logiya-vidhyapathi/llama_quantization_4_bit

Llama-3.1-8B-Instruct – AWQ 4-bit

This repository contains a 4-bit AWQ quantized version of Llama-3.1-8B-Instruct. The model is optimized for lower memory usage and faster inference with minimal quality loss.

🔹 Model Details

• —Base Model: meta-llama/Llama-3.1-8B-Instruct
• —Quantization Method: AWQ (Activation-aware Weight Quantization)
• —Precision: 4-bit
• —Framework: PyTorch
• —Quantized Using: LLM Compressor
• —Intended Use: Text generation, chat, instruction following

🔹 Why AWQ?

AWQ reduces model size and VRAM usage by:

• —Quantizing weights to 4-bit
• —Preserving important activation ranges
• —Maintaining better accuracy compared to naive quantization

🔹 Hardware Requirements

🔹 How to Load the Model

Using Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "your-username/your-model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16
)

prompt = "Explain transformers in simple words"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))