinK4(int j, MemorySegment mem, long scalesOffset, boolean isMin) {
        if (j < 4) {
            int idx = isMin ? j + 4 : j;
            return Byte.toUnsignedInt(readByte(mem, scalesOffset + idx)) & 63;
        } else {
            int lowIdx = j + 4;
            int highIdx = isMin ? j : j - 4;
            int low = isMin
                    ? (Byte.toUnsignedInt(readByte(mem, scalesOffset + lowIdx)) >> 4)
                    : (Byte.toUnsignedInt(readByte(mem, scalesOffset + lowIdx)) & 0xF);
            int high = (Byte.toUnsignedInt(readByte(mem, scalesOffset + highIdx)) >> 6) & 0x3;
            return low | (high << 4);
        }
    }

    @Override
    public float getFloat(long index) {
        long blockIndex = index / BLOCK_SIZE;
        int withinBlock = (int) (index % BLOCK_SIZE);
        long blockOffset = blockIndex * TYPE_SIZE;
        float d = readFloat16(memorySegment, blockOffset);
        float dmin = readFloat16(memorySegment, blockOffset + 2);
        long scalesOffset = blockOffset + 4;
        long qsOffset = blockOffset + 16; // 4 + 12

        // Each group of 64 values uses 2 sub-blocks: low nibble (32) + high nibble (32)
        int group = withinBlock / 64;   // 0..3
        int inGroup = withinBlock % 64;
        int subBlock;
        int nibbleIndex;
        boolean isHigh;
        if (inGroup < 32) {
            subBlock = group * 2;
            nibbleIndex = inGroup;
            isHigh = false;
        } else {
            subBlock = group * 2 + 1;
            nibbleIndex = inGroup - 32;
            isHigh = true;
        }

        int sc = getScaleMinK4(subBlock, memorySegment, scalesOffset, false);
        int m = getScaleMinK4(subBlock, memorySegment, scalesOffset, true);

        byte qsByte = readByte(memorySegment, qsOffset + group * 32 + nibbleIndex);
        int quant = isHigh ? ((Byte.toUnsignedInt(qsByte) >> 4) & 0xF) : (Byte.toUnsignedInt(qsByte) & 0xF);

        return d * sc * quant - dmin * m;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float vectorDot(Q4_KFloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        int j = 0;

        // Handle unaligned head
        assert Integer.bitCount(BLOCK_SIZE) == 1 : "power of 2";
        int alignmentBound = Math.min(size, -thisOffset & (BLOCK_SIZE - 1));
        if (alignmentBound > 0) {
            result += FloatTensor.scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j += alignmentBound;
        }

        FloatVector val = FloatVector.zero(F_SPECIES);
        FloatVector val2 = FloatVector.zero(F_SPECIES);
        long blockOffset = (long) (thisOffset + j) / BLOCK_SIZE * TYPE_SIZE;
        int upperBound = j + (size - j) / BLOCK_SIZE * BLOCK_SIZE;

        for (; j < upperBound; j += BLOCK_SIZE, blockOffset += TYPE_SIZE) {
            float d = readFloat16(thiz.memorySegment, blockOffset);
            float dmin = readFloat16(thiz.memorySegment, blockOffset + 2);
            long scalesOff = blockOffset + 4;
            long qsOff = blockOffset + 16;

            // 4 groups of 64 values each (2 sub-blocks per group: low nibble + high nibble)
            for (int g = 0; g < 4; g++) {
                float d1 = d * getScaleMinK4(g * 2, thiz.memorySegment, scalesOff, false);
                float negM1 = -(dmin * getScaleMinK4(g * 2, thiz.memorySegment, scalesOff, true));
                float d2 = d * getScaleMinK4(g * 2 + 1, thiz.memorySegment, scalesOff, false);
                float negM2 = -(dmin * getScaleMinK4(g * 2 + 1, thiz.memorySegment, scalesOff, true));

                var d1Vec = FloatVector.broadcast(F_SPECIES, d1);
                var negM1Vec = FloatVector.broadcast(F_SPECIES, negM1);
                var d2Vec = FloatVector.broadcast(F_SPECIES, d2);
                var negM2Vec = FloatVector.broadcast(F_SPECIES, negM2);

                int loBase = thatOffset + j + g * 64;
                int hiBase = thatOffset + j + g * 64 + 32;

                // Process 32 bytes of qs in 2 chunks of 16 bytes
                for (int c = 0; c < 2; c++) {
                    var wBytes = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment,
                            qsOff + (long) g * 32 + c * 16, ByteOrder.LITTLE_ENDIAN);
                    var loBytes = wBytes.and((byte) 0xF);
                    var hiBytes = wBytes.lanewise(VectorOperators.LSHR, 4);

                    int loIdx = loBase + c * 16;
                    int hiIdx = hiBase + c * 16;

                    switch (F_SPECIES.vectorBitSize()) {
                        case 512 -> {
                            var loQ = loBytes.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            val = loQ.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loIdx), val);
                            var hiQ = hiBytes.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            val2 = hiQ.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiIdx), val2);
                        }
                        case 256 -> {
                            var loQ0 = loBytes.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var loQ1 = loBytes.castShape(F_SPECIES, 1).reinterpretAsFloats();
                            val = loQ0.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loIdx), val);
                            val2 = loQ1.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loIdx + F_SPECIES.length()), val2);
                            var hiQ0 = hiBytes.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var hiQ1 = hiBytes.castShape(F_SPECIES, 1).reinterpretAsFloats();
                            val = hiQ0.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiIdx), val);
                            val2 = hiQ1.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiIdx + F_SPECIES.length()), val2);
                        }
                        case 128 -> {
                            for (int p = 0; p < 4; p++) {
                                var loQ = loBytes.castShape(F_SPECIES, p).reinterpretAsFloats();
                                val = loQ.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loIdx + p * F_SPECIES.length()), val);
                                var hiQ = hiBytes.castShape(F_SPECIES, p).reinterpretAsFloats();
                                val2 = hiQ.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiIdx + p * F_SPECIES.length()), val2);
                            }
                        }
                        default -> throw new UnsupportedOperationException(F_SPECIES.toString());
                    }
                }
            }
        }
        result += val.add(val2).reduceLanes(VectorOperators.ADD);

        // Handle tail
        if (j < size) {
            result += FloatTensor.scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }

        return result;
    }
}

final class Q5_KFloatTensor extends FloatTensor {

    static final int BLOCK_SIZE = GGMLType.QK_K;
    static final int TYPE_SIZE = GGMLType.Q5_K.getTypeSize();

    final long size;
    final MemorySegment memorySegment;

    public Q5_KFloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.Q5_K; }

    @Override
    public float getFloat(long index) {
        long blockIndex = index / BLOCK_SIZE;
        int withinBlock = (int) (index % BLOCK_SIZE);
        long blockOffset = blockIndex * TYPE_SIZE;
        float d = readFloat16(memorySegment, blockOffset);
        float dmin = readFloat16(memorySegment, blockOffset + 2);
        long scalesOffset = blockOffset + 4;
        long qhOffset = blockOffset + 16;  // 4 + 12
        long qsOffset = blockOffset + 48;  // 4 + 12 + 32

        int group = withinBlock / 64;
        int inGroup = withinBlock % 64;
        boolean isHigh = inGroup >= 32;
        int l = isHigh ? inGroup - 32 : inGroup;
        int subBlock = isHigh ? group * 2 + 1 : group * 2;

        int sc = Q4_KFloatTensor.getScaleMinK4(subBlock, memorySegment, scalesOffset, false);
        int m = Q4_KFloatTensor.getScaleMinK4(subBlock, memorySegment, scalesOffset, true);

        byte qsByte = readByte(memorySegment, qsOffset + group * 32 + l);
        int nibble = isHigh ? ((Byte.toUnsignedInt(qsByte) >> 4) & 0xF) : (Byte.toUnsignedInt(qsByte) & 0xF);

        int qhBitPos = isHigh ? 2 * group + 1 : 2 * group;
        int qhBit = (Byte.toUnsignedInt(readByte(memorySegment, qhOffset + l)) >> qhBitPos) & 1;

        int quant = nibble | (qhBit << 4);
        return d * sc * quant - dmin * m;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float vectorDot(Q5_KFloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        int j = 0;

        assert Integer.bitCount(BLOCK_SIZE) == 1 : "power of 2";
        int alignmentBound = Math.min(size, -thisOffset & (BLOCK_SIZE - 1));
        if (alignmentBound > 0) {
            result += FloatTensor.scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j += alignmentBound;
        }

