Instructions to use meta-llama/Llama-3.3-70B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meta-llama/Llama-3.3-70B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meta-llama/Llama-3.3-70B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use meta-llama/Llama-3.3-70B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meta-llama/Llama-3.3-70B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Llama-3.3-70B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/meta-llama/Llama-3.3-70B-Instruct
- SGLang
How to use meta-llama/Llama-3.3-70B-Instruct 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/Llama-3.3-70B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Llama-3.3-70B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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/Llama-3.3-70B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Llama-3.3-70B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use meta-llama/Llama-3.3-70B-Instruct with Docker Model Runner:
docker model run hf.co/meta-llama/Llama-3.3-70B-Instruct
My request to access t Llama-3.3-70B-Instruct was rejected
Hello,
I am a graduate researcher at a U.S. university, and I need access to the LLaMA-3.3-70B-Instruct model for my research. However, my request was rejected by the repository's authors. The message I received states:
"You need to agree to share your contact information to access this model." How can I resubmit my request?
Could you please assist me in gaining access to this model? I would greatly appreciate your help.
Best,
Mehrnoush
Hi,
Same with me. I'm a student at a Swedish university, and my request was denied.
how can I resubmit the request?
Thanks
I am a software engineer based in the US and my request also got rejected without a reason. It is becoming concerning to use the model as no reason was provided for the rejection. We thought this model was meant for the community and rejection with no reason seems not user friendly.
Does anyone know how can we resubmit the request?