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
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.
Limitations and Biases
- This merged adapter inherits limitations and biases from:
- The base Llama-3.1-8B-instruct model
- More baises from traning data as most of them were fiction work.
- 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:
- Linear interpolation of adapter weights
- Equal weighting (0.25) applied to each source adapter
- 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 adaptersdensity=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.
- Only 1k pairs example of revision task <input_text> + <style_guide> => <-> </revised_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 and , 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
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