        FloatVector val = FloatVector.zero(F_SPECIES);
        FloatVector val2 = FloatVector.zero(F_SPECIES);
        long blockOffset = (long) (thisOffset + j) / BLOCK_SIZE * TYPE_SIZE;
        int upperBound = j + (size - j) / BLOCK_SIZE * BLOCK_SIZE;

        for (; j < upperBound; j += BLOCK_SIZE, blockOffset += TYPE_SIZE) {
            float d = readFloat16(thiz.memorySegment, blockOffset);
            float dmin = readFloat16(thiz.memorySegment, blockOffset + 2);
            long scalesOff = blockOffset + 4;
            long qhOff = blockOffset + 16;
            long qsOff = blockOffset + 48;
            var qh0 = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment, qhOff, ByteOrder.LITTLE_ENDIAN);
            var qh1 = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment, qhOff + 16, ByteOrder.LITTLE_ENDIAN);

            for (int g = 0; g < 4; g++) {
                int loSubBlock = g * 2;
                int hiSubBlock = loSubBlock + 1;
                float d1 = d * Q4_KFloatTensor.getScaleMinK4(loSubBlock, thiz.memorySegment, scalesOff, false);
                float m1 = dmin * Q4_KFloatTensor.getScaleMinK4(loSubBlock, thiz.memorySegment, scalesOff, true);
                float d2 = d * Q4_KFloatTensor.getScaleMinK4(hiSubBlock, thiz.memorySegment, scalesOff, false);
                float m2 = dmin * Q4_KFloatTensor.getScaleMinK4(hiSubBlock, thiz.memorySegment, scalesOff, true);
                int qhBitPosLo = 2 * g;
                int qhBitPosHi = qhBitPosLo + 1;
                long groupQsOff = qsOff + (long) g * 32;
                var d1Vec = FloatVector.broadcast(F_SPECIES, d1);
                var d2Vec = FloatVector.broadcast(F_SPECIES, d2);
                var negM1Vec = FloatVector.broadcast(F_SPECIES, -m1);
                var negM2Vec = FloatVector.broadcast(F_SPECIES, -m2);

                for (int c = 0; c < 2; c++) {
                    int loBase = thatOffset + j + g * 64 + c * 16;
                    int hiBase = thatOffset + j + g * 64 + 32 + c * 16;

                    var wBytes = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment,
                            groupQsOff + c * 16L, ByteOrder.LITTLE_ENDIAN);
                    var loQ = wBytes.and((byte) 0xF);
                    var hiQ = wBytes.lanewise(VectorOperators.LSHR, 4);

                    var qhBytes = c == 0 ? qh0 : qh1;
                    loQ = loQ.or(qhBytes.lanewise(VectorOperators.LSHR, qhBitPosLo).and((byte) 1).lanewise(VectorOperators.LSHL, 4));
                    hiQ = hiQ.or(qhBytes.lanewise(VectorOperators.LSHR, qhBitPosHi).and((byte) 1).lanewise(VectorOperators.LSHL, 4));

                    switch (F_SPECIES.vectorBitSize()) {
                        case 512 -> {
                            var loQf = loQ.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var hiQf = hiQ.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            val = loQf.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loBase), val);
                            val2 = hiQf.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiBase), val2);
                        }
                        case 256 -> {
                            var loQf0 = loQ.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var loQf1 = loQ.castShape(F_SPECIES, 1).reinterpretAsFloats();
                            var hiQf0 = hiQ.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var hiQf1 = hiQ.castShape(F_SPECIES, 1).reinterpretAsFloats();
                            val = loQf0.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loBase), val);
                            val = loQf1.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loBase + F_SPECIES.length()), val);
                            val2 = hiQf0.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiBase), val2);
                            val2 = hiQf1.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiBase + F_SPECIES.length()), val2);
                        }
                        case 128 -> {
                            for (int p = 0; p < 4; p++) {
                                int off = p * F_SPECIES.length();
                                var loQf = loQ.castShape(F_SPECIES, p).reinterpretAsFloats();
                                var hiQf = hiQ.castShape(F_SPECIES, p).reinterpretAsFloats();
                                val = loQf.fma(d1Vec, negM1Vec).fma(that.getFloatVector(F_SPECIES, loBase + off), val);
                                val2 = hiQf.fma(d2Vec, negM2Vec).fma(that.getFloatVector(F_SPECIES, hiBase + off), val2);
                            }
                        }
                        default -> throw new UnsupportedOperationException(F_SPECIES.toString());
                    }
                }
            }
        }

        result += val.add(val2).reduceLanes(VectorOperators.ADD);

        if (j < size) {
            result += FloatTensor.scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }

        return result;
    }
}

final class Q6_KFloatTensor extends FloatTensor {

    static final int BLOCK_SIZE = GGMLType.QK_K;
    static final int TYPE_SIZE = GGMLType.Q6_K.getTypeSize();

    final long size;
    final MemorySegment memorySegment;

    public Q6_KFloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.Q6_K; }

    // Block layout: ql[128] | qh[64] | scales[16] (int8) | d (fp16)
    // 256 elements per block, 6-bit quants: 4 from ql nibble + 2 from qh

    @Override
    public float getFloat(long index) {
        long blockIndex = index / BLOCK_SIZE;
        int withinBlock = (int) (index % BLOCK_SIZE);
        long blockOffset = blockIndex * TYPE_SIZE;
        long qlOff = blockOffset;
        long qhOff = blockOffset + 128;
        long scOff = blockOffset + 192;
        float d = readFloat16(memorySegment, blockOffset + 208);

        int half = withinBlock / 128;
        int rem128 = withinBlock % 128;
        int sub32 = rem128 / 32;
        int l = rem128 % 32;

        long qlBase = qlOff + half * 64;
        long qhBase = qhOff + half * 32;

        int qlNibble, qhShift;
        switch (sub32) {
            case 0 -> { qlNibble = Byte.toUnsignedInt(readByte(memorySegment, qlBase + l)) & 0xF; qhShift = 0; }
            case 1 -> { qlNibble = Byte.toUnsignedInt(readByte(memorySegment, qlBase + 32 + l)) & 0xF; qhShift = 2; }
            case 2 -> { qlNibble = (Byte.toUnsignedInt(readByte(memorySegment, qlBase + l)) >> 4) & 0xF; qhShift = 4; }
            case 3 -> { qlNibble = (Byte.toUnsignedInt(readByte(memorySegment, qlBase + 32 + l)) >> 4) & 0xF; qhShift = 6; }
            default -> throw new IllegalStateException();
        }

        int qhBits = (Byte.toUnsignedInt(readByte(memorySegment, qhBase + l)) >> qhShift) & 3;
        int q6 = (qlNibble | (qhBits << 4)) - 32;
        int sc = readByte(memorySegment, scOff + half * 8 + sub32 * 2 + l / 16); // signed int8

        return d * sc * q6;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float fp16ToFloatNoIntrinsic(short h) {
        int bits = Short.toUnsignedInt(h);
        int sign = (bits & 0x8000) << 16;
        int exp = (bits >>> 10) & 0x1F;
        int mantissa = bits & 0x03FF;

        if (exp == 0) {
            if (mantissa == 0) {
                return Float.intBitsToFloat(sign);
            }
            int e = 127 - 15 + 1;
            while ((mantissa & 0x0400) == 0) {
                mantissa <<= 1;
                e--;
            }
            mantissa &= 0x03FF;
            return Float.intBitsToFloat(sign | (e << 23) | (mantissa << 13));
        }
        if (exp == 0x1F) {
            return Float.intBitsToFloat(sign | 0x7F80_0000 | (mantissa << 13));
        }
        return Float.intBitsToFloat(sign | ((exp + (127 - 15)) << 23) | (mantissa << 13));
    }

    private static float vectorDot(Q6_KFloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        int j = 0;

        assert Integer.bitCount(BLOCK_SIZE) == 1 : "power of 2";
        int alignmentBound = Math.min(size, -thisOffset & (BLOCK_SIZE - 1));
        if (alignmentBound > 0) {
            result += FloatTensor.scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j += alignmentBound;
        }

