From 5ec2bfd97dfd88dba513c2879a2ecb6e6d87db63 Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Mon, 13 Apr 2026 14:15:21 +0200 Subject: [PATCH 1/8] Update transformers to 5.5 withouth minor lock --- inference_models/pyproject.toml | 2 +- inference_models/uv.lock | 159 ++++++++++++++------------------ 2 files changed, 71 insertions(+), 90 deletions(-) diff --git a/inference_models/pyproject.toml b/inference_models/pyproject.toml index 164008fae6..5c10f277c7 100644 --- a/inference_models/pyproject.toml +++ b/inference_models/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "requests>=2.32.0,<3.0.0", "supervision>=0.26.0", "backoff~=2.2.0", - "transformers>=5.2.0,<5.3.0", + "transformers~=5.5", "timm>=1.0.0,<2.0.0", "accelerate>=1.0.0,<2.0.0", "einops>=0.7.0,<1.0.0", diff --git a/inference_models/uv.lock b/inference_models/uv.lock index c58df75c11..b74579f4b1 100644 --- a/inference_models/uv.lock +++ b/inference_models/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 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"sha256:d5d7ee1ee2834d5020c7c616ed5e0d0f29b9a4b1dd283bdebae198ec09778d0e", size = 3394, upload-time = "2026-02-16T22:08:49.92Z" }, -] - [[package]] name = "typing-extensions" version = "4.14.0" From abc98a0e40d242e08a56bfe6cb6ae0a91a0298ca Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Mon, 13 Apr 2026 17:49:35 +0200 Subject: [PATCH 2/8] Release candidate 1 for 0.25.0 --- inference_models/pyproject.toml | 2 +- inference_models/uv.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/inference_models/pyproject.toml b/inference_models/pyproject.toml index 5c10f277c7..28370737f0 100644 --- a/inference_models/pyproject.toml +++ b/inference_models/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "inference-models" -version = "0.24.3" +version = "0.25.0rc1" description = "The new inference engine for Computer Vision models" readme = "README.md" requires-python = ">=3.10,<3.13" diff --git a/inference_models/uv.lock b/inference_models/uv.lock index b74579f4b1..094388b48e 100644 --- a/inference_models/uv.lock +++ b/inference_models/uv.lock @@ -917,7 +917,7 @@ wheels = [ [[package]] name = "inference-models" -version = "0.24.3" +version = "0.25.0rc1" source = { virtual = "." } dependencies = [ { name = "accelerate" }, From ffda41bcac007a4ec06189bc1ff030ec1542c6c3 Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Mon, 13 Apr 2026 18:06:14 +0200 Subject: [PATCH 3/8] Bump inference models to 0.25.0rc1 --- requirements/requirements.cpu.txt | 2 +- requirements/requirements.gpu.txt | 2 +- requirements/requirements.jetson.txt | 2 +- requirements/requirements.vino.txt | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements/requirements.cpu.txt b/requirements/requirements.cpu.txt index cc35172a87..472a895fd2 100644 --- a/requirements/requirements.cpu.txt +++ b/requirements/requirements.cpu.txt @@ -1,3 +1,3 @@ onnxruntime>=1.15.1,<1.22.0 nvidia-ml-py<13.0.0 -inference-models[torch-cpu,onnx-cpu]~=0.24.3 # keep in sync between requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file +inference-models[torch-cpu,onnx-cpu]~=0.25.0rc1 # keep in sync between requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file diff --git a/requirements/requirements.gpu.txt b/requirements/requirements.gpu.txt index 1db18955ea..5dc73f6aa4 100644 --- a/requirements/requirements.gpu.txt +++ b/requirements/requirements.gpu.txt @@ -1,2 +1,2 @@ onnxruntime-gpu>=1.15.1,<1.22.0 -inference-models[torch-cu124,onnx-cu12]~=0.24.3 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt +inference-models[torch-cu124,onnx-cu12]~=0.25.0rc1 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt diff --git a/requirements/requirements.jetson.txt b/requirements/requirements.jetson.txt index 61a5938612..8059c5f9e3 100644 --- a/requirements/requirements.jetson.txt +++ b/requirements/requirements.jetson.txt @@ -1,4 +1,4 @@ pypdfium2>=4.11.0,<5.0.0 jupyterlab>=4.3.0,<5.0.0 PyYAML~=6.0.0 -inference-models~=0.24.3 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file +inference-models~=0.25.0rc1 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file diff --git a/requirements/requirements.vino.txt b/requirements/requirements.vino.txt index ef3bd17110..3e96752113 100644 --- a/requirements/requirements.vino.txt +++ b/requirements/requirements.vino.txt @@ -1,2 +1,2 @@ onnxruntime-openvino>=1.15.0,<1.22.0 -inference-models[torch-cpu]~=0.24.3 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file +inference-models[torch-cpu]~=0.25.0rc1 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file From f32598e50cf1d4af833acf4ca0028a108a068a3b Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Wed, 15 Apr 2026 16:16:23 +0200 Subject: [PATCH 4/8] Add Gemma 4 model support with configuration and registry updates - Introduced new configuration parameters for Gemma 4, including max new tokens, sampling options, and temperature settings. - Registered multiple Gemma 4 model variants in the models registry. - Added Gemma 4 multimodal model implementation in `gemma4_hf.py`. - Created an `__init__.py` for the Gemma 4 module. - Added unit tests to verify model resolution for Gemma 4 variants. --- .../inference_models/configuration.py | 29 ++ .../models/auto_loaders/models_registry.py | 16 + .../models/gemma4/__init__.py | 1 + .../models/gemma4/gemma4_hf.py | 386 ++++++++++++++++++ .../auto_loaders/test_model_registry.py | 23 ++ 5 files changed, 455 insertions(+) create mode 100644 inference_models/inference_models/models/gemma4/__init__.py create mode 100644 inference_models/inference_models/models/gemma4/gemma4_hf.py diff --git a/inference_models/inference_models/configuration.py b/inference_models/inference_models/configuration.py index 0c81482eac..88deca29bb 100644 --- a/inference_models/inference_models/configuration.py +++ b/inference_models/inference_models/configuration.py @@ -234,6 +234,35 @@ variable_name="INFERENCE_MODELS_QWEN25_VL_DEFAULT_SKIP_SPECIAL_TOKENS", default=True, ) +INFERENCE_MODELS_GEMMA4_DEFAULT_MAX_NEW_TOKENS = get_integer_from_env( + variable_name="INFERENCE_MODELS_GEMMA4_DEFAULT_MAX_NEW_TOKENS", + default=512, +) +INFERENCE_MODELS_GEMMA4_DEFAULT_DO_SAMPLE = get_boolean_from_env( + variable_name="INFERENCE_MODELS_GEMMA4_DEFAULT_DO_SAMPLE", + default=INFERENCE_MODELS_DEFAULT_DO_SAMPLE, +) +INFERENCE_MODELS_GEMMA4_DEFAULT_ENABLE_THINKING = get_boolean_from_env( + variable_name="INFERENCE_MODELS_GEMMA4_DEFAULT_ENABLE_THINKING", + default=False, +) +INFERENCE_MODELS_GEMMA4_DEFAULT_SKIP_SPECIAL_TOKENS = get_boolean_from_env( + variable_name="INFERENCE_MODELS_GEMMA4_DEFAULT_SKIP_SPECIAL_TOKENS", + default=True, +) +# Official Gemma 4 sampling recommendations when ``do_sample`` is True (HF model cards). +INFERENCE_MODELS_GEMMA4_DEFAULT_TEMPERATURE = get_float_from_env( + variable_name="INFERENCE_MODELS_GEMMA4_DEFAULT_TEMPERATURE", + default=1.0, +) +INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_P = get_float_from_env( + variable_name="INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_P", + default=0.95, +) +INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_K = get_integer_from_env( + variable_name="INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_K", + default=64, +) INFERENCE_MODELS_RESNET_DEFAULT_CONFIDENCE = get_float_from_env( variable_name="INFERENCE_MODELS_RESNET_DEFAULT_CONFIDENCE", default=INFERENCE_MODELS_DEFAULT_CONFIDENCE, diff --git a/inference_models/inference_models/models/auto_loaders/models_registry.py b/inference_models/inference_models/models/auto_loaders/models_registry.py index b89b21828e..b4d855edb9 100644 --- a/inference_models/inference_models/models/auto_loaders/models_registry.py +++ b/inference_models/inference_models/models/auto_loaders/models_registry.py @@ -286,6 +286,22 @@ class RegistryEntry: module_name="inference_models.models.qwen3_5.qwen3_5_hf", class_name="Qwen35HF", ), + ("gemma-4-e2b-it", VLM_TASK, BackendType.HF): LazyClass( + module_name="inference_models.models.gemma4.gemma4_hf", + class_name="Gemma4HF", + ), + ("gemma-4-e4b-it", VLM_TASK, BackendType.HF): LazyClass( + module_name="inference_models.models.gemma4.gemma4_hf", + class_name="Gemma4HF", + ), + ("gemma-4-31b-it", VLM_TASK, BackendType.HF): LazyClass( + module_name="inference_models.models.gemma4.gemma4_hf", + class_name="Gemma4HF", + ), + ("gemma-4-26b-a4b-it", VLM_TASK, BackendType.HF): LazyClass( + module_name="inference_models.models.gemma4.gemma4_hf", + class_name="Gemma4HF", + ), ("florence-2", VLM_TASK, BackendType.HF): LazyClass( module_name="inference_models.models.florence2.florence2_hf", class_name="Florence2HF", diff --git a/inference_models/inference_models/models/gemma4/__init__.py b/inference_models/inference_models/models/gemma4/__init__.py new file mode 100644 index 0000000000..c2c565967a --- /dev/null +++ b/inference_models/inference_models/models/gemma4/__init__.py @@ -0,0 +1 @@ +# Gemma 4 multimodal (Hugging Face) implementations diff --git a/inference_models/inference_models/models/gemma4/gemma4_hf.py b/inference_models/inference_models/models/gemma4/gemma4_hf.py new file mode 100644 index 0000000000..1412758403 --- /dev/null +++ b/inference_models/inference_models/models/gemma4/gemma4_hf.py @@ -0,0 +1,386 @@ +import os +from threading import Lock +from typing import Any, Final, List, Optional, Tuple, Union + +import numpy as np +import torch +from PIL import Image +from peft import PeftModel +from transformers import AutoModelForMultimodalLM, AutoProcessor, BitsAndBytesConfig +from transformers.utils import is_flash_attn_2_available + +from inference_models.configuration import ( + DEFAULT_DEVICE, + INFERENCE_MODELS_GEMMA4_DEFAULT_DO_SAMPLE, + INFERENCE_MODELS_GEMMA4_DEFAULT_ENABLE_THINKING, + INFERENCE_MODELS_GEMMA4_DEFAULT_MAX_NEW_TOKENS, + INFERENCE_MODELS_GEMMA4_DEFAULT_SKIP_SPECIAL_TOKENS, + INFERENCE_MODELS_GEMMA4_DEFAULT_TEMPERATURE, + INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_K, + INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_P, +) +from inference_models.entities import ColorFormat +from inference_models.errors import InvalidModelInitParameterError +from inference_models.models.common.roboflow.model_packages import ( + InferenceConfig, + ResizeMode, + parse_inference_config, +) +from inference_models.models.common.roboflow.pre_processing import ( + pre_process_network_input, +) + +_GEMMA4_IMAGE_TOKEN_BUDGETS = frozenset({70, 140, 280, 560, 1120}) + +# --- BatchFeature / processor keys not accepted by ``Model.forward`` / ``generate`` --- + +# Per-image soft-token counts produced by the HF vision preprocessor when building +# multimodal prompts. Used to expand image placeholders in text; not a tensor argument +# to the transformer (Hugging Face ``transformers`` multimodal ``ProcessorMixin`` stack). +PROCESSOR_NUM_SOFT_TOKENS_PER_IMAGE_KEY: Final[str] = "num_soft_tokens_per_image" + +# Same role as ``PROCESSOR_NUM_SOFT_TOKENS_PER_IMAGE_KEY`` for video segments. +PROCESSOR_NUM_SOFT_TOKENS_PER_VIDEO_KEY: Final[str] = "num_soft_tokens_per_video" + +# Tokenizer output mapping token indices to source character spans (when requested). +# Never a model forward kwarg; strip if present so ``generate(**batch)`` does not fail. +TOKENIZER_OFFSET_MAPPING_KEY: Final[str] = "offset_mapping" + +# All keys above that we defensively remove before ``self._model.generate(**...)``. +BATCH_KEYS_TO_STRIP_BEFORE_GENERATE: Final[Tuple[str, ...]] = ( + PROCESSOR_NUM_SOFT_TOKENS_PER_IMAGE_KEY, + PROCESSOR_NUM_SOFT_TOKENS_PER_VIDEO_KEY, + TOKENIZER_OFFSET_MAPPING_KEY, +) + + +def _get_gemma4_attn_implementation(device: torch.device) -> str: + if is_flash_attn_2_available() and device.type == "cuda": + try: + import flash_attn # noqa: F401 + + major, _ = torch.cuda.get_device_capability(device=device) + if major >= 8: + return "flash_attention_2" + except ImportError: + pass + return "eager" + + +def _to_pil_rgb( + image: Union[np.ndarray, torch.Tensor], + input_color_format: Optional[ColorFormat], +) -> Image.Image: + if isinstance(image, torch.Tensor): + arr = image.detach().cpu().float().numpy() + if arr.ndim == 3 and arr.shape[0] in (1, 3): + arr = np.transpose(arr, (1, 2, 0)) + if arr.max() <= 1.0 + 1e-6: + arr = (arr * 255.0).clip(0, 255) + arr = arr.astype(np.uint8) + else: + arr = np.asarray(image) + if arr.dtype != np.uint8: + if np.issubdtype(arr.dtype, np.floating) and arr.max() <= 1.0 + 1e-6: + arr = (arr * 255.0).clip(0, 255).astype(np.uint8) + else: + arr = arr.astype(np.uint8) + if arr.ndim == 2: + return Image.fromarray(arr).convert("RGB") + if input_color_format == "bgr": + arr = arr[..., ::-1].copy() if arr.shape[-1] == 3 else arr + elif input_color_format is None and isinstance(image, np.ndarray): + arr = arr[..., ::-1].copy() if arr.shape[-1] == 3 else