Instructions to use RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf 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 RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-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 RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-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 RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-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 RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-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 RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf with Ollama:
ollama run hf.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf:Q4_K_M
Run and chat with the model
lemonade run user.codellama_-_CodeLlama-70b-Python-hf-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download CodeLlama-70b-Python-hf.IQ4_XS.gguf from RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf: direct link, hf CLI and curl.
- Browser
- Download file 37.2 GB
-
https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/resolve/3afe751495d08be61a3d56016c9667b44f63af76/CodeLlama-70b-Python-hf.IQ4_XS.gguf
- Command line
-
hf download hf://RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf@3afe751495d08be61a3d56016c9667b44f63af76/CodeLlama-70b-Python-hf.IQ4_XS.gguf
-
curl -L -o CodeLlama-70b-Python-hf.IQ4_XS.gguf https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/resolve/3afe751495d08be61a3d56016c9667b44f63af76/CodeLlama-70b-Python-hf.IQ4_XS.gguf
37.2 GB
- Xet hash:
- 371937b84132567de363993a6d442678a61326ea556c4997b3873cc4a3deb63d
- Size of remote file:
- 37.2 GB
- SHA256:
- e3fb49025ecafd171633d912e9a24ce359e86ade27b073f0a7ff8c6fb71b64b0
·
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