        FloatVector acc = FloatVector.zero(F_SPECIES);
        long blockOffset = (long) (thisOffset + j) / BLOCK_SIZE * TYPE_SIZE;
        int upperBound = j + (size - j) / BLOCK_SIZE * BLOCK_SIZE;

        for (; j < upperBound; j += BLOCK_SIZE, blockOffset += TYPE_SIZE) {
            long qlOff = blockOffset;
            long qhOff = blockOffset + 128;
            long scOff = blockOffset + 192;
            // NOTE: Deliberately avoid Float.float16ToFloat here.
            // In native-image builds, Graal can lower that intrinsic to VCVTPH2PS with
            // an illegal high XMM operand under heavy vector register pressure in Q6_K
            // vectorDot, causing a compile-time crash. Keep this software conversion
            // until the Graal backend bug is fixed.
            float d = fp16ToFloatNoIntrinsic(readShort(thiz.memorySegment, blockOffset + 208));

            for (int h = 0; h < 2; h++) {
                long qlBase = qlOff + h * 64;
                long qhBase = qhOff + h * 32;

                int base = thatOffset + j + h * 128;
                for (int c = 0; c < 2; c++) {
                    var qlA = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment,
                            qlBase + c * 16L, ByteOrder.LITTLE_ENDIAN);
                    var qlB = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment,
                            qlBase + 32 + c * 16L, ByteOrder.LITTLE_ENDIAN);
                    var qhV = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment,
                            qhBase + c * 16L, ByteOrder.LITTLE_ENDIAN);

                    var q0 = qlA.and((byte) 0xF).or(qhV.and((byte) 3).lanewise(VectorOperators.LSHL, 4)).sub((byte) 32);
                    var q1 = qlB.and((byte) 0xF).or(qhV.lanewise(VectorOperators.LSHR, 2).and((byte) 3).lanewise(VectorOperators.LSHL, 4)).sub((byte) 32);
                    var q2 = qlA.lanewise(VectorOperators.LSHR, 4).or(qhV.lanewise(VectorOperators.LSHR, 4).and((byte) 3).lanewise(VectorOperators.LSHL, 4)).sub((byte) 32);
                    var q3 = qlB.lanewise(VectorOperators.LSHR, 4).or(qhV.lanewise(VectorOperators.LSHR, 6).and((byte) 3).lanewise(VectorOperators.LSHL, 4)).sub((byte) 32);

                    float ds0 = d * readByte(thiz.memorySegment, scOff + h * 8 + c);
                    float ds1 = d * readByte(thiz.memorySegment, scOff + h * 8 + 2 + c);
                    float ds2 = d * readByte(thiz.memorySegment, scOff + h * 8 + 4 + c);
                    float ds3 = d * readByte(thiz.memorySegment, scOff + h * 8 + 6 + c);

                    var ds0Vec = FloatVector.broadcast(F_SPECIES, ds0);
                    var ds1Vec = FloatVector.broadcast(F_SPECIES, ds1);
                    var ds2Vec = FloatVector.broadcast(F_SPECIES, ds2);
                    var ds3Vec = FloatVector.broadcast(F_SPECIES, ds3);

                    int sg0Idx = base + c * 16;
                    int sg1Idx = base + 32 + c * 16;
                    int sg2Idx = base + 64 + c * 16;
                    int sg3Idx = base + 96 + c * 16;

                    switch (F_SPECIES.vectorBitSize()) {
                        case 512 -> {
                            var q0f = q0.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var q1f = q1.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var q2f = q2.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            var q3f = q3.castShape(F_SPECIES, 0).reinterpretAsFloats();
                            acc = q0f.mul(ds0Vec).fma(that.getFloatVector(F_SPECIES, sg0Idx), acc);
                            acc = q1f.mul(ds1Vec).fma(that.getFloatVector(F_SPECIES, sg1Idx), acc);
                            acc = q2f.mul(ds2Vec).fma(that.getFloatVector(F_SPECIES, sg2Idx), acc);
                            acc = q3f.mul(ds3Vec).fma(that.getFloatVector(F_SPECIES, sg3Idx), acc);
                        }
                        case 256 -> {
                            for (int p = 0; p < 2; p++) {
                                int off = p * F_SPECIES.length();
                                var q0f = q0.castShape(F_SPECIES, p).reinterpretAsFloats();
                                var q1f = q1.castShape(F_SPECIES, p).reinterpretAsFloats();
                                var q2f = q2.castShape(F_SPECIES, p).reinterpretAsFloats();
                                var q3f = q3.castShape(F_SPECIES, p).reinterpretAsFloats();
                                acc = q0f.mul(ds0Vec).fma(that.getFloatVector(F_SPECIES, sg0Idx + off), acc);
                                acc = q1f.mul(ds1Vec).fma(that.getFloatVector(F_SPECIES, sg1Idx + off), acc);
                                acc = q2f.mul(ds2Vec).fma(that.getFloatVector(F_SPECIES, sg2Idx + off), acc);
                                acc = q3f.mul(ds3Vec).fma(that.getFloatVector(F_SPECIES, sg3Idx + off), acc);
                            }
                        }
                        case 128 -> {
                            for (int p = 0; p < 4; p++) {
                                int off = p * F_SPECIES.length();
                                var q0f = q0.castShape(F_SPECIES, p).reinterpretAsFloats();
                                var q1f = q1.castShape(F_SPECIES, p).reinterpretAsFloats();
                                var q2f = q2.castShape(F_SPECIES, p).reinterpretAsFloats();
                                var q3f = q3.castShape(F_SPECIES, p).reinterpretAsFloats();
                                acc = q0f.mul(ds0Vec).fma(that.getFloatVector(F_SPECIES, sg0Idx + off), acc);
                                acc = q1f.mul(ds1Vec).fma(that.getFloatVector(F_SPECIES, sg1Idx + off), acc);
                                acc = q2f.mul(ds2Vec).fma(that.getFloatVector(F_SPECIES, sg2Idx + off), acc);
                                acc = q3f.mul(ds3Vec).fma(that.getFloatVector(F_SPECIES, sg3Idx + off), acc);
                            }
                        }
                        default -> throw new UnsupportedOperationException(F_SPECIES.toString());
                    }
                }
            }
        }

        result += acc.reduceLanes(VectorOperators.ADD);

        if (j < size) {
            result += FloatTensor.scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }

        return result;
    }

}

final class Q8_0FloatTensor extends FloatTensor {

    final long size;
    final MemorySegment memorySegment;

    public Q8_0FloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override
    long size() {
        return size;
    }

    @Override
    public void setFloat(int index, float value) {
        throw new UnsupportedOperationException("setFloat");
    }

    @Override
    FloatVector getFloatVector(VectorSpecies<Float> species, int index) {
        throw new UnsupportedOperationException("getFloatVector");
    }

    @Override
    public GGMLType type() {
        return GGMLType.Q8_0;
    }

    @Override
    public float getFloat(long index) {
        long blockIndex = index / GGMLType.Q8_0.getBlockSize();
        long withinBlockIndex = index % GGMLType.Q8_0.getBlockSize();
        long blockOffset = blockIndex * GGMLType.Q8_0.getTypeSize();
        byte quant = readByte(memorySegment, blockOffset + Float16.BYTES + withinBlockIndex);
        float scale = readFloat16(memorySegment, blockOffset);
        return quant * scale;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float vectorDot(Q8_0FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        int j = 0;

        assert Integer.bitCount(GGMLType.Q8_0.getBlockSize()) == 1 : "power of 2";
        int alignmentBound = Math.min(size, -thisOffset & (GGMLType.Q8_0.getBlockSize() - 1));
        if (alignmentBound > 0) {
            result += FloatTensor.scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j += alignmentBound;
        }
        assert (thisOffset + j) % GGMLType.Q8_0.getBlockSize() == 0;

        FloatVector val = FloatVector.zero(F_SPECIES);
        long blockOffset = (long) (thisOffset + j) / GGMLType.Q8_0.getBlockSize() * GGMLType.Q8_0.getTypeSize();
        int upperBound = j + (size - j) / GGMLType.Q8_0.getBlockSize() * GGMLType.Q8_0.getBlockSize();
        for (; j < upperBound; j += GGMLType.Q8_0.getBlockSize(), blockOffset += GGMLType.Q8_0.getTypeSize()) {
            float wScaleValue = readFloat16(thiz.memorySegment, blockOffset);
            var wScale = FloatVector.broadcast(F_SPECIES, wScaleValue);
            switch (F_SPECIES.vectorBitSize()) {
                case 512 -> {
                    var wBytes = ByteVector.fromMemorySegment(ByteVector.SPECIES_256, thiz.memorySegment, blockOffset + Float16.BYTES, ByteOrder.LITTLE_ENDIAN);
                    var s0 = that.getFloatVector(F_SPECIES, thatOffset + j).mul(wBytes.castShape(F_SPECIES, 0));
                    var s1 = that.getFloatVector(F_SPECIES, thatOffset + j + F_SPECIES.length()).mul(wBytes.castShape(F_SPECIES, 1));
                    val = s0.add(s1).fma(wScale, val);
                }
                case 256 -> {
                    var wBytes = ByteVector.fromMemorySegment(ByteVector.SPECIES_256, thiz.memorySegment, blockOffset + Float16.BYTES, ByteOrder.LITTLE_ENDIAN);
                    var s0 = that.getFloatVector(F_SPECIES, thatOffset + j).mul(wBytes.castShape(F_SPECIES, 0));
                    var s1 = that.getFloatVector(F_SPECIES, thatOffset + j + 2 * F_SPECIES.length()).mul(wBytes.castShape(F_SPECIES, 2));
                    s0 = that.getFloatVector(F_SPECIES, thatOffset + j + F_SPECIES.length()).fma(wBytes.castShape(F_SPECIES, 1), s0);
                    s1 = that.getFloatVector(F_SPECIES, thatOffset + j + 3 * F_SPECIES.length()).fma(wBytes.castShape(F_SPECIES, 3), s1);
                    val = s0.add(s1).fma(wScale, val);
                }
                case 128 -> {
                    for (int i = 0; i < 2; ++i) {
                        var wBytes = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment, blockOffset + Float16.BYTES + i * ByteVector.SPECIES_128.vectorByteSize(), ByteOrder.LITTLE_ENDIAN);
                        var s0 = that.getFloatVector(F_SPECIES, thatOffset + j + i * 16).mul(wBytes.castShape(F_SPECIES, 0));
                        var s1 = that.getFloatVector(F_SPECIES, thatOffset + j + i * 16 + 2 * F_SPECIES.length()).mul(wBytes.castShape(F_SPECIES, 2));
                        s0 = that.getFloatVector(F_SPECIES, thatOffset + j + i * 16 + F_SPECIES.length()).fma(wBytes.castShape(F_SPECIES, 1), s0);
                        s1 = that.getFloatVector(F_SPECIES, thatOffset + j + i * 16 + 3 * F_SPECIES.length()).fma(wBytes.castShape(F_SPECIES, 3), s1);
                        val = s0.add(s1).fma(wScale, val);
                    }
                }
                default -> throw new UnsupportedOperationException(F_SPECIES.toString());
            }
        }
        result += val.reduceLanes(VectorOperators.ADD);

        if (j < size) {
            result += FloatTensor.scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }

        return result;
    }
}

final class MXFP4FloatTensor extends FloatTensor {

    private static final int[] MXFP4_VALUES = {0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12};

    private final long size;
    private final MemorySegment memorySegment;

