Instructions to use nmitchko/medguanaco-lora-33b-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nmitchko/medguanaco-lora-33b-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nmitchko/medguanaco-lora-33b-8bit")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nmitchko/medguanaco-lora-33b-8bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nmitchko/medguanaco-lora-33b-8bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nmitchko/medguanaco-lora-33b-8bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nmitchko/medguanaco-lora-33b-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nmitchko/medguanaco-lora-33b-8bit
- SGLang
How to use nmitchko/medguanaco-lora-33b-8bit 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 "nmitchko/medguanaco-lora-33b-8bit" \ --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": "nmitchko/medguanaco-lora-33b-8bit", "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 "nmitchko/medguanaco-lora-33b-8bit" \ --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": "nmitchko/medguanaco-lora-33b-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nmitchko/medguanaco-lora-33b-8bit with Docker Model Runner:
docker model run hf.co/nmitchko/medguanaco-lora-33b-8bit
Download adapter_model.bin from nmitchko/medguanaco-lora-33b-8bit: direct link, hf CLI and curl.
- Browser
- Download file 205 MB
-
https://huggingface.co/nmitchko/medguanaco-lora-33b-8bit/resolve/main/adapter_model.bin
- Command line
-
hf download hf://nmitchko/medguanaco-lora-33b-8bit/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/nmitchko/medguanaco-lora-33b-8bit/resolve/main/adapter_model.bin
205 MB
- Xet hash:
- db10d9194da116f0e36b78d10c6d51223baa9ab76b14d50695080d87acdc36a5
- Size of remote file:
- 205 MB
- SHA256:
- f71cefb236fb5a3e0037e473243fa21d7f02b7a6e113cea25528d8b155e34a13
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