Instructions to use robertmccourt/endoscopy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use robertmccourt/endoscopy 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 robertmccourt/endoscopy:Q4_0 # Run inference directly in the terminal: llama cli -hf robertmccourt/endoscopy:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf robertmccourt/endoscopy:Q4_0 # Run inference directly in the terminal: llama cli -hf robertmccourt/endoscopy:Q4_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf robertmccourt/endoscopy:Q4_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf robertmccourt/endoscopy:Q4_0
Use Docker
docker model run hf.co/robertmccourt/endoscopy:Q4_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use robertmccourt/endoscopy with Ollama:
ollama run hf.co/robertmccourt/endoscopy:Q4_0
- Unsloth Desktop
- Docker Model Runner
How to use robertmccourt/endoscopy with Docker Model Runner:
docker model run hf.co/robertmccourt/endoscopy:Q4_0
- Lemonade
How to use robertmccourt/endoscopy with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull robertmccourt/endoscopy:Q4_0
Run and chat with the model
lemonade run user.endoscopy-Q4_0
List all available models
lemonade list
- Atomic Chat
Download training_args.bin from robertmccourt/endoscopy: direct link, hf CLI and curl.
- Browser
- Download file 6.2 kB
-
https://huggingface.co/robertmccourt/endoscopy/resolve/main/training_args.bin
- Command line
-
hf download hf://robertmccourt/endoscopy/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/robertmccourt/endoscopy/resolve/main/training_args.bin
6.2 kB
- Xet hash:
- b4d973859ffb1a2075dc4b57a6cbfc76e4e38085a0e4fa432369b5213b0ea6a0
- Size of remote file:
- 6.2 kB
- SHA256:
- e3b3dbdec3ee7159450ff67c62b4f150708fc1383e0c3a0ca28d691124a59407
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