Instructions to use sharpbai/open_llama_13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharpbai/open_llama_13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sharpbai/open_llama_13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sharpbai/open_llama_13b") model = AutoModelForCausalLM.from_pretrained("sharpbai/open_llama_13b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sharpbai/open_llama_13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sharpbai/open_llama_13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sharpbai/open_llama_13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sharpbai/open_llama_13b
- SGLang
How to use sharpbai/open_llama_13b 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 "sharpbai/open_llama_13b" \ --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": "sharpbai/open_llama_13b", "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 "sharpbai/open_llama_13b" \ --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": "sharpbai/open_llama_13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sharpbai/open_llama_13b with Docker Model Runner:
docker model run hf.co/sharpbai/open_llama_13b
Download pytorch_model-00033-of-00042.bin from sharpbai/open_llama_13b: direct link, hf CLI and curl.
- Browser
- Download file 634 MB
-
https://huggingface.co/sharpbai/open_llama_13b/resolve/5ba1cb9ecb79ee1d7779c519de092377229e7ae9/pytorch_model-00033-of-00042.bin
- Command line
-
hf download hf://sharpbai/open_llama_13b@5ba1cb9ecb79ee1d7779c519de092377229e7ae9/pytorch_model-00033-of-00042.bin
-
curl -L -o pytorch_model-00033-of-00042.bin https://huggingface.co/sharpbai/open_llama_13b/resolve/5ba1cb9ecb79ee1d7779c519de092377229e7ae9/pytorch_model-00033-of-00042.bin
634 MB
- Xet hash:
- 5a83e7c83b90d39e2c9d27c538d2710cc93a4cd5fec0fd22e62e49331d58bb70
- Size of remote file:
- 634 MB
- SHA256:
- 17bb7825e4a9c9dbfb0805a55c7407f68376a4ae47f104b57a0d28b53c7264b1
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