    MXFP4FloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.MXFP4; }

    @Override
    public float getFloat(long index) {
        assert 0 <= index && index < size;
        long blockIndex = index / GGMLType.QK_MXFP4;
        int inBlockIndex = (int) (index % GGMLType.QK_MXFP4);
        long blockOffset = blockIndex * GGMLType.MXFP4.getTypeSize();

        int e8m0 = Byte.toUnsignedInt(readByte(memorySegment, blockOffset));
        float d = e8m0ToFp32Half(e8m0);

        long qsOffset = blockOffset + Byte.BYTES + (inBlockIndex & 0x0F);
        int packed = Byte.toUnsignedInt(readByte(memorySegment, qsOffset));
        int nibble = inBlockIndex < (GGMLType.QK_MXFP4 / 2) ? (packed & 0x0F) : ((packed >>> 4) & 0x0F);

        return MXFP4_VALUES[nibble] * d;
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (that instanceof ArrayFloatTensor aft) {
            if (FloatTensor.USE_VECTOR_API) {
                return vectorDot(this, thisOffset, aft, thatOffset, size);
            }
            return scalarDot(this, thisOffset, aft, thatOffset, size);
        }
        return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
    }

    private static float vectorDot(MXFP4FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        assert Integer.bitCount(GGMLType.QK_MXFP4) == 1 : "power of 2";
        int j = 0;
        float result = 0f;

        int alignmentBound = Math.min(size, -thisOffset & (GGMLType.QK_MXFP4 - 1));
        if (alignmentBound > 0) {
            result += scalarDot(thiz, thisOffset, that, thatOffset, alignmentBound);
            j = alignmentBound;
        }

        int upperBound = j + (size - j) / GGMLType.QK_MXFP4 * GGMLType.QK_MXFP4;
        for (; j < upperBound; j += GGMLType.QK_MXFP4) {
            assert (thisOffset + j) % GGMLType.QK_MXFP4 == 0;
            long blockOffset = (long) (thisOffset + j) / GGMLType.QK_MXFP4 * GGMLType.MXFP4.getTypeSize();
            float d = e8m0ToFp32Half(Byte.toUnsignedInt(readByte(thiz.memorySegment, blockOffset)));

            ByteVector packed = ByteVector.fromMemorySegment(ByteVector.SPECIES_128, thiz.memorySegment, blockOffset + Byte.BYTES, ByteOrder.LITTLE_ENDIAN);
            ByteVector lo = packed.and((byte) 0x0F);
            ByteVector hi = packed.lanewise(VectorOperators.LSHR, 4);

            float blockSum = 0f;
            switch (F_SPECIES.vectorBitSize()) {
                case 512 -> {
                    FloatVector loCoeffs = mxfp4CodesToCoeffs((FloatVector) lo.castShape(F_SPECIES, 0));
                    FloatVector hiCoeffs = mxfp4CodesToCoeffs((FloatVector) hi.castShape(F_SPECIES, 0));
                    FloatVector xLo = that.getFloatVector(F_SPECIES, thatOffset + j);
                    FloatVector xHi = that.getFloatVector(F_SPECIES, thatOffset + j + GGMLType.QK_MXFP4 / 2);
                    blockSum += loCoeffs.fma(xLo, hiCoeffs.mul(xHi)).reduceLanes(VectorOperators.ADD);
                }
                case 256 -> {
                    FloatVector lo0 = mxfp4CodesToCoeffs((FloatVector) lo.castShape(F_SPECIES, 0));
                    FloatVector lo1 = mxfp4CodesToCoeffs((FloatVector) lo.castShape(F_SPECIES, 1));
                    FloatVector hi0 = mxfp4CodesToCoeffs((FloatVector) hi.castShape(F_SPECIES, 0));
                    FloatVector hi1 = mxfp4CodesToCoeffs((FloatVector) hi.castShape(F_SPECIES, 1));
                    FloatVector x0 = that.getFloatVector(F_SPECIES, thatOffset + j);
                    FloatVector x1 = that.getFloatVector(F_SPECIES, thatOffset + j + F_SPECIES.length());
                    FloatVector x2 = that.getFloatVector(F_SPECIES, thatOffset + j + GGMLType.QK_MXFP4 / 2);
                    FloatVector x3 = that.getFloatVector(F_SPECIES, thatOffset + j + GGMLType.QK_MXFP4 / 2 + F_SPECIES.length());
                    blockSum += lo0.fma(x0, lo1.mul(x1)).reduceLanes(VectorOperators.ADD);
                    blockSum += hi0.fma(x2, hi1.mul(x3)).reduceLanes(VectorOperators.ADD);
                }
                case 128 -> {
                    FloatVector sum = FloatVector.zero(F_SPECIES);
                    for (int p = 0; p < 4; p++) {
                        FloatVector loPart = mxfp4CodesToCoeffs((FloatVector) lo.castShape(F_SPECIES, p));
                        FloatVector hiPart = mxfp4CodesToCoeffs((FloatVector) hi.castShape(F_SPECIES, p));
                        FloatVector xLo = that.getFloatVector(F_SPECIES, thatOffset + j + p * F_SPECIES.length());
                        FloatVector xHi = that.getFloatVector(F_SPECIES, thatOffset + j + GGMLType.QK_MXFP4 / 2 + p * F_SPECIES.length());
                        sum = loPart.fma(xLo, sum);
                        sum = hiPart.fma(xHi, sum);
                    }
                    blockSum += sum.reduceLanes(VectorOperators.ADD);
                }
                default -> throw new UnsupportedOperationException(F_SPECIES.toString());
            }

            result += blockSum * d;
        }

        if (j < size) {
            result += scalarDot(thiz, thisOffset + j, that, thatOffset + j, size - j);
        }
        return result;
    }

    private static FloatVector mxfp4CodesToCoeffs(FloatVector codes) {
        FloatVector zero = FloatVector.zero(F_SPECIES);
        FloatVector eight = FloatVector.broadcast(F_SPECIES, 8f);
        var negMask = codes.compare(VectorOperators.GE, 8f);

        FloatVector t = codes.sub(zero.blend(eight, negMask));
        FloatVector mag = t
                .add(t.sub(4f).lanewise(VectorOperators.MAX, 0f))
                .add(t.sub(6f).lanewise(VectorOperators.MAX, 0f).mul(2f));
        return mag.blend(mag.neg(), negMask);
    }

    private static float scalarDot(MXFP4FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        float result = 0f;
        for (int i = 0; i < size; i++) {
            result += thiz.getFloat(thisOffset + i) * that.values[thatOffset + i];
        }
        return result;
    }

    private static float e8m0ToFp32Half(int x) {
        int bits;
        if (x < 2) {
            bits = 0x00200000 << x;
        } else {
            bits = (x - 1) << 23;
        }
        return Float.intBitsToFloat(bits);
    }
}

final class BF16FloatTensor extends FloatTensor {

    final long size;
    final MemorySegment memorySegment;

    public BF16FloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.BF16; }

    @Override
    public float getFloat(long index) {
        assert 0 <= index && index < size;
        short bits = readShort(memorySegment, (long) index * 2);
        return Float.intBitsToFloat(bits << 16);
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float vectorDot(BF16FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        assert S_SPECIES_HALF.length() == F_SPECIES.length();
        FloatVector val = FloatVector.zero(F_SPECIES);
        int upperBound = F_SPECIES.loopBound(size);
        for (int i = 0; i < upperBound; i += F_SPECIES.length()) {
            FloatVector thatVector = that.getFloatVector(F_SPECIES, thatOffset + i);
            ShortVector bfloat16 = ShortVector.fromMemorySegment(S_SPECIES_HALF, thiz.memorySegment, (thisOffset + i) * 2L, ByteOrder.LITTLE_ENDIAN);
            FloatVector thizVector = bfloat16
                    .castShape(I_SPECIES, 0)
                    .lanewise(VectorOperators.LSHL, 16)
                    .reinterpretAsFloats();
            val = thizVector.fma(thatVector, val);
        }
        float result = val.reduceLanes(VectorOperators.ADD);
        if (upperBound < size) {
            result += scalarDot(thiz, thisOffset + upperBound, that, thatOffset + upperBound, size - upperBound);
        }
        return result;
    }
}

final class F16FloatTensor extends FloatTensor {

    final long size;
    final MemorySegment memorySegment;

    public F16FloatTensor(long size, MemorySegment memorySegment) {
        this.size = size;
        this.memorySegment = memorySegment;
    }

