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
| Type | Requirement |
|---|---|
| GPU | 8–10 GB VRAM (recommended) |
| CPU | Supported (slower) |
| RAM | 16 GB or more |
🔹 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))
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