arr + return Image.fromarray(arr).convert("RGB") + + +class Gemma4HF: + """Hugging Face Gemma 4 multimodal (vision + text) instruction-tuned models.""" + + @classmethod + def from_pretrained( + cls, + model_name_or_path: str, + device: torch.device = DEFAULT_DEVICE, + trust_remote_code: bool = False, + local_files_only: bool = True, + quantization_config: Optional[BitsAndBytesConfig] = None, + disable_quantization: bool = False, + gemma_image_seq_length: Optional[int] = None, + **kwargs, + ) -> "Gemma4HF": + """Load a Gemma 4 checkpoint from a local directory (or cache path). + + Args: + model_name_or_path: Directory with model weights and processor files. + device: Torch device used for ``device_map`` and tensor placement. + trust_remote_code: Passed through to Hugging Face loaders. + local_files_only: If True, do not hit the network when resolving files. + quantization_config: Optional BitsAndBytes config; when None on CUDA, + a default 4-bit config may be applied unless ``disable_quantization``. + disable_quantization: When True, skip default 4-bit loading on CUDA. + gemma_image_seq_length: Overrides the processor's visual token budget for + each image (``processor.image_seq_length``). Gemma 4 uses this budget + alongside variable aspect ratios: higher values keep more visual detail + at higher compute cost; lower values speed up inference when fine detail + is not needed. Allowed values: ``70``, ``140``, ``280``, ``560``, + ``1120`` (see Hugging Face model cards). Typical guidance: prefer lower + budgets for classification, captioning, or many-frame / video-style + workloads; prefer higher budgets for OCR, documents, or small text. + + Returns: + An initialized :class:`Gemma4HF` instance. + """ + adapter_config_path = os.path.join(model_name_or_path, "adapter_config.json") + inference_config_path = os.path.join( + model_name_or_path, "inference_config.json" + ) + inference_config = None + if os.path.exists(inference_config_path): + inference_config = parse_inference_config( + config_path=inference_config_path, + allowed_resize_modes={ + ResizeMode.STRETCH_TO, + ResizeMode.LETTERBOX, + ResizeMode.CENTER_CROP, + ResizeMode.LETTERBOX_REFLECT_EDGES, + ResizeMode.FIT_LONGER_EDGE, + }, + ) + + if gemma_image_seq_length is not None and ( + gemma_image_seq_length not in _GEMMA4_IMAGE_TOKEN_BUDGETS + ): + raise InvalidModelInitParameterError( + message=( + f"While loading Gemma 4, `gemma_image_seq_length` was set to `{gemma_image_seq_length}` " + f"which is invalid. Supported visual token budgets: " + f"{sorted(_GEMMA4_IMAGE_TOKEN_BUDGETS)}." + ), + help_url="https://inference-models.roboflow.com/errors/model-loading/#invalidmodelinitparametererror", + ) + + if ( + quantization_config is None + and device.type == "cuda" + and not disable_quantization + ): + quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.bfloat16, + bnb_4bit_quant_type="nf4", + ) + + attn_implementation = _get_gemma4_attn_implementation(device) + + load_kw = dict( + dtype="auto", + device_map=device, + trust_remote_code=trust_remote_code, + local_files_only=local_files_only, + attn_implementation=attn_implementation, + ) + if quantization_config is not None and device.type == "cuda": + load_kw["quantization_config"] = quantization_config + + if os.path.exists(adapter_config_path): + base_model_path = os.path.join(model_name_or_path, "base") + base_model = AutoModelForMultimodalLM.from_pretrained( + base_model_path, + **load_kw, + ) + model = PeftModel.from_pretrained(base_model, model_name_or_path) + if quantization_config is None: + model = model.merge_and_unload() + model.to(device) + processor_path = ( + os.path.join(model_name_or_path, "base") + if os.path.isdir(os.path.join(model_name_or_path, "base")) + else model_name_or_path + ) + else: + model = AutoModelForMultimodalLM.from_pretrained( + model_name_or_path, + **load_kw, + ) + processor_path = model_name_or_path + + processor = AutoProcessor.from_pretrained( + processor_path, + trust_remote_code=trust_remote_code, + local_files_only=local_files_only, + ) + if gemma_image_seq_length is not None: + processor.image_seq_length = gemma_image_seq_length + + model.eval() + + return cls( + model=model, + processor=processor, + inference_config=inference_config, + device=device, + ) + + def __init__( + self, + model: torch.nn.Module, + processor: Any, + inference_config: Optional[InferenceConfig], + device: torch.device, + ): + self._model = model + self._processor = processor + self._inference_config = inference_config + self._device = device + self.default_system_prompt = ( + "You are Gemma 4, a helpful multimodal assistant. Answer clearly and accurately." + ) + self._lock = Lock() + + def prompt( + self, + images: Union[torch.Tensor, List[torch.Tensor], np.ndarray, List[np.ndarray]], + prompt: str = None, + input_color_format: ColorFormat = None, + max_new_tokens: int = INFERENCE_MODELS_GEMMA4_DEFAULT_MAX_NEW_TOKENS, + do_sample: bool = INFERENCE_MODELS_GEMMA4_DEFAULT_DO_SAMPLE, + skip_special_tokens: bool = INFERENCE_MODELS_GEMMA4_DEFAULT_SKIP_SPECIAL_TOKENS, + enable_thinking: bool = INFERENCE_MODELS_GEMMA4_DEFAULT_ENABLE_THINKING, + **kwargs, + ) -> List[str]: + inputs = self.pre_process_generation( + images=images, + prompt=prompt, + input_color_format=input_color_format, + enable_thinking=enable_thinking, + ) + generated_ids = self.generate( + inputs=inputs, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + **kwargs, + ) + return self.post_process_generation( + generated_ids=generated_ids, + skip_special_tokens=skip_special_tokens, + ) + + def pre_process_generation( + self, + images: Union[torch.Tensor, List[torch.Tensor], np.ndarray, List[np.ndarray]], + prompt: str = None, + input_color_format: ColorFormat = None, + image_size: Optional[Tuple[int, int]] = None, + enable_thinking: bool = INFERENCE_MODELS_GEMMA4_DEFAULT_ENABLE_THINKING, + **kwargs, + ): + if self._inference_config is None: + + def _collect_list() -> List[Union[np.ndarray, torch.Tensor]]: + if isinstance(images, torch.Tensor) and images.ndim == 4: + return [images[i] for i in range(images.shape[0])] + if isinstance(images, list): + return images + return [images] + + raw_list = _collect_list() + else: + processed = pre_process_network_input( + images=images, + image_pre_processing=self._inference_config.image_pre_processing, + network_input=self._inference_config.network_input, + target_device=self._device, + input_color_format=input_color_format, + image_size_wh=image_size, + )[0] + raw_list = [t.squeeze(0) for t in torch.split(processed, 1, dim=0)] + + pil_images = [_to_pil_rgb(img, input_color_format) for img in raw_list] + + if prompt is None: + prompt = "Describe what you see in this image." + system_prompt = self.default_system_prompt + else: + split_prompt = prompt.split("") + if len(split_prompt) == 1: + prompt = split_prompt[0] or "Describe what you see in this image." + system_prompt = self.default_system_prompt + else: + prompt = split_prompt[0] or "Describe what you see in this image." + system_prompt = split_prompt[1] or self.default_system_prompt + + user_content: List[dict] = [ + {"type": "image", "image": pil} for pil in pil_images + ] + user_content.append({"type": "text", "text": prompt}) + + conversation = [ + { + "role": "system", + "content": [{"type": "text", "text": system_prompt}], + }, + {"role": "user", "content": user_content}, + ] + + inputs = self._processor.apply_chat_template( + conversation, + tokenize=True, + return_dict=True, + return_tensors="pt", + add_generation_prompt=True, + enable_thinking=enable_thinking, + ) + return inputs.to(self._device) + + def generate( + self, + inputs, + max_new_tokens: int = INFERENCE_MODELS_GEMMA4_DEFAULT_MAX_NEW_TOKENS, + do_sample: bool = INFERENCE_MODELS_GEMMA4_DEFAULT_DO_SAMPLE, + **kwargs, + ) -> torch.Tensor: + batch = dict(inputs) + for _meta_key in BATCH_KEYS_TO_STRIP_BEFORE_GENERATE: + batch.pop(_meta_key, None) + input_len = batch["input_ids"].shape[-1] + + tok = self._processor.tokenizer + pad_id = getattr(tok, "pad_token_id", None) or tok.eos_token_id + gen_kw = { + **batch, + "max_new_tokens": max_new_tokens, + "do_sample": do_sample, + "pad_token_id": pad_id, + "eos_token_id": tok.eos_token_id, + } + if do_sample: + gen_kw.setdefault( + "temperature", + kwargs.get("temperature", INFERENCE_MODELS_GEMMA4_DEFAULT_TEMPERATURE), + ) + gen_kw.setdefault( + "top_p", + kwargs.get("top_p", INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_P), + ) + gen_kw.setdefault( + "top_k", + kwargs.get("top_k", INFERENCE_MODELS_GEMMA4_DEFAULT_TOP_K), + ) + + with