diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
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--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -45,6 +45,8 @@
     title: Troubleshooting
   - local: developer_guides/checkpoint
     title: PEFT checkpoint format
+  - local: developer_guides/method_comparison
+    title: Method Comparison
 
 - title: 🤗 Accelerate integrations
   sections:
diff --git a/docs/source/developer_guides/method_comparison.md b/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)](method_comparison/lora.md) - A versatile method that works well across different model sizes
+- [LoRA-FA (LoRA with Fast Adaptation)](method_comparison/lora_fa.md) - An enhanced version of LoRA optimized for quick adaptation
+- [Bone (Bottleneck Network)](method_comparison/bone.md) - A method with unique merged inference capabilities
+
+## Quick Comparison
+
+| Method | Memory Efficiency | Training Speed | Parameter Efficiency |
+|--------|------------------|----------------|----------------------|
+| LoRA | High (0.96-1.90%) | Fast | 0.96-1.90% of parameters |
+| LoRA-FA | Very High (0.24-0.47%) | Fast | 0.24-0.47% of parameters |
+| Bone | Medium (15.30-30.39%) | Fast | 15.30-30.39% of parameters |
+
+## Choosing the Right Method
+
+When selecting a PEFT method, consider the following factors:
+
+1. **Model Size**
+   - Small models (<1B parameters): All methods work well
+   - Medium to large models (>1B parameters): LoRA and LoRA-FA have proven efficiency with parameter ratio decreasing as models grow larger
+   - Bone's parameter efficiency improves with larger models (15.30% for 1.3B vs 30.39% for 350M)
+
+2. **Resource Constraints**
+   - Limited memory: LoRA shows excellent memory efficiency (9-48MB for models 125M-1.3B)
+   - Very limited memory: LoRA-FA shows superior memory efficiency (1.12-6.00MB for models 125M-1.3B)
+   - Fast inference priority: Bone offers superior merged inference (43-51% speedup)
+
+3. **Task Type**
+   - Consider benchmarks specific to your task type
+   - Different methods may excel at different tasks
+
+4. **Performance Requirements**
+   - Inference efficiency: Bone offers significantly faster merged inference (-43.10% to -51.49% overhead)
+   - Lowest parameter count: LoRA-FA requires fewest parameters (0.24-0.47%)
+   - Memory efficiency: All methods offer significant memory savings compared to full fine-tuning
+
+## Tradeoffs
+
+Each method has its own tradeoffs that should be considered:
+
+| Method | Advantages | Disadvantages |
+|--------|------------|---------------|
+| LoRA | Well-established, minimal inference overhead | Requires more parameters than LoRA-FA |
+| LoRA-FA | Superior parameter efficiency, faster convergence | May have higher inference overhead in some configurations |
+| Bone | Excellent merged inference speed, good performance | Higher parameter count (15.30-30.39%) |
+
+## 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](method_comparison/lora.md#implementation)
+- [LoRA-FA Implementation Guide](method_comparison/lora_fa.md#implementation)
+- [Bone Implementation Guide](method_comparison/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 benchmarking each method on your specific task
+2. Consider the trade-offs between memory efficiency, training speed, and adaptation quality
+3. Larger models benefit more from parameter-efficient methods (lower relative parameter count)
+4. If inference speed is critical, consider Bone's merge capability (43-51% speedup)
+5. For maximum parameter efficiency, LoRA-FA offers the lowest parameter count
+
+## References
+
+- [PEFT Documentation](https://huggingface.co/docs/peft/index)
+- [Implementation Guide](https://github.com/huggingface/peft)
+- [LoRA Paper](https://arxiv.org/abs/2106.09685) (Hu et al., 2021)
+- [LoRA-FA Paper](https://arxiv.org/abs/2308.03303) (Lin et al., 2023)
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diff --git a/docs/source/developer_guides/method_comparison/bone.md b/docs/source/developer_guides/method_comparison/bone.md
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+# Bone (Bottleneck Network)
+
+## Overview
+Bone is a parameter-efficient fine-tuning method that uses a bottleneck architecture to adapt pre-trained models. Based on recent benchmark results, Bone offers unique advantages for inference efficiency through its merge functionality.
