Imrove CI/CL, add more unit tests, improve documentation

.gitignore

@@ -1,3 +1,13 @@
+# Archive and draft directories
+_arxiv/
+drafts/
+
+# Backup files
+*.bak
+*.old
+*_old.*
+*_backup.*
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -2,23 +2,18 @@
 
 Neural network alignment analysis and intelligent pruning framework.
 
----
-
 ## Overview
 
 This framework provides tools for analyzing neural networks through information-theoretic metrics and performing redundancy-aware pruning and quantization.
 
-**Key capabilities:**
-- Alignment metrics (Rayleigh Quotient, class-conditioned RQ, mutual information)
-- Information-theoretic analysis (redundancy, synergy, PID)
-- Pruning strategies (16 methods including magnitude, gradient-based, redundancy-aware)
-- Quantization (INT8, INT4, mixed-precision with alignment-guided bit allocation)
-- Architecture support (MLPs, CNNs, Transformers, LLMs)
-- Data loading (vision and text datasets)
-- Evaluation (classification accuracy, perplexity for language models)
-- Visualization (plots and reports)
-
----
+Key capabilities:
+- Alignment metrics for analyzing neural network weight-input relationships
+- Information-theoretic analysis tools
+- Pruning strategies with multiple scoring methods
+- Quantization with alignment-guided precision selection
+- Architecture support for MLPs, CNNs, Transformers, and LLMs
+- Data loading for vision and text datasets
+- Evaluation and visualization tools
 
 ## Installation
 
@@ -30,96 +25,55 @@ conda activate alignment
 pip install -e .
 ```
 
-Details: [docs/installation.md](docs/installation.md)
-
----
+See [docs/installation.md](docs/installation.md) for details.
 
 ## Usage
 
-### Command Line
+Run experiments using configuration files:
 
 ```bash
 python scripts/run_experiment.py --config configs/examples/mnist_basic.yaml
-python scripts/run_experiment.py --config configs/examples/resnet_pruning.yaml
-python scripts/run_experiment.py --config configs/examples/llama3_scoring.yaml
-python scripts/run_experiment.py --config configs/examples/llama3_quantization.yaml
-```
-
-### Python API
-
-```python
-from alignment import ModelWrapper, get_metric
-
-wrapper = ModelWrapper(model)
-rq = get_metric('rayleigh_quotient')
-
-outputs, acts = wrapper.forward_with_activations(inputs)
-weights = wrapper.get_layer_weights()
-
-scores = rq.compute(acts['layer_input'], weights['layer'])
 ```
 
-### Quantization
-
-```python
-from alignment.quantization import quantize_model, find_optimal_bit_allocation
+The framework supports:
+- Training networks from scratch or loading pre-trained models
+- Computing alignment and information-theoretic scores
+- Applying pruning with different strategies and distributions
+- Quantization with various precision settings
 
-# INT8 quantization
-results = quantize_model(model, precision='int8')
+Example configurations are available in `configs/examples/`:
+- `mnist_basic.yaml` - Basic alignment analysis on MNIST
+- `resnet_pruning.yaml` - Pruning ResNet on CIFAR-10
+- `llama3_scoring.yaml` - Computing scores for LLaMA models
+- `llama3_pruning.yaml` - Pruning transformer networks
 
-# Mixed precision using alignment scores
-layer_importance = {layer: rq_scores[layer].mean() for layer in layers}
-bit_allocation = find_optimal_bit_allocation(model, layer_importance, target_avg_bits=6.0)
-```
-
----
+See `configs/template.yaml` for all available configuration options.
 
 ## Documentation
 
-- [Installation](docs/installation.md) - Setup and dependencies
-- [Usage Guide](docs/usage.md) - Running experiments
-- [User Guide](docs/user_guide.md) - Complete documentation
-- [API Reference](docs/api_reference.md) - API details
+- [Usage Guide](docs/usage.md) - Running experiments with configs
+- [User Guide](docs/user_guide.md) - Detailed framework documentation
+- [API Reference](docs/api_reference.md) - API documentation
 - [Quick Reference](docs/quick_reference.md) - Code examples
-- [Changelog](docs/changelog.md) - Version history
-
-Full documentation: [docs/README.md](docs/README.md)
-
----
-
-## Configuration
 
-Experiments are configured via YAML files. See `configs/template.yaml` for all parameters.
+## Examples
 
-Available examples in `configs/examples/`:
-- mnist_basic.yaml
-- resnet_pruning.yaml
-- llama3_scoring.yaml
-- llama3_pruning.yaml
-- llama3_quantization.yaml
-
----
-
-## Code Examples
-
-Python examples in `examples/` directory:
+Python scripts demonstrating framework capabilities:
 
 ```bash
 python examples/07_mnist_intelligent_pruning.py
 python examples/08_llama_ffn_pruning.py
 python examples/09_attention_neuron_vs_head_pruning.py
 ```
 
----
+
 
 ## Testing
 
 ```bash
 pytest tests/
 ```
 
----
-
 ## License
 
 See LICENSE file.