Instructions to use meta-llama/Llama-3.2-1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meta-llama/Llama-3.2-1B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-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-3.2-1B-Instruct") model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct", device_map="auto") 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
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use meta-llama/Llama-3.2-1B-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-3.2-1B-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-3.2-1B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/meta-llama/Llama-3.2-1B-Instruct
- SGLang
How to use meta-llama/Llama-3.2-1B-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-3.2-1B-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-3.2-1B-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-3.2-1B-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-3.2-1B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use meta-llama/Llama-3.2-1B-Instruct with Docker Model Runner:
docker model run hf.co/meta-llama/Llama-3.2-1B-Instruct
Request for reconsideration of my access request
Hello Meta team,
My access request to this repository was previously rejected, and I understand that rejected requests cannot be resubmitted from the user side on Hugging Face.
I am a PhD student at East China Normal University, and I would like to respectfully request reconsideration of my application for access to Llama 3.2.
My intended use is strictly academic and non-commercial. I hope to use the model mainly for learning, study, and academic exploration, in order to better understand large language models and their research value.
It is possible that my previous application did not clearly explain my academic affiliation, learning needs, and research purpose. I would be very grateful if you could help review my case and, if possible, reset or reconsider my access request.
I will fully comply with the Llama license and acceptable use policy.
Thank you very much for your time and consideration.