Instructions to use hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16")
model = AutoModelForCausalLM.from_pretrained("hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16

SGLang

How to use hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16 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 "hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16" \
    --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": "hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16",
		"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 "hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16" \
        --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": "hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16 with Docker Model Runner:
```
docker model run hf.co/hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16
```

This repository is a community-driven quantized version of the original model meta-llama/Meta-Llama-3.1-8B which is the BF16 half-precision official version released by Meta AI.

Model Information

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

This repository contains meta-llama/Meta-Llama-3.1-8B quantized using bitsandbytes from BF16 down to NF4 with a block size of 64, and storage type torch.bfloat16.

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Safetensors

Model size

3B params

Tensor type

BF16

F32