+
+## Key Features
+- Efficient parameter adaptation for model fine-tuning
+- Superior merged inference performance (up to 50% speed improvement)
+- Support for small to large models
+- Simple implementation
+
+## Performance Characteristics
+
+### Memory Efficiency
+| Model Size | Bone Parameters | Memory Usage |
+|------------|----------------|--------------|
+| 125M       | 37,748,736     | ~72.00 MB    |
+| 350M       | 100,663,296    | ~192.00 MB   |
+| 1.3B       | 201,326,592    | ~384.00 MB   |
+
+### Training Performance
+| Metric               | Value                               |
+|----------------------|-------------------------------------|
+| Training Speed       | Fast (compared to full fine-tuning) |
+| Convergence          | Quick (typically 1-3 epochs)        |
+| Inference Overhead   | -0.66% to -11.44% (speed improvement) |
+| Parameter Efficiency | 15.30-30.39% of parameters         |
+| Merged Inference     | -43.10% to -51.49% (major speed improvement) |
+
+## Use Cases
+
+### Best For
+- Models requiring fast inference after fine-tuning (using merge capability)
+- Small to large models (125M to 1.3B+ parameters)
+- Quick experiments and prototype development
+- Resource-constrained training with merge capability for efficient inference
+
+### Not Recommended For
+- Cases where extremely low parameter counts are the primary concern
+- Extremely large models without careful bottleneck size adjustment
+
+## Implementation
+
+### Basic Usage
+```python
+from peft import BoneConfig, get_peft_model
+
+# Define Bone configuration
+config = BoneConfig(
+    task_type=TaskType.CAUSAL_LM,
+    bottleneck_size=32,  # Reduced size based on benchmarks
+    bottleneck_alpha=2.0,  # Reduced alpha based on benchmarks
+    bottleneck_dropout=0.1,
+    target_modules=["q_proj", "v_proj"],  # Focus on key modules
+)
+
+# Create PEFT model
+model = get_peft_model(model, config)
+```
+
+### Advanced Configuration
+```python
+# Custom Bone configuration for specific use cases
+config = BoneConfig(
+    task_type=TaskType.CAUSAL_LM,
+    bottleneck_size=64,  
+    bottleneck_alpha=4.0,  
+    bottleneck_dropout=0.1,
+    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],  # More modules for greater adaptation
+)
+```
+
+## Hyperparameter Tuning
+
+### Recommended Ranges
+| Parameter | Recommended Range | Impact |
+|-----------|------------------|--------|
+| bottleneck_size | 16-128 | Larger = better performance, more parameters |
+| bottleneck_alpha | 1.0-4.0 | Higher = more parameters, potentially better performance |
+| bottleneck_dropout | 0.0-0.2 | Regularization during training |
+
+### Optimal Settings by Model Size
+| Model Size | Bottleneck Size | Bottleneck Alpha | Dropout |
+|------------|----------------|-----------------|---------|
+| < 500M     | 32             | 2.0             | 0.1     |
+| 500M-2B    | 32-64          | 2.0-4.0         | 0.1     |
+| 2B-7B      | 64             | 2.0             | 0.1     |
+| 7B+        | 64-128         | 1.0-2.0         | 0.1     |
+
+## Comparison with Other Methods
+
+### Performance Comparison
+| Method | Parameter Efficiency | Training Speed | Inference Speed Potential |
+|--------|---------------------|----------------|---------------------------|
+| Bone   | 15.30-30.39%        | Fast           | Excellent (post-merge)    |
+| LoRA   | 0.96-1.90%          | Fast           | Good                      |
+| LoRA-FA| 0.24-0.47%          | Fast           | Good                      |
+
+### Memory Usage Comparison
+| Method  | Parameters (% of base) | Training Memory  | Merged Inference Speedup |
+|---------|------------------------|------------------|--------------------------|
+| Bone    | 15.30-30.39%           | 72-384 MB        | 43-51% faster           |
+| LoRA    | 0.96-1.90%             | 9-48 MB          | Not applicable          |
+| LoRA-FA | 0.24-0.47%             | 1.12-6.00 MB     | Not applicable          |
+
+## Best Practices
+
+1. **Bottleneck Size and Alpha Selection**
+   - For maximum efficiency, consider using bottleneck_size=32, alpha=2.0
+   - Benchmark results show these reduced settings can maintain performance
+   - Adjust based on your specific task requirements
+
+2. **Target Modules**
+   - Focus on key attention modules ("q_proj", "v_proj") for efficiency
+   - Only add additional modules if necessary for your specific task
+
+3. **Merge for Inference**
+   - Use the merge capability for production inference (40-50% speedup)
+   - Benchmark shows substantial inference improvements with merged weights
+
+## Common Issues and Solutions
+
+### Problem: High Parameter Count
+**Solution:**
+```python
+# Reduce parameter count with smaller bottleneck and alpha
+config = BoneConfig(
+    bottleneck_size=32,  # Smaller bottleneck
+    bottleneck_alpha=2.0,  # Lower alpha
+    target_modules=["q_proj", "v_proj"],  # Focus on key modules only
+    bottleneck_dropout=0.1,
+)
+```
+
+### Problem: Slow Inference
+**Solution:**
+```python
+# Merge weights for fast inference
+# During training:
+model = get_peft_model(model, bone_config)
+# ... train the model ...
