---
license: apache-2.0
language:
- en
base_model:
- meta-llama/Llama-3.3-70B-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/llama317
  - https://huggingface.co/kevin009/llama318
  - https://huggingface.co/kevin009/llama319
  - https://huggingface.co/kevin009/llama320

## 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
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-instruct")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-instruct")

# Load merged LoRA adapter
model = PeftModel.from_pretrained(base_model, "path_to_merged_adapter")
```

## 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.

### Step-by-Step Merging Process

1. Load the base model and initial adapter:
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

# Load first adapter as base
peft_model = PeftModel.from_pretrained(model, "llama319", adapter_name="llama319")
```

2. Load additional adapters:
```python
# Load remaining adapters
peft_model.load_adapter("llama320", adapter_name="llama320")
peft_model.load_adapter("llama318", adapter_name="llama318")
peft_model.load_adapter("llama317", adapter_name="llama317")
```

3. Configure and execute the merger:
```python
# Define adapters and their weights
adapters = ["llama319", "llama320", "llama318", "llama317"]
weights = [1.0, 1.0, 1.0, 1.0]  # Equal weights for all adapters

# Merge adapters
peft_model.add_weighted_adapter(
    adapters, 
    weights, 
    "merge",
    combination_type="ties",  # Using ties combination method
    density=0.2              # Density parameter for merger
)

# Set active adapter to merged version
peft_model.set_adapter("merge")

# Save the merged adapter
peft_model.save_pretrained("merged")
```

### 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
- `weights=[1.0, 1.0, 1.0, 1.0]`: Equal weighting for all adapters (0.25 each after normalization)

### 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.

## 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