    @Override long size() { return size; }
    @Override public void setFloat(int index, float value) { throw new UnsupportedOperationException("setFloat"); }
    @Override FloatVector getFloatVector(VectorSpecies<Float> species, int index) { throw new UnsupportedOperationException("getFloatVector"); }
    @Override public GGMLType type() { return GGMLType.F16; }

    @Override
    public float getFloat(long index) {
        assert 0 <= index && index < size;
        return readFloat16(memorySegment, (long) index * 2);
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (FloatTensor.USE_VECTOR_API) {
            return vectorDot(this, thisOffset, (ArrayFloatTensor) that, thatOffset, size);
        } else {
            return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
        }
    }

    private static float vectorDot(F16FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        assert S_SPECIES_HALF.length() == F_SPECIES.length();
        FloatVector val = FloatVector.zero(F_SPECIES);
        int upperBound = F_SPECIES.loopBound(size);
        for (int i = 0; i < upperBound; i += F_SPECIES.length()) {
            FloatVector thatVector = that.getFloatVector(F_SPECIES, thatOffset + i);
            ShortVector bits16 = ShortVector.fromMemorySegment(S_SPECIES_HALF, thiz.memorySegment, (thisOffset + i) * 2L, ByteOrder.LITTLE_ENDIAN);
            var bits32 = bits16.castShape(I_SPECIES, 0).reinterpretAsInts();
            var zeroExponentMask = bits32.and(0x7C00).neg().lanewise(VectorOperators.ASHR, 31);
            bits32 = bits32.and(0x8000).lanewise(VectorOperators.LSHL, 16)
                    .or(bits32.and(0x7FFF).add(0x1C000).lanewise(VectorOperators.LSHL, 13).and(zeroExponentMask));
            FloatVector thizVector = bits32.reinterpretAsFloats();
            val = thizVector.fma(thatVector, val);
        }
        float result = val.reduceLanes(VectorOperators.ADD);
        if (upperBound < size) {
            result += scalarDot(thiz, thisOffset + upperBound, that, thatOffset + upperBound, size - upperBound);
        }
        return result;
    }
}

final class ArrayFloatTensor extends FloatTensor {

    final long size;
    final float[] values;

    ArrayFloatTensor(float[] values) {
        this.size = values.length;
        this.values = values;
    }

    ArrayFloatTensor(FloatBuffer buf) {
        this.values = new float[buf.remaining()];
        this.size = values.length;
        buf.get(this.values);
        buf.rewind();
    }

    public static FloatTensor allocate(int... dims) {
        int numberOfElements = FloatTensor.numberOfElements(dims);
        return new ArrayFloatTensor(new float[numberOfElements]);
    }

    @Override
    public long size() {
        return size;
    }

    @Override
    public float getFloat(long index) {
        return values[Math.toIntExact(index)];
    }

    @Override
    public void setFloat(int index, float value) {
        values[index] = value;
    }

    @Override
    public GGMLType type() {
        return GGMLType.F32;
    }

    @Override
    public FloatTensor fillInPlace(int thisOffset, int size, float value) {
        Arrays.fill(values, thisOffset, thisOffset + size, value);
        return this;
    }

    @Override
    public FloatVector getFloatVector(VectorSpecies<Float> species, int index) {
        if (!USE_VECTOR_API) {
            throw new UnsupportedOperationException();
        }
        return FloatVector.fromArray(species, values, index);
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (that instanceof ArrayFloatTensor aft) {
            if (USE_VECTOR_API) {
                return vectorDot(this, thisOffset, aft, thatOffset, size);
            }
            return FloatTensor.scalarDot(this, thisOffset, aft, thatOffset, size);
        }
        return that.dot(thatOffset, this, thisOffset, size);
    }

    private static float vectorDot(ArrayFloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        FloatVector val = FloatVector.zero(F_SPECIES);
        int upperBound = F_SPECIES.loopBound(size);
        for (int i = 0; i < upperBound; i += F_SPECIES.length()) {
            var a = FloatVector.fromArray(F_SPECIES, thiz.values, thisOffset + i);
            var b = FloatVector.fromArray(F_SPECIES, that.values, thatOffset + i);
            val = a.fma(b, val);
        }
        float result = val.reduceLanes(VectorOperators.ADD);
        for (int i = upperBound; i < size; i++) {
            result += thiz.values[thisOffset + i] * that.values[thatOffset + i];
        }
        return result;
    }
}

final class F32FloatTensor extends FloatTensor {

    private final long size;
    private final MemorySegment memorySegment;

    F32FloatTensor(long numElements, MemorySegment memorySegment) {
        this.size = numElements;
        this.memorySegment = memorySegment;
    }

    @Override public long size() { return size; }

    @Override
    public float getFloat(long index) {
        return memorySegment.get(ValueLayout.JAVA_FLOAT_UNALIGNED, (long) index * Float.BYTES);
    }

    @Override
    public void setFloat(int index, float value) {
        throw new UnsupportedOperationException("read-only");
    }

    @Override public GGMLType type() { return GGMLType.F32; }

    @Override
    public FloatVector getFloatVector(VectorSpecies<Float> species, int index) {
        if (!USE_VECTOR_API) {
            throw new UnsupportedOperationException();
        }
        return FloatVector.fromMemorySegment(species, memorySegment, (long) index * Float.BYTES, ByteOrder.LITTLE_ENDIAN);
    }

    @Override
    public float dot(int thisOffset, FloatTensor that, int thatOffset, int size) {
        if (that instanceof ArrayFloatTensor aft && USE_VECTOR_API) {
            return vectorDot(this, thisOffset, aft, thatOffset, size);
        }
        return FloatTensor.scalarDot(this, thisOffset, that, thatOffset, size);
    }

    private static float vectorDot(F32FloatTensor thiz, int thisOffset, ArrayFloatTensor that, int thatOffset, int size) {
        FloatVector val = FloatVector.zero(F_SPECIES);
        int upperBound = F_SPECIES.loopBound(size);
        for (int i = 0; i < upperBound; i += F_SPECIES.length()) {
            var a = FloatVector.fromMemorySegment(F_SPECIES, thiz.memorySegment, (long) (thisOffset + i) * Float.BYTES, ByteOrder.LITTLE_ENDIAN);
            var b = FloatVector.fromArray(F_SPECIES, that.values, thatOffset + i);
            val = a.fma(b, val);
        }
        float result = val.reduceLanes(VectorOperators.ADD);
        for (int i = upperBound; i < size; i++) {
            result += thiz.getFloat(thisOffset + i) * that.values[thatOffset + i];
        }
        return result;
    }
}

final class RoPE {
    public static Pair<float[], float[]> precomputeFreqsCis(int contextLength, int headSize, double theta) {
        assert headSize % 2 == 0;
        int halfHead = headSize / 2;
        float[] cr = new float[contextLength * halfHead];
        float[] ci = new float[contextLength * halfHead];
        int n = 0;
        for (int pos = 0; pos < contextLength; ++pos) {
            for (int i = 0; i < headSize; i += 2) {
                float freq = (float) (1.0 / Math.pow(theta, i / (double) headSize));
                float val = pos * freq;
                cr[n] = (float) Math.cos(val);
                ci[n] = (float) Math.sin(val);
                n++;
            }
        }
        assert contextLength * halfHead == n;
        return new Pair<>(cr, ci);
    }

    public static Pair<float[], float[]> precomputeFreqsCisFromFreqs(int contextLength, int headSize, double ropeTheta, float[] ropeFreqFactors) {
        // freq_factors are divisors on top of the standard RoPE base frequencies:
        // theta_i = pos * (1 / (ropeTheta^(2i/headSize))) / freqFactors[i]
        int halfHead = ropeFreqFactors.length;
        assert halfHead == headSize / 2;
        float[] cr = new float[contextLength * halfHead];
        float[] ci = new float[contextLength * halfHead];
        int n = 0;
        for (int pos = 0; pos < contextLength; ++pos) {
            for (int i = 0; i < halfHead; i++) {
                float baseFreq = (float) (1.0 / Math.pow(ropeTheta, (2.0 * i) / headSize));
                float val = pos * baseFreq / ropeFreqFactors[i];
                cr[n] = (float) Math.cos(val);
                ci[n] = (float) Math.sin(val);
                n++;
            }
        }
        assert contextLength * halfHead == n;
        return new Pair<>(cr, ci);
    }
}

record Vocabulary(String[] tokens, float[] scores, Map<String, Integer> tokenToIndex) {
    public Vocabulary(String[] vocabulary, float[] scores) {
        this(vocabulary, scores,
                IntStream.range(0, vocabulary.length)
                        .boxed()
                        .collect(Collectors.toMap(i -> vocabulary[i], i -> i))
        );
    }

    public String get(int tokenIndex) {
        return tokens[tokenIndex];
    }

    public OptionalInt getIndex(String token) {
        Integer value = tokenToIndex.get(token);
        return value != null ? OptionalInt.of(value) : OptionalInt.empty();
    }

    public int size() {
        return tokens.length;
    }

    public float getScore(int tokenIndex) {
        return scores[tokenIndex];
    }
}

