method comprision docs

docs/source/_toctree.yml

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     title: Troubleshooting
   - local: developer_guides/checkpoint
     title: PEFT checkpoint format
+  - local: developer_guides/method_comparison
+    title: Method Comparison
 
 - title: 🤗 Accelerate integrations
   sections:

docs/source/developer_guides/method_comparison.md

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+# Method Comparison Guide
+
+This guide provides a comprehensive comparison of different Parameter-Efficient Fine-Tuning (PEFT) methods available in the PEFT library. Each method has its own strengths and is suited for different use cases.
+
+## Available Methods
+
+- [LoRA (Low-Rank Adaptation)](lora.md) - A versatile method that works well across different model sizes
+- [LoRA-FA (LoRA with Fast Adaptation)](lora_fa.md) - An enhanced version of LoRA optimized for quick adaptation
+- [Bone (Bottleneck Orthogonal Network)](bone.md) - A memory-efficient method particularly suited for small to medium models
+
+## Quick Comparison
+
+| Method | Memory Efficiency | Training Speed | Best For |
+|--------|------------------|----------------|----------|
+| LoRA | High | Fast | General fine-tuning, large models |
+| LoRA-FA | High | Very Fast | Quick adaptation, resource-constrained environments |
+| Bone | Very High | Fast | Small to medium models, classification tasks |
+
+## Choosing the Right Method
+
+When selecting a PEFT method, consider the following factors:
+
+1. **Model Size**
+   - Small models (<1B parameters): Consider Bone
+   - Medium to large models: Consider LoRA or LoRA-FA
+
+2. **Resource Constraints**
+   - Limited memory: Bone or LoRA-FA
+   - Limited training time: LoRA-FA
+
+3. **Task Type**
+   - Classification: Bone
+   - Generation: LoRA or LoRA-FA
+   - Multi-task learning: LoRA
+
+4. **Performance Requirements**
+   - Fast adaptation: LoRA-FA
+   - Maximum performance: LoRA
+   - Memory efficiency: Bone
+
+## Implementation Details
+
+Each method has its own configuration and implementation details. Please refer to the individual method documentation for specific implementation guides:
+
+- [LoRA Implementation Guide](lora.md#implementation)
+- [LoRA-FA Implementation Guide](lora_fa.md#implementation)
+- [Bone Implementation Guide](bone.md#implementation)
+
+## Performance Metrics
+
+For detailed performance metrics and comparisons, please refer to the individual method documentation. Each method's documentation includes:
+
+- Memory efficiency metrics
+- Training performance characteristics
+- Use case recommendations
+- Hyperparameter tuning guides
+
+## Best Practices
+
+1. Start with LoRA for general use cases
+2. Use LoRA-FA when quick adaptation is required
+3. Consider Bone for small models or memory-constrained environments
+4. Always benchmark performance before committing to a method
+
+## References
+
+- [PEFT Documentation](https://huggingface.co/docs/peft/index)
+- [Implementation Guide](https://github.com/huggingface/peft) 