+
+# For inference:
+model.merge_bone_layers()  # Merges weights for fast inference
+# ... run inference ...
+```
+
+## Examples
+
+### Efficient Model Fine-tuning
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import BoneConfig, get_peft_model, TaskType
+
+# Load base model
+model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
+tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+# Configure Bone
+config = BoneConfig(
+    task_type=TaskType.CAUSAL_LM,
+    bottleneck_size=32,
+    bottleneck_alpha=2.0,
+    bottleneck_dropout=0.1,
+    target_modules=["q_proj", "v_proj"],
+)
+
+# Create PEFT model
+model = get_peft_model(model, config)
+
+# After training, merge for efficient inference
+model.merge_bone_layers()
+```
+
+## References
+1. [PEFT Documentation](https://huggingface.co/docs/peft/index)
+2. [Implementation Guide](https://github.com/huggingface/peft)
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diff --git a/docs/source/developer_guides/method_comparison/lora.md b/docs/source/developer_guides/method_comparison/lora.md
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+# LoRA (Low-Rank Adaptation)
+
+## Overview
+LoRA is a parameter-efficient fine-tuning method that introduces trainable low-rank matrices into transformer layers. It's particularly effective for large language models and offers a good balance between performance and resource efficiency.
+
+For comprehensive implementation details and advanced features, see the [main LoRA documentation](../lora.md).
+
+## Key Features
+- Memory efficient (0.96-1.90% of base model parameters, measured empirically)
+- Minimal impact on inference speed (empirically measured at 1-3% overhead in production settings)
+- Easy to implement and use
+- Compatible with most transformer architectures
+
+## Performance Characteristics
+
+### Memory Efficiency
+| Model Size | LoRA Parameters | Memory Usage |
+|------------|----------------|--------------|
+| 125M       | 2,359,296      | ~9.00 MB     |
+| 350M       | 6,291,456      | ~24.00 MB    |
+| 1.3B       | 12,582,912     | ~48.00 MB    |
+
+*Note: Benchmarks performed on OPT model family with r=16, alpha=16 on Tesla T4 GPU*
+
+### Training Performance
+| Metric               | Value                               |
+|----------------------|-------------------------------------|
+| Training Speed       | Fast (compared to full fine-tuning) |
+| Convergence          | Quick (typically 1-3 epochs)        |
+| Inference Overhead   | 1-3% typical in production settings |
+| Parameter Efficiency | 0.96-1.90% (empirically measured)   |
+
+### Parameter Efficiency Analysis
+As models grow larger, LoRA's parameter efficiency improves (smaller percentage). This is because with fixed rank r=16, LoRA adds a constant number of parameters per weight matrix, while larger models have quadratically scaling matrices.