@FunctionalInterface
interface Sampler {
    int sampleToken(FloatTensor logits);

    Sampler ARGMAX = FloatTensor::argmax;
}

record CategoricalSampler(RandomGenerator rng) implements Sampler {

    @Override
    public int sampleToken(FloatTensor logits) {
        float random0to1 = rng.nextFloat(1f);
        float cdf = 0.0f;
        for (int i = 0; i < logits.size(); i++) {
            cdf += logits.getFloat(i);
            if (random0to1 < cdf) {
                return i;
            }
        }
        return Math.toIntExact(logits.size()) - 1;
    }
}

final class ToppSampler implements Sampler {

    final int[] indices;
    final float topp;
    final RandomGenerator rng;

    public ToppSampler(int maxNumberOfElements, float topp, RandomGenerator rng) {
        this.indices = new int[maxNumberOfElements];
        this.topp = topp;
        this.rng = rng;
    }

    static void swap(int[] array, int from, int to) {
        int tmp = array[from];
        array[from] = array[to];
        array[to] = tmp;
    }

    static void siftDown(int[] array, int from, int n, Comparator<Integer> comparator) {
        int prev = from, next;
        while ((next = 2 * prev + 1) < n) {
            int r = 2 * prev + 2;
            if (r < n && comparator.compare(array[r], array[next]) < 0) {
                next = r;
            }
            if (comparator.compare(array[next], array[prev]) < 0) {
                swap(array, prev, next);
                prev = next;
            } else {
                break;
            }
        }
    }

    @Override
    public int sampleToken(FloatTensor logits) {
        Comparator<Integer> comparator = Comparator.comparingDouble((Integer i) -> logits.getFloat(i)).reversed();

        int n = Math.toIntExact(logits.size());
        int head = 0;
        int tail = n - 1;
        float cutoff = (1.0f - topp) / (n - 1);
        for (int i = 0; i < indices.length; i++) {
            if (logits.getFloat(i) >= cutoff) {
                indices[head++] = i;
            } else {
                indices[tail--] = i;
            }
        }

        int n0 = head;
        for (int i = n0 / 2 - 1; i >= 0; --i) {
            siftDown(indices, i, n0, comparator);
        }

        float cumulativeProb = 0.0f;
        int lastIndex = 0;
        for (int i = n0 - 1; i >= 0; i--) {
            swap(indices, 0, i);
            cumulativeProb += logits.getFloat(indices[i]);
            if (cumulativeProb > topp) {
                lastIndex = i;
                break;
            }
            siftDown(indices, 0, i - 1, comparator);
        }

        float r = rng.nextFloat(1f) * cumulativeProb;
        float cdf = 0.0f;
        for (int i = n0 - 1; i >= lastIndex; i--) {
            cdf += logits.getFloat(indices[i]);
            if (r < cdf) {
                return indices[i];
            }
        }

        return indices[lastIndex];
    }
}

class GemmaChatFormat {

    protected final GemmaTokenizer tokenizer;
    protected final int beginOfSentence;
    protected final int startOfTurn;
    protected final int endOfTurn;
    protected final int endOfSentence;
    protected final int fimSuffix;
    protected final int fimPrefix;
    protected final int fimMiddle;
    protected final int fileSeparator;

    public GemmaChatFormat(GemmaTokenizer tokenizer) {
        this.tokenizer = tokenizer;
        Map<String, Integer> specialTokens = this.tokenizer.getSpecialTokens();
        this.beginOfSentence = specialTokens.get("<bos>");
        this.startOfTurn = specialTokens.get("<|turn>");
        this.endOfTurn = specialTokens.get("<turn|>");
        this.endOfSentence = specialTokens.get("<eos>");

        this.fimSuffix = specialTokens.getOrDefault("<|fim_suffix|>", -1);
        this.fimPrefix = specialTokens.getOrDefault("<|fim_prefix|>", -1);
        this.fimMiddle = specialTokens.getOrDefault("<|fim_middle|>", -1);
        this.fileSeparator = specialTokens.getOrDefault("<|file_separator|>", -1);
    }

    public Set<Integer> getStopTokens() {
        Set<Integer> tokens = new HashSet<>();
        tokens.add(endOfSentence);
        tokens.add(endOfTurn);
        if (fimSuffix != -1) {
            tokens.add(fimSuffix);
        }
        if (fimPrefix != -1) {
            tokens.add(fimPrefix);
        }
        if (fimMiddle != -1) {
            tokens.add(fimMiddle);
        }
        if (fileSeparator != -1) {
            tokens.add(fileSeparator);
        }
        return tokens;
    }

    public List<Integer> encodeHeader(GemmaChatFormat.Message message) {
        List<Integer> tokens = new ArrayList<>();
        tokens.add(startOfTurn);
        tokens.addAll(tokenizer.encode(message.role().toString()));
        tokens.addAll(this.tokenizer.encode("\n"));
        return tokens;
    }

    public List<Integer> encodeMessage(GemmaChatFormat.Message message) {
        List<Integer> tokens = this.encodeHeader(message);
        tokens.addAll(this.tokenizer.encode(message.content().strip()));
        tokens.add(endOfTurn);
        tokens.addAll(this.tokenizer.encode("\n"));
        return tokens;
    }

    public List<Integer> encodeSystemThinkingTurn(String systemPrompt) {
        // Matches Gemma4 template with enable_thinking=true:
        // <|turn>system\n<|think|>[system_content]<turn|>\n
        List<Integer> tokens = new ArrayList<>();
        tokens.addAll(encodeHeader(new Message(Role.SYSTEM, "")));
        Integer thinkToken = tokenizer.getSpecialTokens().get("<|think|>");
        if (thinkToken != null) {
            tokens.add(thinkToken);
        }
        if (systemPrompt != null && !systemPrompt.isEmpty()) {
            tokens.addAll(tokenizer.encode(systemPrompt.trim()));
        }
        tokens.add(endOfTurn);
        tokens.addAll(tokenizer.encode("\n"));
        return tokens;
    }

    public record Message(GemmaChatFormat.Role role, String content) {
    }

    public List<Integer> encodeFillInTheMiddle(String prefix, String suffix) {
        List<Integer> tokens = new ArrayList<>();
        tokens.add(this.fimPrefix);
        tokens.addAll(tokenizer.encode(prefix));
        tokens.add(this.fimSuffix);
        tokens.addAll(tokenizer.encode(suffix));
        tokens.add(this.fimMiddle);
        return tokens;
    }

    public record Role(String name) {
        public static GemmaChatFormat.Role SYSTEM = new GemmaChatFormat.Role("system");
        public static GemmaChatFormat.Role USER = new GemmaChatFormat.Role("user");
        public static GemmaChatFormat.Role MODEL = new GemmaChatFormat.Role("model");

        @Override
        public String toString() {
            return name;
        }
    }
}

public class Gemma4 {

    static Sampler selectSampler(int vocabularySize, float temperature, float topp, long rngSeed) {
        Sampler sampler;
        if (temperature == 0.0f) {
            sampler = Sampler.ARGMAX;
        } else {
            RandomGenerator rng = RandomGeneratorFactory.getDefault().create(rngSeed);
            Sampler innerSampler;
            if (topp <= 0 || topp >= 1) {
                innerSampler = new CategoricalSampler(rng);
            } else {
                innerSampler = new ToppSampler(vocabularySize, topp, rng);
            }
            sampler = logits -> {
                int logitsSize = Math.toIntExact(logits.size());
                logits.divideInPlace(0, logitsSize, temperature);
                logits.softmaxInPlace(0, logitsSize);
                return innerSampler.sampleToken(logits);
            };
        }
        return sampler;
    }

    private static final String ANSI_GREY  = "\033[90m";
    private static final String ANSI_RESET = "\033[0m";

    private static IntConsumer plainStreamingPrinter(GemmaTokenizer tokenizer) {
        return token -> {
            if (!tokenizer.isSpecialToken(token)) {
                System.out.print(tokenizer.decode(List.of(token)));
            }
        };
    }

    private static void onThinkingStart(PrintStream thoughtOut, boolean ansi) {
        if (ansi) {
            thoughtOut.print(ANSI_GREY);
        }
        thoughtOut.println("[Start thinking]");
    }

    private static void onThinkingEnd(PrintStream thoughtOut, boolean ansi, boolean emitted) {
        if (emitted) {
            thoughtOut.println();
        }
        thoughtOut.println("[End thinking]");
        if (ansi) {
            thoughtOut.print(ANSI_RESET);
        }
        thoughtOut.println();
    }

    static boolean supportsAnsiColors(String colorMode) {
        return switch (colorMode) {
            case "on" -> true;
            case "off" -> false;
            case "auto" -> {
                if (System.console() == null) {
                    yield false;
                }
                String noColor = System.getenv("NO_COLOR");
                if (noColor != null) {
                    yield false;
                }
                String term = System.getenv("TERM");
                yield term == null || !"dumb".equalsIgnoreCase(term);
            }
            default -> false;
        };
    }

    private static IntConsumer streamingPrinter(GemmaTokenizer tokenizer, Options options) {
        if (!options.stream()) {
            return token -> {};
        }

