Instructions to use CojoFitz/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CojoFitz/model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CojoFitz/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use CojoFitz/model 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 CojoFitz/model:Q4_K_M # Run inference directly in the terminal: llama cli -hf CojoFitz/model:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf CojoFitz/model:Q4_K_M # Run inference directly in the terminal: llama cli -hf CojoFitz/model: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 CojoFitz/model:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf CojoFitz/model: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 CojoFitz/model:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf CojoFitz/model:Q4_K_M
Use Docker
docker model run hf.co/CojoFitz/model:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use CojoFitz/model with Ollama:
ollama run hf.co/CojoFitz/model:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use CojoFitz/model with Docker Model Runner:
docker model run hf.co/CojoFitz/model:Q4_K_M
- Lemonade
How to use CojoFitz/model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CojoFitz/model:Q4_K_M
Run and chat with the model
lemonade run user.model-Q4_K_M
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- 60ab823f6cedc80f653e1ce665ead97c6ab38be783fb5a0a3a96a8b513cee578
- Size of remote file:
- 8.54 GB
- SHA256:
- b0f47c8402465602b9246adc321ce8e9ef0af63b1158b1100f85c46842212a61
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