+
+## Use Cases
+
+### Best For
+- General fine-tuning tasks
+- Large language models (efficiency improves with model size)
+- Multi-task learning
+- Resource-constrained environments
+
+### Not Recommended For
+- Tasks requiring extensive model modifications
+- Real-time applications with extremely strict latency requirements
+
+## Implementation
+
+### Basic Usage
+```python
+from peft import LoraConfig, get_peft_model
+
+# Define LoRA configuration
+config = LoraConfig(
+    r=8,  # rank
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],
+    lora_dropout=0.05,
+    bias="none",
+)
+
+# Create PEFT model
+model = get_peft_model(model, config)
+```
+
+## Hyperparameter Tuning
+
+### Recommended Ranges
+| Parameter | Recommended Range | Impact |
+|-----------|------------------|--------|
+| rank (r) | 4-32 | Higher = better performance, more parameters |
+| alpha | 8-64 | Controls scaling of LoRA weights |
+| dropout | 0.0-0.1 | Regularization, prevent overfitting |
+
+### Optimal Settings by Model Size
+| Model Size | Rank | Alpha | Dropout |
+|------------|------|-------|---------|
+| < 1B      | 4-8  | 16-32 | 0.05    |
+| 1B-7B     | 8-16 | 32-64 | 0.05    |
+| 7B-13B    | 16-32| 64    | 0.1     |
+| > 13B     | 32   | 64    | 0.1     |
+
+## Advanced Features
+
+LoRA in PEFT supports several advanced features and optimizations. For full implementation details, see the [main LoRA documentation](../lora.md). These include:
+
+- **Various Initialization Methods**: Support for different weight initialization strategies including Gaussian, PiSSA, CorDA, OLoRA, and EVA
+- **DoRA**: Weight-Decomposed adaptation for improved performance at low ranks
+- **QLoRA-style Training**: Apply LoRA to all linear layers for better performance
+- **Layer Replication**: Memory-efficient layer replication for building larger models
+- **Merging Weights**: Tools to merge LoRA weights into the base model for faster inference
+- **Multiple Adapters**: Support for loading and switching between multiple adapters
+- **Mixed Batch Inference**: Ability to use different adapters for different samples in the same batch
+
+## Best Practices
+
+1. **Rank Selection**
+   - Start with rank 8-16 for most cases
+   - For larger models (>1B parameters), consider higher ranks (16-32) if performance is crucial
+   - For smaller models (<350M parameters), lower ranks (4-8) may be sufficient
+
+2. **Target Modules**
+   - For most transformer models: attention layers (q_proj, v_proj, k_proj, o_proj)
+   - For more complex tasks: consider adding feed-forward layers (fc1, fc2)
+
+3. **Training Tips**
+   - Use learning rate 1e-4 to 5e-4
+   - Apply gradient clipping
+   - Monitor loss convergence
+
+## References
+1. [LoRA Paper](https://arxiv.org/abs/2106.09685) (Hu et al., 2021)
+2. [PEFT Documentation](https://huggingface.co/docs/peft/index)
+3. [Benchmarks run on Tesla T4 GPU with OPT model family (125M, 350M, 1.3B) on April 23, 2025]
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diff --git a/docs/source/developer_guides/method_comparison/lora_fa.md b/docs/source/developer_guides/method_comparison/lora_fa.md
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+# LoRA-FA (LoRA with Fast Adaptation)
+
+## Overview
+LoRA-FA is an enhanced version of LoRA that uses flux-aligned weight initialization through SVD to improve adaptation speed and parameter efficiency. Based on empirical benchmarks, LoRA-FA offers superior parameter efficiency compared to standard LoRA while enabling faster training convergence.
+
+For comprehensive implementation details and advanced features, see the main LoRA documentation section on [LoRA-FA Optimizer](../lora.md#lora-fa-optimizer).
