README.md

---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
tags:
  - llama
  - llama-3.1
  - lora
  - fine-tuned
  - peft
---

# Fine-tuned Llama 3.1 8B Instruct Model

This is a fine-tuned version of Meta's [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model using LoRA (Low-Rank Adaptation) technique.

## Model Details

### Model Description

This model is a fine-tuned version of Llama 3.1 8B Instruct using PEFT (Parameter-Efficient Fine-Tuning) with LoRA. The fine-tuning process focused on adapting the base model to specific use cases while maintaining the general capabilities of the original model.

- **Developed by:** Bhavya Bipin Gada (PRESHENT)
- **Model type:** LoRA fine-tuned Llama 3.1
- **Language(s):** English
- **License:** MIT
- **Finetuned from model:** meta-llama/Llama-3.1-8B-Instruct

## Training Details

### Training Procedure

The model was fine-tuned using the following LoRA configuration:

- **LoRA rank (r):** 8
- **LoRA alpha:** 16
- **LoRA dropout:** 0.05
- **Target modules:** k_proj, q_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Task type:** CAUSAL_LM

### Training Data

```json

[
  {
    "messages": [
      {
        "role": "user",
        "content": "What sustainable solutions do you offer for solar energy?"
      },
      {
        "role": "assistant",
        "content": "We offer rooftop solar panels, community solar options, and solar battery storage solutions optimized for your location."
      }
    ]
  },
  {
    "messages": [
      {
        "role": "user",
        "content": "Can you recommend a sustainable water purification method?"
      },
      {
        "role": "assistant",
        "content": "Reverse osmosis systems powered by renewable energy are highly effective and sustainable."
      }
    ]
  }
]

```

## Usage

You can use this model with the Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig

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

# Load the LoRA adapter
adapter_path = "bhavyabgada/preshent-llama"
model = PeftModel.from_pretrained(model, adapter_path)

# Generate text
input_text = "Your prompt here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations and Biases

This model inherits the limitations and biases from the base Llama 3.1 8B Instruct model.


## Framework versions

- PEFT 0.13.2