self._lock, torch.inference_mode(): + generation = self._model.generate(**gen_kw) + + return generation[:, input_len:] + + def post_process_generation( + self, + generated_ids: torch.Tensor, + skip_special_tokens: bool = False, + **kwargs, + ) -> List[str]: + decoded = self._processor.tokenizer.batch_decode( + generated_ids, + skip_special_tokens=skip_special_tokens, + ) + return [text.strip() for text in decoded] diff --git a/inference_models/tests/unit_tests/models/auto_loaders/test_model_registry.py b/inference_models/tests/unit_tests/models/auto_loaders/test_model_registry.py index 33f47f5141..b6a3ff541f 100644 --- a/inference_models/tests/unit_tests/models/auto_loaders/test_model_registry.py +++ b/inference_models/tests/unit_tests/models/auto_loaders/test_model_registry.py @@ -206,3 +206,26 @@ def test_model_implementation_exists_when_known_model_feature_requested() -> Non # then assert result is True + + +@pytest.mark.parametrize( + "model_architecture", + ( + "gemma-4-e2b-it", + "gemma-4-e4b-it", + "gemma-4-31b-it", + "gemma-4-26b-a4b-it", + ), +) +def test_gemma4_variants_resolve_to_gemma4_hf(model_architecture: str) -> None: + assert model_implementation_exists( + model_architecture=model_architecture, + task_type="vlm", + backend=BackendType.HF, + ) + cls = resolve_model_class( + model_architecture=model_architecture, + task_type="vlm", + backend=BackendType.HF, + ) + assert cls.__name__ == "Gemma4HF" From 89c294c017c69f7b4a3ef262e0ac61d8a0e79051 Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Wed, 15 Apr 2026 16:38:34 +0200 Subject: [PATCH 5/8] Add example scripts and configuration for Gemma 4 model - Introduced a new example configuration file for Gemma 4 model. - Added a script to run Gemma 4 locally, demonstrating end-to-end inference with a sample image. - Included instructions for using the script with both hosted and local model setups. --- .../examples/gemma4/model_config.example.json | 5 + .../examples/gemma4/run_gemma4_local.py | 106 ++++++++++++++++++ 2 files changed, 111 insertions(+) create mode 100644 inference_models/examples/gemma4/model_config.example.json create mode 100644 inference_models/examples/gemma4/run_gemma4_local.py diff --git a/inference_models/examples/gemma4/model_config.example.json b/inference_models/examples/gemma4/model_config.example.json new file mode 100644 index 0000000000..4538c412e9 --- /dev/null +++ b/inference_models/examples/gemma4/model_config.example.json @@ -0,0 +1,5 @@ +{ + "model_architecture": "gemma-4-e2b-it", + "task_type": "vlm", + "backend_type": "hugging-face" +} diff --git a/inference_models/examples/gemma4/run_gemma4_local.py b/inference_models/examples/gemma4/run_gemma4_local.py new file mode 100644 index 0000000000..5f58f47588 --- /dev/null +++ b/inference_models/examples/gemma4/run_gemma4_local.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +"""End-to-end Gemma 4 example using ``inference_models.AutoModel`` (no CLI arguments). + +Downloads a public sample image, loads a hosted Gemma 4 checkpoint via Roboflow, and +asks a focused counting question. + +Run from the ``inference_models`` package root:: + + export ROBOFLOW_API_KEY=your_key + uv run python examples/gemma4/run_gemma4_local.py + +For offline use with a local Hugging Face snapshot, set ``GEMMA4_MODEL_PATH`` to a +directory that contains weights and ``model_config.json`` (see +``examples/gemma4/model_config.example.json``). An API key is not required in that mode. +""" + +from __future__ import annotations + +import io +import os +import sys + +import numpy as np +import requests +from PIL import Image + +from inference_models import AutoModel +from inference_models.configuration import DEFAULT_DEVICE + +# Same image used in repo docs (e.g. workflows benchmarks). +IMAGE_URL = "https://media.roboflow.com/inference/people-walking.jpg" + +# Roboflow registry id (must match a registered Gemma 4 package). +DEFAULT_MODEL_ID = "gemma-4-e2b-it" + +SYSTEM_PROMPT = ( + "You are a precise vision assistant. When asked about people or objects in a scene, " + "base your answer only on what is clearly visible. If you are uncertain, say so. " + "For counting questions, give