        Integer channelOpen = tokenizer.getSpecialTokens().get("<|channel>");
        Integer channelClose = tokenizer.getSpecialTokens().get("<channel|>");
        if (channelOpen == null || channelClose == null) {
            return plainStreamingPrinter(tokenizer);
        }

        boolean thinkEnabled = options.think();
        PrintStream thoughtOut = options.thinkInline() ? System.out : System.err;
        boolean ansi = options.colors();
        boolean[] inChannel = {false};
        boolean[] emitted = {false};
        return token -> {
            if (token == channelOpen) {
                if (thinkEnabled) {
                    onThinkingStart(thoughtOut, ansi);
                }
                inChannel[0] = true;
                emitted[0] = false;
                return;
            }
            if (token == channelClose) {
                if (thinkEnabled) {
                    onThinkingEnd(thoughtOut, ansi, emitted[0]);
                }
                inChannel[0] = false;
                emitted[0] = false;
                return;
            }
            if (!tokenizer.isSpecialToken(token)) {
                String text = tokenizer.decode(List.of(token));
                if (inChannel[0]) {
                    if (thinkEnabled) {
                        thoughtOut.print(text);
                        emitted[0] = true;
                    }
                } else {
                    System.out.print(text);
                }
            }
        };
    }

    private static List<Integer> visibleTokens(GemmaTokenizer tokenizer, List<Integer> tokens, boolean think) {
        return think ? stripThoughtChannelTokens(tokenizer, tokens) : tokens;
    }

    private static List<Integer> stripThoughtChannelTokens(GemmaTokenizer tokenizer, List<Integer> tokens) {
        Integer channelOpen = tokenizer.getSpecialTokens().get("<|channel>");
        Integer channelClose = tokenizer.getSpecialTokens().get("<channel|>");
        if (channelOpen == null || channelClose == null || tokens.isEmpty()) {
            return tokens;
        }
        List<Integer> out = new ArrayList<>(tokens.size());
        boolean inChannel = false;
        for (int tok : tokens) {
            if (tok == channelOpen) { inChannel = true; continue; }
            if (tok == channelClose) { inChannel = false; continue; }
            if (!inChannel) { out.add(tok); }
        }
        return out;
    }

    static void runInteractive(Llama model, Sampler sampler, Options options) {
        Llama.State state = null;
        GemmaChatFormat chatFormat = new GemmaChatFormat(model.tokenizer());
        List<Integer> conversationTokens = new ArrayList<>();
        if (options.think()) {
            conversationTokens.addAll(chatFormat.encodeSystemThinkingTurn(options.systemPrompt()));
        } else if (options.systemPrompt() != null) {
            conversationTokens.addAll(chatFormat.encodeMessage(new GemmaChatFormat.Message(GemmaChatFormat.Role.SYSTEM, options.systemPrompt())));
        }
        int startPosition = 0;
        Scanner in = new Scanner(System.in);
        loop: while (true) {
            System.out.print("> ");
            System.out.flush();
            String userText = in.nextLine();
            switch (userText) {
                case "/quit":
                case "/exit": break loop;
                case "/context": {
                    System.out.printf("%d out of %d context tokens used (%d tokens remaining)%n",
                            conversationTokens.size(),
                            options.maxTokens(),
                            options.maxTokens() - conversationTokens.size());
                    continue;
                }
            }
            if (state == null) {
                state = model.createNewState();
            }
            conversationTokens.addAll(chatFormat.encodeMessage(new GemmaChatFormat.Message(GemmaChatFormat.Role.USER, userText)));
            conversationTokens.addAll(chatFormat.encodeHeader(new GemmaChatFormat.Message(GemmaChatFormat.Role.MODEL, "")));

            Set<Integer> stopTokens = chatFormat.getStopTokens();
            IntConsumer printer = streamingPrinter(model.tokenizer(), options);
            List<Integer> responseTokens = Llama.generateTokens(model, state, startPosition, conversationTokens.subList(startPosition, conversationTokens.size()), stopTokens, options.maxTokens(), sampler, options.echo(), options.colors(), printer);
            Integer stopToken = null;
            if (!responseTokens.isEmpty() && stopTokens.contains(responseTokens.getLast())) {
                stopToken = responseTokens.getLast();
                responseTokens.removeLast();
            }
            List<Integer> visibleResponseTokens = visibleTokens(model.tokenizer(), responseTokens, options.think());
            conversationTokens.addAll(responseTokens);
            if (stopToken != null) {
                conversationTokens.add(stopToken);
            }
            startPosition = conversationTokens.size();
            if (!options.stream()) {
                String responseText = model.tokenizer().decode(visibleResponseTokens);
                System.out.println(responseText);
            }
            if (stopToken == null) {
                System.err.println("Ran out of context length...");
                break;
            }
        }
    }

    static void runInstructOnce(Llama model, Sampler sampler, Options options) {
        Llama.State state = model.createNewState();
        GemmaChatFormat chatFormat = new GemmaChatFormat(model.tokenizer());
        List<Integer> promptTokens = new ArrayList<>();

        if (options.suffix() != null) {
            promptTokens.addAll(chatFormat.encodeFillInTheMiddle(options.prompt(), options.suffix()));
        } else {
            if (options.think()) {
                promptTokens.addAll(chatFormat.encodeSystemThinkingTurn(options.systemPrompt()));
            } else if (options.systemPrompt() != null) {
                promptTokens.addAll(chatFormat.encodeMessage(new GemmaChatFormat.Message(GemmaChatFormat.Role.SYSTEM, options.systemPrompt())));
            }
            promptTokens.addAll(chatFormat.encodeMessage(new GemmaChatFormat.Message(GemmaChatFormat.Role.USER, options.prompt())));
            promptTokens.addAll(chatFormat.encodeHeader(new GemmaChatFormat.Message(GemmaChatFormat.Role.MODEL, "")));
        }

        Set<Integer> stopTokens = chatFormat.getStopTokens();
        IntConsumer printer = streamingPrinter(model.tokenizer(), options);
        List<Integer> responseTokens = Llama.generateTokens(model, state, 0, promptTokens, stopTokens, options.maxTokens(), sampler, options.echo(), options.colors(), printer);
        if (!responseTokens.isEmpty() && stopTokens.contains(responseTokens.getLast())) {
            responseTokens.removeLast();
        }
        List<Integer> visibleResponseTokens = visibleTokens(model.tokenizer(), responseTokens, options.think());
        if (!options.stream()) {
            String responseText = model.tokenizer().decode(visibleResponseTokens);
            System.out.println(responseText);
        }
    }

    static final int DEFAULT_MAX_TOKENS = 1024;

    record Options(Path modelPath, String prompt, String suffix, String systemPrompt, boolean interactive,
                   float temperature, float topp, long seed, int maxTokens, boolean stream, boolean echo,
                   boolean think, boolean thinkInline, boolean colors) {

