Instructions to use biololab/tinyllama-symptom-extractor_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biololab/tinyllama-symptom-extractor_16bit with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biololab/tinyllama-symptom-extractor_16bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use biololab/tinyllama-symptom-extractor_16bit 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 biololab/tinyllama-symptom-extractor_16bit:F16 # Run inference directly in the terminal: llama cli -hf biololab/tinyllama-symptom-extractor_16bit:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf biololab/tinyllama-symptom-extractor_16bit:F16 # Run inference directly in the terminal: llama cli -hf biololab/tinyllama-symptom-extractor_16bit:F16
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 biololab/tinyllama-symptom-extractor_16bit:F16 # Run inference directly in the terminal: ./llama-cli -hf biololab/tinyllama-symptom-extractor_16bit:F16
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 biololab/tinyllama-symptom-extractor_16bit:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf biololab/tinyllama-symptom-extractor_16bit:F16
Use Docker
docker model run hf.co/biololab/tinyllama-symptom-extractor_16bit:F16
- LM Studio
- Jan
- Ollama
How to use biololab/tinyllama-symptom-extractor_16bit with Ollama:
ollama run hf.co/biololab/tinyllama-symptom-extractor_16bit:F16
- Unsloth Desktop
- Docker Model Runner
How to use biololab/tinyllama-symptom-extractor_16bit with Docker Model Runner:
docker model run hf.co/biololab/tinyllama-symptom-extractor_16bit:F16
- Lemonade
How to use biololab/tinyllama-symptom-extractor_16bit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull biololab/tinyllama-symptom-extractor_16bit:F16
Run and chat with the model
lemonade run user.tinyllama-symptom-extractor_16bit-F16
List all available models
lemonade list
- Atomic Chat
Download tinyllama-symptom-extractor_16bit-unsloth.F16.gguf from biololab/tinyllama-symptom-extractor_16bit: direct link, hf CLI and curl.
- Browser
- Download file 2.2 GB
-
https://huggingface.co/biololab/tinyllama-symptom-extractor_16bit/resolve/452173319d6a86bfc04b337647ab93110927859f/tinyllama-symptom-extractor_16bit-unsloth.F16.gguf
- Command line
-
hf download hf://biololab/tinyllama-symptom-extractor_16bit@452173319d6a86bfc04b337647ab93110927859f/tinyllama-symptom-extractor_16bit-unsloth.F16.gguf
-
curl -L -o tinyllama-symptom-extractor_16bit-unsloth.F16.gguf https://huggingface.co/biololab/tinyllama-symptom-extractor_16bit/resolve/452173319d6a86bfc04b337647ab93110927859f/tinyllama-symptom-extractor_16bit-unsloth.F16.gguf
2.2 GB
- Xet hash:
- 5c832a7497bdf260dffebe4b5d8406d36f97a4add61606fc5a4c924924c1e43e
- Size of remote file:
- 2.2 GB
- SHA256:
- 65e0295cf69d65616a24b6ad53f3a78bc50fe4892d8eda149a91d056d016ab5d
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