Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-instruct
+pipeline_tag: text-generation
+tags:
+- lora
+- adapter
+- writing
+- CoT
+---
+# Merged-Llama-Adapters-317-320
+
+A merged LoRA adapter combining four fine-tuned adapters (317-320) for the Llama-3.1-8B language model.
+
+## Model Details
+
+- Base Model: meta-llama/Llama-3.1-8B-instruct
+- Adaptation Method: Merged LoRA
+- Source Adapters:
+  - https://huggingface.co/kevin009/merge-17-2
+  - https://huggingface.co/kevin009/llama318
+  - https://huggingface.co/kevin009/llama319
+  - https://huggingface.co/kevin009/llama320
+  - https://huggingface.co/kevin009/llama1720-base
+  - https://huggingface.co/kevin009/llama324
+
+## Merger Configuration
+
+### Source Adapters
+
+All source adapters share the following configuration:
+- Rank (r): 16
+- Alpha: 16
+- Target Modules:
+  - q_proj (Query projection)
+  - k_proj (Key projection)
+  - v_proj (Value projection)
+  - o_proj (Output projection)
+  - up_proj (Upsampling projection)
+  - down_proj (Downsampling projection)
+  - gate_proj (Gate projection)
+
+### Merger Details
+
+- Merger Method: Linear interpolation
+- Merger Weights: Equal weights (0.25) for each adapter
+- Combined Rank: 16 (maintained from source adapters)
+
+## Usage
+
+This merged adapter must be used with the base Llama-3.1-8B-instruct model.
+
+### Loading the Model
+
+```python
+# Initialize with first F32 model
+peft_model = PeftModel.from_pretrained(model, "llama319", adapter_name="llama319")
+# Load F32 models (higher precision)
+peft_model.load_adapter("llama324", adapter_name="llama324")
+peft_model.load_adapter("llama320", adapter_name="llama320")
+peft_model.load_adapter("llama318", adapter_name="llama318")
+peft_model.load_adapter("llama1720-base", adapter_name="llama1720-base")
+peft_model.load_adapter("merge-17-20", adapter_name="merge-17-20")
+
+adapters = ["llama319", "llama320", "llama1720-base", "merge-17-20", "llama324", "llama318"]
+weights = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+peft_model.add_weighted_adapter(adapters, weights, "merge", combination_type="ties", density=0.2)
+
+peft_model.set_adapter("merge")
+peft_model.save_pretrained("merged")
+
+```
+
+## Limitations and Biases
+
+- This merged adapter inherits limitations and biases from:
+  - The base Llama-3.1-8B-instruct model
+  - All four source adapters
+- The merging process may result in:
+  - Potential loss of specialized capabilities from individual adapters
+  - Averaged behavior across different adapter specializations
+  - Possible interference between adapter weights
+
+## Merging Process
+
+The adapters were merged using the following approach:
+1. Linear interpolation of adapter weights
+2. Equal weighting (0.25) applied to each source adapter
+3. Preservation of original LoRA rank and architecture
+
+### Method Used
+
+The adapters were merged using PEFT (Parameter-Efficient Fine-Tuning) library's weighted adapter combination feature. The process combines multiple LoRA adapters using linear interpolation with specified weights.
+
+
+### Key Parameters
+
+- `combination_type="ties"`: Uses the TIES (Task Interference Edge Selection) method for combining adapters
+- `density=0.2`: Controls the sparsity of the merged weights
+
+
+### Notes
+
+- The order of loading adapters may affect the final result
+- Equal weights were chosen to maintain balanced influence from each adapter
+- The merged adapter maintains the same architecture and rank as the original adapters
+- While this adapter merges multiple fine-tunes, each component was developed as part of independent research efforts to explore and language model capabilities as part of R&D process.
+
+
+## Datasets
+
+- Not yet released, but should be released after evaluation has completed.
+- Creating dataset alone tooks more than 3 month for creating 30k pairs dataset.
+- Only 1k pairs example considered to be synthetic dataset, the rest half synthetic and human written text.
+
+### Use Cases
+
+- This merged adapter can be used for a wide range of tasks, including but not limited to:
+  - Accessibility
+  - Revision & Editing
+  - instruction-following use with xml tags
+  - Thinking & reasoning with xml tag of <thinking> and </thinking>, if being asked i the instructions.
+  
+
+These Models not optimized for code, math, or other specialized tasks that need Perefence Optimization.
+
+## Why SFT Instead of RLHF/DPO?
+- RLHF and DPO approaches often lead to vocabulary limitations and overfitting due to their optimization objectives
+
+
+## Why Multiple Adapters?
+- Resource Issue: Placing the training into smaller adapters requires less GPU memory and compute time while gives more control over the training process.
+- Iterative Development: Each adapter can be developed and tested independently
+- Training Infrastructure: The complete fine-tuning process was conducted across multiple sessions, totaling over 100 hours on high-end GPUs (H100, H200, or L40s)
+- Flexibility: Multiple adapters allow for different combinations or weightings
+
+
+## License
+
+Licensed under Apache 2.0 License.
+
+This merged adapter is part of independent individual research work. While the code is open-source under the Apache 2.0 license, please note:
+
+- You are free to use, modify, and distribute this adapter following the Apache 2.0 license terms
+- This work is provided "as is" without warranties or conditions of any kind
+- This is an independent research project and not affiliated with any organization
+- Attribution is appreciated but not required
+- For full license details, see: https://www.apache.org/licenses/LICENSE-2.0