Instructions to use aaditya/OpenBioLLM-Llama3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use aaditya/OpenBioLLM-Llama3-8B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="aaditya/OpenBioLLM-Llama3-8B-GGUF", filename="openbiollm-llama3-8b.Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use aaditya/OpenBioLLM-Llama3-8B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use aaditya/OpenBioLLM-Llama3-8B-GGUF with Ollama:
ollama run hf.co/aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
- Unsloth Studio
How to use aaditya/OpenBioLLM-Llama3-8B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aaditya/OpenBioLLM-Llama3-8B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aaditya/OpenBioLLM-Llama3-8B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aaditya/OpenBioLLM-Llama3-8B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use aaditya/OpenBioLLM-Llama3-8B-GGUF with Docker Model Runner:
docker model run hf.co/aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
- Lemonade
How to use aaditya/OpenBioLLM-Llama3-8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aaditya/OpenBioLLM-Llama3-8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.OpenBioLLM-Llama3-8B-GGUF-Q4_K_M
List all available models
lemonade list
Update README.md
Browse filessorry the readme file wasnt updated. Trying again.
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license: mit
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license: mit
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To run a simple model do the following. The model of course didnt work that well for me:
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pip install llama-cpp-python
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pip install huggingface-hub
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You could of course change the gguf file to download. Please dont download all the files as it can be fairly big.
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huggingface-cli download aaditya/OpenBioLLM-Llama3-8B-GGUF openbiollm-llama3-8b.Q4_K_M.gguf --local-dir ./models --local-dir-use-symlinks False
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The file to simply start generating, do the following:
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from llama_cpp import Llama
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llm = Llama(model_path="./models/openbiollm-llama3-8b.Q4_K_M.gguf", chat_format="llama-3") # Set chat_format according to the model you are using
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response=llm.create_chat_completion(
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max_tokens=250, messages = [
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{"role": "system", "content": "You are biomed ai"},
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{"role": "user", "content": "name 5 diabetes medications"}
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]
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)
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print(response["choices"][0]["message"]["content"])
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