Instructions to use meta-llama/Meta-Llama-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Meta-Llama-3-8B with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
model = AutoModelForMultimodalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Meta-Llama-3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Meta-Llama-3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Meta-Llama-3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/meta-llama/Meta-Llama-3-8B

SGLang

How to use meta-llama/Meta-Llama-3-8B 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 "meta-llama/Meta-Llama-3-8B" \
    --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": "meta-llama/Meta-Llama-3-8B",
		"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 "meta-llama/Meta-Llama-3-8B" \
        --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": "meta-llama/Meta-Llama-3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use meta-llama/Meta-Llama-3-8B with Docker Model Runner:
```
docker model run hf.co/meta-llama/Meta-Llama-3-8B
```

Request for Reconsideration of Access to Meta-Llama-3-8B-Instruct

#280

by xiaz112358 - opened May 10

Discussion

xiaz112358

May 10

Hello Meta Llama team,

I am writing to respectfully request a reconsideration of my rejected access request for meta-llama/Meta-Llama-3-8B-Instruct.

I understand that access to this model is restricted and subject to review. I would like to clarify my intended use case, as my previous request may not have provided enough context.

I am an undergraduate student researcher working on humanoid motion generation and locomotion learning. My intended use is strictly non-commercial academic research. Specifically, I need access to Meta-Llama-3-8B-Instruct only because it is required as the base model for the LLM2Vec text encoder used by NVIDIA Kimodo. Kimodo depends on this Llama-based text encoder to convert natural-language motion prompts into embeddings for text-conditioned motion generation.

My research use is local and simulation-only. I want to evaluate Kimodo as a motion reference generator for humanoid locomotion tasks, such as step-over, step-up, gap-crossing, and other terrain traversal motions. The model will not be used as a chatbot, public-facing application, autonomous agent, API service, commercial product, or user-data-processing system.

I will not redistribute the model weights, provide access to the model to others, expose it through any public endpoint, fine-tune it for deployment, or use it for any prohibited or harmful application. The model will only be used on my local workstation as a dependency for academic experimentation, in compliance with the Llama license and Acceptable Use Policy.

If my previous request was rejected due to insufficient information or unclear intended use, I hope this clarification helps. I would be grateful if you could review my request again.

Thank you for your time and consideration.

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