Instructions to use lab-ii/llama3.2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lab-ii/llama3.2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lab-ii/llama3.2-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lab-ii/llama3.2-base") model = AutoModelForCausalLM.from_pretrained("lab-ii/llama3.2-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lab-ii/llama3.2-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lab-ii/llama3.2-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lab-ii/llama3.2-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lab-ii/llama3.2-base
- SGLang
How to use lab-ii/llama3.2-base 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 "lab-ii/llama3.2-base" \ --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": "lab-ii/llama3.2-base", "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 "lab-ii/llama3.2-base" \ --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": "lab-ii/llama3.2-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lab-ii/llama3.2-base with Docker Model Runner:
docker model run hf.co/lab-ii/llama3.2-base
- Xet hash:
- 26af8d0b4b0bb192450f13b7c1587dd53967a1ff858428808d7040cfd621f557
- Size of remote file:
- 22.9 MB
- SHA256:
- 9c1d0d6d9d70a621bafa0d7160a6270c20655ecc90b3ab937c5e7cb27ee214d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.