bhavyabgada/preshent-llama

Fine-tuned Llama 3.1 8B Instruct Model

This is a fine-tuned version of Meta's 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

[
  {
    "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:

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