Instructions to use MoMonir/AutoCoder_S_6.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MoMonir/AutoCoder_S_6.7B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MoMonir/AutoCoder_S_6.7B-GGUF", filename="autocoder_s_6.7b-q4_k_m.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use MoMonir/AutoCoder_S_6.7B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MoMonir/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-server -hf MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MoMonir/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 MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MoMonir/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 MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use MoMonir/AutoCoder_S_6.7B-GGUF with Ollama:
ollama run hf.co/MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
- Unsloth Studio new
How to use MoMonir/AutoCoder_S_6.7B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MoMonir/AutoCoder_S_6.7B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MoMonir/AutoCoder_S_6.7B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MoMonir/AutoCoder_S_6.7B-GGUF to start chatting
- Docker Model Runner
How to use MoMonir/AutoCoder_S_6.7B-GGUF with Docker Model Runner:
docker model run hf.co/MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
- Lemonade
How to use MoMonir/AutoCoder_S_6.7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.AutoCoder_S_6.7B-GGUF-Q4_K_M
List all available models
lemonade list
MoMonir/AutoCoder_S_6.7B-GGUF
This model was converted to GGUF format from Bin12345/AutoCoder_S_6.7B using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
About GGUF (TheBloke Description)
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
- llama.cpp. The source project for GGUF. Offers a CLI and a server option.
- text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
- KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
- GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
- LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
- LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
- backyard.ai (Formeraly Faraday.dev), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
- llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
- candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
- ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
Use with llama.cpp
Install llama.cpp through brew.
brew install ggerganov/ggerganov/llama.cpp
Invoke the llama.cpp server or the CLI. CLI:
llama-cli --hf-repo MoMonir/AutoCoder_S_6.7B-GGUF --model autocoder_s_6.7b-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo MoMonir/AutoCoder_S_6.7B-GGUF --model autocoder_s_6.7b-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m autocoder_s_6.7b-q4_k_m.gguf -n 128
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