a single best estimate and briefly note any ambiguity " + "(e.g. partially occluded figures or unclear backpacks)." +) + +USER_PROMPT = ( + "How many people in this image are clearly wearing a backpack? " + "Answer with a number first, then one short sentence explaining what you counted." +) + + +def _build_prompt(user: str, system: str) -> str: + return f"{user}{system}" + + +def _load_image_rgb(url: str) -> np.ndarray: + response = requests.get(url, timeout=60) + response.raise_for_status() + image = Image.open(io.BytesIO(response.content)).convert("RGB") + return np.array(image) + + +def main() -> None: + local_path = os.environ.get("GEMMA4_MODEL_PATH") + api_key = os.environ.get("ROBOFLOW_API_KEY") + + if local_path: + load_target = local_path + load_kw: dict = { + "device": DEFAULT_DEVICE, + "backend": "hugging-face", + } + print(f"Loading local package from {load_target!r} …") + else: + if not api_key: + print( + "Missing ROBOFLOW_API_KEY. Set it to load the hosted model, or set " + "GEMMA4_MODEL_PATH to a local directory with model_config.json and HF weights.", + file=sys.stderr, + ) + sys.exit(1) + load_target = os.environ.get("GEMMA4_MODEL_ID", DEFAULT_MODEL_ID) + load_kw = { + "api_key": api_key, + "device": DEFAULT_DEVICE, + "backend": "hugging-face", + } + print(f"Loading hosted model {load_target!r} …") + + model = AutoModel.from_pretrained(load_target, **load_kw) + + print(f"Fetching image {IMAGE_URL!r} …") + image_rgb = _load_image_rgb(IMAGE_URL) + prompt = _build_prompt(USER_PROMPT, SYSTEM_PROMPT) + + print("Running inference …") + outputs = model.prompt( + images=image_rgb, + prompt=prompt, + input_color_format="rgb", + max_new_tokens=256, + do_sample=False, + ) + print("---") + print(outputs[0] if outputs else outputs) + + +if __name__ == "__main__": + main() From 256006609e8a7d6eac41dab0582810d9ae47d0cc Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Wed, 15 Apr 2026 22:59:27 +0200 Subject: [PATCH 6/8] Add new example scripts for Gemma 4 model - Created `__init__.py` files for the examples directory and the Gemma 4 module. - Added `count_backpacks.py` script demonstrating end-to-end inference with the Gemma 4 model using a sample image. - Included prompts and instructions for running the example locally. --- inference_models/examples/__init__.py | 0 inference_models/examples/gemma4/__init__.py | 0 ...run_gemma4_local.py => count_backpacks.py} | 38 ++++--------------- 3 files changed, 7 insertions(+), 31 deletions(-) create mode 100644 inference_models/examples/__init__.py create mode 100644 inference_models/examples/gemma4/__init__.py rename inference_models/examples/gemma4/{run_gemma4_local.py => count_backpacks.py} (62%) diff --git a/inference_models/examples/__init__.py b/inference_models/examples/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/inference_models/examples/gemma4/__init__.py b/inference_models/examples/gemma4/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/inference_models/examples/gemma4/run_gemma4_local.py b/inference_models/examples/gemma4/count_backpacks.py similarity index 62% rename from inference_models/examples/gemma4/run_gemma4_local.py rename to inference_models/examples/gemma4/count_backpacks.py index 5f58f47588..730db011f5 100644 --- a/inference_models/examples/gemma4/run_gemma4_local.py +++ b/inference_models/examples/gemma4/count_backpacks.py @@ -6,12 +6,9 @@ Run from the ``inference_models`` package root:: - export ROBOFLOW_API_KEY=your_key uv run python examples/gemma4/run_gemma4_local.py -For offline use with a local Hugging Face snapshot, set ``GEMMA4_MODEL_PATH`` to a -directory that contains weights and ``model_config.json`` (see -``examples/gemma4/model_config.example.json``). An API key is not required in that mode. +Optional: set ``GEMMA4_MODEL_ID`` to override the default Roboflow registry id. """ from __future__ import annotations @@ -58,33 +55,12 @@ def _load_image_rgb(url: str) -> np.ndarray: def main() -> None: - local_path = os.environ.get("GEMMA4_MODEL_PATH") - api_key = os.environ.get("ROBOFLOW_API_KEY") - - if local_path: - load_target = local_path - load_kw: dict = { - "device": DEFAULT_DEVICE, - "backend": "hugging-face", - } - print(f"Loading local package from {load_target!r} …") - else: - if not api_key: - print( - "Missing ROBOFLOW_API_KEY. Set it to load the hosted model, or set " - "GEMMA4_MODEL_PATH to a local directory with model_config.json and HF weights.", - file=sys.stderr, - ) - sys.exit(1) - load_target = os.environ.get("GEMMA4_MODEL_ID", DEFAULT_MODEL_ID) - load_kw = { - "api_key": api_key, - "device": DEFAULT_DEVICE, - "backend": "hugging-face", - } - print(f"Loading hosted model {load_target!r} …") - - model = AutoModel.from_pretrained(load_target, **load_kw) + load_kw = { + "device": DEFAULT_DEVICE, + "backend": "hugging-face", + } + print(f"Loading hosted model {DEFAULT_MODEL_ID!r} …") + model = AutoModel.from_pretrained(DEFAULT_MODEL_ID, **load_kw) print(f"Fetching image {IMAGE_URL!r} …") image_rgb = _load_image_rgb(IMAGE_URL) From d510ac9afdf4166e4e9f6d23f1577014d2206401 Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Thu, 16 Apr 2026 01:09:01 +0200 Subject: [PATCH 7/8] Bump inference models version to 0.25.0rc2 --- inference_models/pyproject.toml | 2 +- inference_models/uv.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/inference_models/pyproject.toml b/inference_models/pyproject.toml index 28370737f0..4523b7d01d 100644 --- a/inference_models/pyproject.toml +++ b/inference_models/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "inference-models" -version = "0.25.0rc1" +version = "0.25.0rc2" description = "The new inference engine for Computer Vision models" readme = "README.md" requires-python = ">=3.10,<3.13" diff --git a/inference_models/uv.lock b/inference_models/uv.lock index 094388b48e..d159ac7949 100644 --- a/inference_models/uv.lock +++ b/inference_models/uv.lock @@ -917,7 +917,7 @@ wheels = [ [[package]] name = "inference-models" -version = "0.25.0rc1" +version = "0.25.0rc2" source = { virtual = "." } dependencies = [ { name = "accelerate" }, From 41f9ed2f4410a6ca4dcd2501bf7e84f5991644cd Mon Sep 17 00:00:00 2001 From: Damian Kosowski Date: Thu, 16 Apr 2026 01:20:26 +0200 Subject: [PATCH 8/8] Bump inference models version to 0.25.0rc2 across all requirements files --- requirements/requirements.cpu.txt | 2 +- requirements/requirements.gpu.txt | 2 +- requirements/requirements.jetson.txt | 2 +- requirements/requirements.vino.txt | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements/requirements.cpu.txt b/requirements/requirements.cpu.txt index 472a895fd2..a56a10661d 100644 --- a/requirements/requirements.cpu.txt +++ b/requirements/requirements.cpu.txt @@ -1,3 +1,3 @@ onnxruntime>=1.15.1,<1.22.0 nvidia-ml-py<13.0.0 -inference-models[torch-cpu,onnx-cpu]~=0.25.0rc1 # keep in sync between requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file +inference-models[torch-cpu,onnx-cpu]~=0.25.0rc2 # keep in sync between requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file diff --git a/requirements/requirements.gpu.txt b/requirements/requirements.gpu.txt index 5dc73f6aa4..4fa38d2de1 100644 --- a/requirements/requirements.gpu.txt +++ b/requirements/requirements.gpu.txt @@ -1,2 +1,2 @@ onnxruntime-gpu>=1.15.1,<1.22.0 -inference-models[torch-cu124,onnx-cu12]~=0.25.0rc1 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt +inference-models[torch-cu124,onnx-cu12]~=0.25.0rc2 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt diff --git a/requirements/requirements.jetson.txt b/requirements/requirements.jetson.txt index 8059c5f9e3..d78b775771 100644 --- a/requirements/requirements.jetson.txt +++ b/requirements/requirements.jetson.txt @@ -1,4 +1,4 @@ pypdfium2>=4.11.0,<5.0.0 jupyterlab>=4.3.0,<5.0.0 PyYAML~=6.0.0 -inference-models~=0.25.0rc1 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file +inference-models~=0.25.0rc2 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file diff --git a/requirements/requirements.vino.txt b/requirements/requirements.vino.txt index 3e96752113..93ceab7ce3 100644 --- a/requirements/requirements.vino.txt +++ b/requirements/requirements.vino.txt @@ -1,2 +1,2 @@ onnxruntime-openvino>=1.15.0,<1.22.0 -inference-models[torch-cpu]~=0.25.0rc1 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file +inference-models[torch-cpu]~=0.25.0rc2 # keep in sync between requirements.jetson requirements.gpu.txt, requirements.cpu.txt, requirements.vino.txt \ No newline at end of file