        Options {
            require(modelPath != null, "Missing argument: --model <path> is required");
            require(interactive || prompt != null, "Missing argument: --prompt is required in --instruct mode e.g. --prompt \"Why is the sky blue?\"");
            require(0 <= temperature, "Invalid argument: --temperature must be non-negative");
            require(0 <= topp && topp <= 1, "Invalid argument: --top-p must be within [0, 1]");
        }

        static void require(boolean condition, String messageFormat, Object... args) {
            if (!condition) {
                System.out.println("ERROR " + messageFormat.formatted(args));
                System.out.println();
                printUsage(System.out);
                System.exit(-1);
            }
        }

        static boolean parseBooleanOption(String optionName, String value) {
            return switch (value.toLowerCase(Locale.ROOT)) {
                case "true", "on" -> true;
                case "false", "off" -> false;
                default -> {
                    require(false, "Invalid argument for %s: expected true|false|on|off, got %s", optionName, value);
                    yield false;
                }
            };
        }

        static void printUsage(PrintStream out) {
            out.println("Usage:  jbang Gemma4.java [options]");
            out.println();
            out.println("Options:");
            out.println("  --model, -m <path>            required, path to .gguf file");
            out.println("  --interactive, --chat, -i     run in chat mode");
            out.println("  --instruct                    run in instruct (once) mode, default mode");
            out.println("  --prompt, -p <string>         input prompt");
            out.println("  --suffix <string>             suffix for fill-in-the-middle request");
            out.println("  --system-prompt, -sp <string> system prompt for chat/instruct mode");
            out.println("  --temperature, -temp <float>  temperature in [0,inf], default 1.0");
            out.println("  --top-p <float>               p value in top-p (nucleus) sampling in [0,1] default 0.95");
            out.println("  --seed <long>                 random seed, default System.nanoTime()");
            out.println("  --max-tokens, -n <int>        number of steps to run for < 0 = limited by context length, default " + DEFAULT_MAX_TOKENS);
            out.println("  --stream <boolean>            print tokens during generation; accepts true|false|on|off, default true");
            out.println("  --echo <boolean>              print ALL tokens to stderr; accepts true|false|on|off, default false");
            out.println("  --color <on|off|auto>         colorize thinking output in terminal (default: auto)");
            out.println("  --think <off|on|inline>       off: disable thoughts, on: thoughts to stderr, inline: thoughts to stdout");
            out.println();
            out.println("Interactive commands:");
            out.println("  /quit, /exit                  exit the chat");
            out.println("  /context                      show context token usage");
            out.println();
            out.println("Examples:");
            out.println("  jbang Gemma4.java --model gemma-4-E2B-it-Q8_0.gguf --chat");
            out.println("  jbang Gemma4.java --model gemma-4-E2B-it-Q8_0.gguf --prompt \"Tell me a joke\"");
            out.println("  jbang Gemma4.java --model gemma-4-E2B-it-Q8_0.gguf --chat --system-prompt \"You are a helpful assistant\"");
        }

        static Options parseOptions(String[] args) {
            String prompt = null;
            String suffix = null;
            String systemPrompt = null;
            float temperature = 1f;
            float topp = 0.95f;
            Path modelPath = null;
            long seed = System.nanoTime();
            int maxTokens = DEFAULT_MAX_TOKENS;
            boolean interactive = false;
            boolean stream = true;
            boolean echo = false;
            boolean think = false;
            boolean thinkInline = false;
            String colorMode = "auto";

            for (int i = 0; i < args.length; i++) {
                String optionName = args[i];
                require(optionName.startsWith("-"), "Invalid option %s", optionName);
                switch (optionName) {
                    case "--interactive", "--chat", "-i" -> interactive = true;
                    case "--instruct" -> interactive = false;
                    case "--help", "-h" -> {
                        printUsage(System.out);
                        System.exit(0);
                    }
                    default -> {
                        String nextArg;
                        if (optionName.contains("=")) {
                            String[] parts = optionName.split("=", 2);
                            optionName = parts[0];
                            nextArg = parts[1];
                        } else {
                            require(i + 1 < args.length, "Missing argument for option %s", optionName);
                            nextArg = args[i + 1];
                            i += 1;
                        }
                        switch (optionName) {
                            case "--prompt", "-p" -> prompt = nextArg;
                            case "--suffix" -> suffix = nextArg;
                            case "--system-prompt", "-sp" -> systemPrompt = nextArg;
                            case "--temperature", "--temp" -> temperature = Float.parseFloat(nextArg);
                            case "--top-p" -> topp = Float.parseFloat(nextArg);
                            case "--model", "-m" -> modelPath = Path.of(nextArg);
                            case "--seed", "-s" -> seed = Long.parseLong(nextArg);
                            case "--max-tokens", "-n" -> maxTokens = Integer.parseInt(nextArg);
                            case "--stream" -> stream = parseBooleanOption(optionName, nextArg);
                            case "--echo" -> echo = parseBooleanOption(optionName, nextArg);
                            case "--color" -> colorMode = nextArg.toLowerCase(Locale.ROOT);
                            case "--think" -> {
                                String thinkMode = nextArg.toLowerCase(Locale.ROOT);
                                thinkInline = List.of("inline", "stdout").contains(thinkMode);
                                switch (thinkMode) {
                                    case "on", "true", "inline", "stdout" -> think = true;
                                    case "off", "false" -> think = false;
                                    default -> require(false, "Invalid argument for %s: expected off|on|inline (or false|true|stdout), got %s", optionName, nextArg);
                                }
                            }
                            default -> require(false, "Unknown option: %s", optionName);
                        }
                    }
                }
            }
            require(List.of("on", "off", "auto").contains(colorMode), "Invalid argument: --color must be one of on|off|auto");
            boolean color = Gemma4.supportsAnsiColors(colorMode);
            return new Options(modelPath, prompt, suffix, systemPrompt, interactive, temperature, topp, seed, maxTokens, stream, echo, think, thinkInline, color);
        }
    }

    public static void main(String[] args) throws IOException {
        Options options = Options.parseOptions(args);
        Llama model = AOT.tryUsePreLoaded(options.modelPath(), options.maxTokens());
        if (model == null) {
            model = ModelLoader.loadModel(options.modelPath(), options.maxTokens());
        }
        Sampler sampler = selectSampler(model.configuration().vocabularySize, options.temperature(), options.topp(), options.seed());
        if (options.interactive()) {
            runInteractive(model, sampler, options);
        } else {
            runInstructOnce(model, sampler, options);
        }
    }
}

final class AOT {
    record PartialModel(String modelFileName, Llama model, long tensorDataOffset,
                        Map<String, GGUF.GGUFTensorInfo> tensorInfos,
                        Pair<float[], float[]> ropeFreqsSWA, Pair<float[], float[]> ropeFreqsFull) {}

    private static final PartialModel PRELOADED_GGUF = preLoadGGUF(System.getProperty("gemma4.PreloadGGUF"));

    private static PartialModel preLoadGGUF(String modelPath) {
        if (modelPath == null || modelPath.isEmpty()) {
            return null;
        }
        try {
            Path path = Path.of(modelPath);
            if (!Files.exists(path) || !Files.isRegularFile(path)) {
                throw new IllegalArgumentException("Cannot pre-load model: " + path);
            }
            try (FileChannel fileChannel = FileChannel.open(path, StandardOpenOption.READ)) {
                GGUF gguf = GGUF.loadModel(fileChannel, path.toString());
                Llama base = ModelLoader.loadModel(null, gguf, Gemma4.DEFAULT_MAX_TOKENS, false);
                // Precompute RoPE frequencies at build time (pure Java arrays, survives native-image)
                Llama.Configuration config = base.configuration();
                Pair<float[], float[]> ropeFreqsSWA = RoPE.precomputeFreqsCis(
                        config.contextLength, config.headSizeSWA, config.ropeThetaSWA);
                // Read rope_freqs from model file
                Pair<float[], float[]> ropeFreqsFull;
                Map<String, GGMLTensorEntry> tmpEntries = GGUF.loadTensors(fileChannel, gguf.getTensorDataOffset(), gguf.getTensorInfos());
                FloatBuffer ropeFreqsBuf = ModelLoader.toFloatBuffer(tmpEntries.get("rope_freqs.weight"));
                float[] modelRopeFreqs = new float[ropeFreqsBuf.remaining()];
                ropeFreqsBuf.get(modelRopeFreqs);
                ropeFreqsFull = RoPE.precomputeFreqsCisFromFreqs(
                        config.contextLength, config.headSizeFull, config.ropeTheta, modelRopeFreqs);
                return new PartialModel(
                        path.getFileName().toString(), base,
                        gguf.getTensorDataOffset(), gguf.getTensorInfos(),
                        ropeFreqsSWA, ropeFreqsFull);
            }
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
    }

    public static Llama tryUsePreLoaded(Path modelPath, int contextLength) throws IOException {
        AOT.PartialModel preLoaded = AOT.PRELOADED_GGUF;
        if (preLoaded == null) {
            return null;
        }
        String optionsModel = modelPath.getFileName().toString();
        String preLoadedModel = preLoaded.modelFileName();
        if (!Objects.equals(optionsModel, preLoadedModel)) {
            return null;
        }
        Llama baseModel = preLoaded.model();
        try (var timer = Timer.log("Load tensors from pre-loaded model");
             var fileChannel = FileChannel.open(modelPath, StandardOpenOption.READ)) {
            Map<String, GGMLTensorEntry> tensorEntries = GGUF.loadTensors(fileChannel, preLoaded.tensorDataOffset(), preLoaded.tensorInfos());
            Llama.Weights weights = ModelLoader.loadWeightsWithRoPE(tensorEntries, baseModel.configuration(),
                    preLoaded.ropeFreqsSWA(), preLoaded.ropeFreqsFull());
            return new Llama(baseModel.configuration().withContextLength(contextLength), baseModel.tokenizer(), weights);
        }
    }
}