Instructions to use fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym")
model = AutoModelForMultimodalLM.from_pretrained("fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym

SGLang

How to use fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym
```

Model Information

Quantized version of meta-llama/Llama-3.1-8B using torch.float32 for quantization tuning.

4 bits (INT4)
group size = 128
Symmetrical Quantization
Method AutoAWQ

Quantization framework: Intel AutoRound

Note: this INT4 version of Llama-3.1-8B has been quantized to run inference through CPU.

Replication Recipe

Step 1 Install Requirements

I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.

python -m pip install <package> --upgrade

accelerate==1.0.1
auto_gptq==0.7.1
neural_compressor==3.1
torch==2.3.0+cpu
torchaudio==2.5.0+cpu
torchvision==0.18.0+cpu
transformers==4.45.2

Step 2 Build Intel Autoround wheel from sources

python -m pip install git+https://github.com/intel/auto-round.git

Step 3 Script for Quantization

  from transformers import AutoModelForCausalLM, AutoTokenizer
  model_name = "meta-llama/Llama-3.1-8B"
  model = AutoModelForCausalLM.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym, device, amp = 4, 128, True, 'cpu', False
  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
  autoround.quantize()
  output_dir = "./AutoRound/meta-llama_Llama-3.1-8B-auto_awq-int4-gs128-sym"
  autoround.save_quantized(output_dir, format='auto_awq', inplace=True)