Instructions to use cortexso/codestral 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 cortexso/codestral 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 cortexso/codestral:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/codestral:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/codestral:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/codestral: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 cortexso/codestral:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/codestral: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 cortexso/codestral:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/codestral:Q4_K_M
Use Docker
docker model run hf.co/cortexso/codestral:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/codestral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/codestral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/codestral", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/codestral:Q4_K_M
- Ollama
How to use cortexso/codestral with Ollama:
ollama run hf.co/cortexso/codestral:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use cortexso/codestral with Docker Model Runner:
docker model run hf.co/cortexso/codestral:Q4_K_M
- Lemonade
How to use cortexso/codestral with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/codestral:Q4_K_M
Run and chat with the model
lemonade run user.codestral-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download model.gguf from cortexso/codestral: direct link, hf CLI and curl.
- Browser
- Download file 13.3 GB
-
https://huggingface.co/cortexso/codestral/resolve/8926c3ca5d6ccdc6d8c3025206a887ae6bc136e6/model.gguf
- Command line
-
hf download hf://cortexso/codestral@8926c3ca5d6ccdc6d8c3025206a887ae6bc136e6/model.gguf
-
curl -L -o model.gguf https://huggingface.co/cortexso/codestral/resolve/8926c3ca5d6ccdc6d8c3025206a887ae6bc136e6/model.gguf
13.3 GB
- Xet hash:
- 3706497896779b6afc10560e32343a6828747df8926181351b3db4dfe97d7c10
- Size of remote file:
- 13.3 GB
- SHA256:
- 003e48ed892850b80994fcddca2bd6b833b092a4ef2db2853c33a3144245e06c
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