Instructions to use Indah1/BioChat13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Indah1/BioChat13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Indah1/BioChat13") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Indah1/BioChat13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Indah1/BioChat13 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Indah1/BioChat13" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Indah1/BioChat13", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Indah1/BioChat13
- SGLang
How to use Indah1/BioChat13 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 "Indah1/BioChat13" \ --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": "Indah1/BioChat13", "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 "Indah1/BioChat13" \ --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": "Indah1/BioChat13", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Indah1/BioChat13 with Docker Model Runner:
docker model run hf.co/Indah1/BioChat13
Download training_args.bin from Indah1/BioChat13: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Indah1/BioChat13/resolve/main/training_args.bin
- Command line
-
hf download hf://Indah1/BioChat13/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Indah1/BioChat13/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- f3d3bd0e7a715ee8760e2ca636b74cfc614f6176ec01ec63576eecc54dff87a9
- Size of remote file:
- 5.43 kB
- SHA256:
- 7a6ce65df748d685e492acd464e3eb9f8077f2ade643a2611e3141eccf45706f
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