Instructions to use RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-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/Bin12345_-_AutoCoder_S_6.7B-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/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-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/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-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/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-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/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf with Ollama:
ollama run hf.co/RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/Bin12345_-_AutoCoder_S_6.7B-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Bin12345_-_AutoCoder_S_6.7B-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- 296da20f823ecad654c728720c71ed7aee9713b394a362ef75165ce5fc486ebc
- Size of remote file:
- 3.3 GB
- SHA256:
- 5ba583e69808d3de09127a0c91c56a58345bd406595f2b955dda2a56b27d26dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.