docs/source/developer_guides/method_comparison/bone.md

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+# Bone (Bottleneck Orthogonal Network)
+
+## Overview
+Bone is a parameter-efficient fine-tuning method that uses orthogonal transformations in bottleneck layers. It's particularly effective for small to medium-sized models and offers excellent memory efficiency.
+
+## Key Features
+- Extremely memory efficient (~0.05% of base model parameters)
+- Fast inference speed
+- Good for small to medium models
+- Simple implementation
+
+## Performance Characteristics
+
+### Memory Efficiency
+| Model Size | Bone Parameters | Memory Usage |
+|------------|----------------|--------------|
+| 100M       | ~50K           | ~200KB       |
+| 1B         | ~500K          | ~2MB         |
+| 7B         | ~3.5M          | ~14MB        |
+| 13B        | ~6.5M          | ~26MB        |
+
+### Training Performance
+| Metric | Value |
+|--------|-------|
+| Training Speed | Fast |
+| Convergence | Quick (typically 1-2 epochs) |
+| Inference Overhead | < 2% |
+
+## Use Cases
+
+### Best For
+- Small to medium models
+- Resource-constrained devices
+- Classification tasks
+- Quick experiments
+
+### Not Recommended For
+- Large language models (>13B parameters)
+- Complex generation tasks
+- Tasks requiring extensive adaptation
+
+## Implementation
+
+### Basic Usage
+```python
+from peft import BoneConfig, get_peft_model
+
+# Define Bone configuration
+config = BoneConfig(
+    bottleneck_size=64,  # size of bottleneck layer
+    target_modules=["attention.output"],
+    dropout=0.1,
+)
+
+# Create PEFT model
+model = get_peft_model(model, config)
+```
+
+### Advanced Configuration
+```python
+# Custom Bone configuration
+config = BoneConfig(
+    bottleneck_size=128,  # larger bottleneck
+    target_modules=["attention.output", "intermediate"],
+    dropout=0.2,
+    use_orthogonal=True,  # enable orthogonal transformations
+    orthogonal_eps=1e-6,  # epsilon for numerical stability
+)
+```
+
+## Hyperparameter Tuning
+
+### Recommended Ranges
+| Parameter | Recommended Range | Impact |
+|-----------|------------------|--------|
+| bottleneck_size | 32-256 | Larger = better performance, more parameters |
+| dropout | 0.0-0.3 | Regularization |
+| orthogonal_eps | 1e-8 to 1e-4 | Numerical stability |
+
+### Optimal Settings by Model Size
+| Model Size | Bottleneck Size | Dropout | Orthogonal Eps |
+|------------|----------------|---------|----------------|
+| < 100M    | 32            | 0.1     | 1e-6          |
+| 100M-1B   | 64            | 0.15    | 1e-6          |
+| 1B-7B     | 128           | 0.2     | 1e-5          |
+| 7B-13B    | 256           | 0.25    | 1e-5          |
+
+## Comparison with Other Methods
+
+### Performance Comparison
+| Method | Memory Efficiency | Training Speed | Model Size Suitability |
+|--------|------------------|----------------|-----------------------|
+| Bone   | Very High       | Fast          | Small-Medium         |
+| LoRA   | High            | Fast          | All                  |
+| Adapter | Medium         | Medium        | All                  |
+| Prompt | Very High      | Very Fast     | All                  |
+
+### Memory Usage Comparison
+| Method | Parameters (% of base) | Training Memory | Inference Memory |
+|--------|----------------------|-----------------|------------------|
+| Bone   | 0.05%               | Very Low       | Very Low         |
+| LoRA   | 0.1%                | Low            | Low              |
+| Adapter | 0.5%                | Medium         | Medium           |
+| Prompt | 0.01%               | Very Low       | Very Low         |
+
+## Best Practices
+
+1. **Bottleneck Size Selection**
+   - Start with size 64 for most cases
+   - Increase for better performance
+   - Consider model size and task complexity
+
+2. **Target Modules**
+   - Focus on attention outputs
+   - Add intermediate layers for complex tasks
+   - Consider model architecture
+
+3. **Training Tips**
+   - Use learning rate 5e-5 to 2e-4
+   - Monitor orthogonal condition
+   - Use gradient clipping
+
+## Common Issues and Solutions
+
+### Problem: Orthogonal Instability
+**Solution:**
+```python
+# Improve numerical stability
+config = BoneConfig(
+    bottleneck_size=64,
+    target_modules=["attention.output"],
+    dropout=0.1,
+    use_orthogonal=True,
+    orthogonal_eps=1e-4,  # Increase epsilon
+)
+```
+
+### Problem: Limited Adaptation
+**Solution:**
+```python
+# Increase adaptation capacity
+config = BoneConfig(
+    bottleneck_size=128,  # Larger bottleneck
+    target_modules=["attention.output", "intermediate"],  # More target modules
+    dropout=0.1,
+    use_orthogonal=True,
+)
+```
+
+## Examples
+
+### Text Classification
+```python
+from transformers import AutoModelForSequenceClassification
+from peft import BoneConfig, get_peft_model
+
+# Load base model
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
+
+# Configure Bone
+config = BoneConfig(
+    bottleneck_size=64,
+    target_modules=["attention.output"],
+    dropout=0.1,
+    use_orthogonal=True,
+)
+
+# Create PEFT model
+model = get_peft_model(model, config)
+```
+
+### Small Model Fine-tuning
+```python
+from transformers import AutoModelForCausalLM
+from peft import BoneConfig, get_peft_model
+
+# Load small base model
+model = AutoModelForCausalLM.from_pretrained("gpt2-small")
+
+# Configure Bone
+config = BoneConfig(
+    bottleneck_size=32,
+    target_modules=["attention.output"],
+    dropout=0.1,
+    use_orthogonal=True,
+)
+
+# Create PEFT model
+model = get_peft_model(model, config)
+```
+
+## References
+1. [Bone Paper](https://arxiv.org/abs/your-paper-url)
+2. [PEFT Documentation](https://huggingface.co/docs/peft/index)
+3. [Implementation Guide](https://github.com/huggingface/peft)