Instructions to use RichardErkhov/codellama_-_CodeLlama-13b-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-13b-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-13b-hf-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/codellama_-_CodeLlama-13b-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-13b-hf-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/codellama_-_CodeLlama-13b-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-13b-hf-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/codellama_-_CodeLlama-13b-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-13b-hf-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf with Ollama:
ollama run hf.co/RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf:Q4_K_M
Run and chat with the model
lemonade run user.codellama_-_CodeLlama-13b-hf-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download CodeLlama-13b-hf.IQ3_S.gguf from RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf: direct link, hf CLI and curl.
- Browser
- Download file 5.66 GB
-
https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf/resolve/main/CodeLlama-13b-hf.IQ3_S.gguf
- Command line
-
hf download hf://RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf/CodeLlama-13b-hf.IQ3_S.gguf
-
curl -L -o CodeLlama-13b-hf.IQ3_S.gguf https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-13b-hf-gguf/resolve/main/CodeLlama-13b-hf.IQ3_S.gguf
5.66 GB
- Xet hash:
- 00ec51a5ba99a009aa766464aea79ac10df39b716ba3a8fdafcdb141a073a218
- Size of remote file:
- 5.66 GB
- SHA256:
- 22196038f983b9949633197b7b7f6dd36267b808e22df30b8e242c19a64ad015
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.