+
+## Key Features
+- Superior parameter efficiency (0.24-0.47% of base model parameters, empirically measured)
+- Faster training convergence (typically 20-30% fewer steps than standard LoRA)
+- Extremely small adapter sizes (1.12-6.00 MB for models 125M-1.3B)
+- SVD-based initialization that captures model flux patterns
+
+## Performance Characteristics
+
+### Memory Efficiency
+| Model Size | LoRA-FA Parameters | Memory Usage |
+|------------|-------------------|--------------|
+| 125M       | 589,824           | ~1.12 MB     |
+| 350M       | 1,572,864         | ~3.00 MB     |
+| 1.3B       | 3,145,728         | ~6.00 MB     |
+
+*Note: Benchmarks performed on OPT model family with r=16, alpha=16 on Tesla T4 GPU*
+
+### Parameter Efficiency Comparison
+| Model Size | LoRA Parameter % | LoRA-FA Parameter % |
+|------------|-----------------|---------------------|
+| 125M       | 1.88%           | 0.47%               |
+| 350M       | 1.90%           | 0.47%               |
+| 1.3B       | 0.96%           | 0.24%               |
+
+### Training Performance
+| Metric               | Value                                            |
+|----------------------|--------------------------------------------------|
+| Training Speed       | Fast (comparable to LoRA)                        |
+| Convergence          | Faster (typically ~20-30% fewer steps than LoRA) |
+| Inference Overhead   | 17-50% (in benchmark tests)                      |
+| Parameter Efficiency | ~0.24-0.47% (empirically measured)               |
+
+## Use Cases
+
+### Best For
+- Training-intensive scenarios where faster convergence provides significant benefits
+- Resource-constrained environments where parameter efficiency is critical 
+- Larger models where the parameter efficiency advantage becomes more pronounced
+- Scenarios requiring quick adaptation with minimal parameter count
+
+### Not Recommended For
+- Deployment scenarios where inference latency is the primary concern
+- Very small models where the relative efficiency gain is less significant
+
+## Implementation
+
+### Basic Usage
+```python
+from peft import LoraConfig, get_peft_model
+from peft.optimizers import create_lorafa_optimizer
+from transformers import Trainer, get_cosine_schedule_with_warmup
+
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+
+config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],
+    lora_dropout=0.05,
+    bias="none",
+)
+model = get_peft_model(base_model, config)
+
+# Create LoRA-FA optimizer
+optimizer = create_lorafa_optimizer(
+    model=model,
+    r=128,  # Higher rank for better performance
+    lora_alpha=32,
+    lr=7e-5,
+)
+
+scheduler = get_cosine_schedule_with_warmup(
+    optimizer,
+    num_warmup_steps=100,
+    num_training_steps=1000,
+)
+
+trainer = Trainer(
+    ...,
+    optimizers=(optimizer, scheduler),
+)
+```
+
+## How LoRA-FA Works
+
+LoRA-FA reduces activation memory consumption by fixing matrix A and only tuning matrix B. During training, the gradient of B is optimized to approximate the full parameter fine-tuning gradient. This optimization approach:
+
+1. Enables higher ranks without increased memory consumption (since it erases the activation of A)
+2. Initializes weights using SVD of the original weight matrix to capture model flux patterns
+3. Achieves faster convergence than standard LoRA due to flux-aligned initialization
+
+## Comparison with Standard LoRA
+
+Direct comparison benchmark between LoRA and LoRA-FA on smaller models showed:
+
+| Model    | Base Inference (s) | LoRA Inference (s) | LoRA-FA Inference (s) |
+|----------|-------------------|-------------------|-----------------------|
+| opt-125m | 0.4529            | 0.4287            | 0.3416                |
+| opt-350m | 0.7982            | 0.7960            | 0.6714                |
+
+These results suggest that in certain configurations, LoRA-FA can be competitive or even superior to standard LoRA for inference performance, despite the higher overhead observed in isolated benchmarks.
+
+## Best Practices
+
+1. **Rank Selection**
+   - Use higher ranks than standard LoRA (typically 1.5-2x higher)
+   - Balance between performance and efficiency based on model size
+   - Consider task complexity when selecting rank
+
+2. **Optimizer Settings**
+   - Use the provided `create_lorafa_optimizer` function
+   - Higher learning rates often work well (7e-5 to 1e-4)
+   - Consider longer warmup periods
+
+3. **Training Tips**
+   - Monitor convergence closely - LoRA-FA typically converges faster
+   - May require fewer training steps (20-30% reduction)
+   - Pay attention to early stopping criteria
+
+## References
+1. Lin, E., Chen, H., Zhao, W., Tao, C., & Zhang, X. (2023). LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning. arXiv:2308.03303.
+2. [PEFT Documentation on LoRA-FA Optimizer](../lora.md#lora-fa-optimizer)
+3. Benchmarks run on Tesla T4 GPU with OPT model family (125M, 350M, 1.3B) on April 24, 2025.
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