README.md

---
base_model: meta-llama/meta-llama-3.1-8b-instruct
tags:
- llama adapter
- trl
- llama3.1 8b
license: apache-2.0
language:
- en
---
## Model Overview
A LoRA (Low-Rank Adaptation) fine-tuned adapter for the Llama-3.1-8B language model.

## Model Details
- Base Model: meta-llama/Llama-3.1-8B-instruct
- Adaptation Method: LoRA

## Training Configuration
### Training Hyperparameters
- Learning Rate: 25e-6
- Batch Size: 2
- Number of Epochs: 2
- Training Steps: ~3,000
- Precision: "BF16"

### LoRA 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)

## Usage
This adapter must be used in conjunction with the base Llama-3.1-8B 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 LoRA adapter
model = PeftModel.from_pretrained(base_model, "path_to_adapter")
```

## Limitations and Biases
- This adapter might inherits some limitations and biases present in the base Llama-3.1-8B-instruct model
- The training dataset size (~1k steps) is relatively small, which may limit the adapter's effectiveness