Text Generation
Transformers
Safetensors
PyTorch
llama
feature-extraction
bnb-my-repo
facebook
meta
llama-3
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use bnb-community/Llama-3.2-1B-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bnb-community/Llama-3.2-1B-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bnb-community/Llama-3.2-1B-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bnb-community/Llama-3.2-1B-bnb-4bit") model = AutoModel.from_pretrained("bnb-community/Llama-3.2-1B-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bnb-community/Llama-3.2-1B-bnb-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bnb-community/Llama-3.2-1B-bnb-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bnb-community/Llama-3.2-1B-bnb-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bnb-community/Llama-3.2-1B-bnb-4bit
- SGLang
How to use bnb-community/Llama-3.2-1B-bnb-4bit 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 "bnb-community/Llama-3.2-1B-bnb-4bit" \ --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": "bnb-community/Llama-3.2-1B-bnb-4bit", "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 "bnb-community/Llama-3.2-1B-bnb-4bit" \ --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": "bnb-community/Llama-3.2-1B-bnb-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bnb-community/Llama-3.2-1B-bnb-4bit with Docker Model Runner:
docker model run hf.co/bnb-community/Llama-3.2-1B-bnb-4bit
- Xet hash:
- 47964912cbc350710191b4cfdd5162c7e1b42a4af1d478173ca95f5cc29a56fe
- Size of remote file:
- 1.03 GB
- SHA256:
- 346e84bb47ef81550442af0bd6cdb87b0c0fa3cadf61f9b034b8534bedbc654e
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