Instructions to use MoMonir/codegeex4-all-9b-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 MoMonir/codegeex4-all-9b-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 MoMonir/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MoMonir/codegeex4-all-9b-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 MoMonir/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MoMonir/codegeex4-all-9b-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/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MoMonir/codegeex4-all-9b-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/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MoMonir/codegeex4-all-9b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MoMonir/codegeex4-all-9b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use MoMonir/codegeex4-all-9b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MoMonir/codegeex4-all-9b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MoMonir/codegeex4-all-9b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MoMonir/codegeex4-all-9b-GGUF:Q4_K_M
- Ollama
How to use MoMonir/codegeex4-all-9b-GGUF with Ollama:
ollama run hf.co/MoMonir/codegeex4-all-9b-GGUF:Q4_K_M
- Unsloth Studio
How to use MoMonir/codegeex4-all-9b-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/codegeex4-all-9b-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/codegeex4-all-9b-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/codegeex4-all-9b-GGUF to start chatting
- Docker Model Runner
How to use MoMonir/codegeex4-all-9b-GGUF with Docker Model Runner:
docker model run hf.co/MoMonir/codegeex4-all-9b-GGUF:Q4_K_M
- Lemonade
How to use MoMonir/codegeex4-all-9b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MoMonir/codegeex4-all-9b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.codegeex4-all-9b-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Create README.md
Browse files
README.md
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---
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license: other
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license_name: codegeex4
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license_link: https://huggingface.co/THUDM/codegeex4-all-9b/blob/main/LICENSE
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language:
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- zh
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- en
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tags:
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- glm
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- codegeex
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- thudm
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inference: false
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pipeline_tag: text-generation
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base_model: THUDM/codegeex4-all-9b
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---
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# MoMonir/codegeex4-all-9b-GGUF
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This model was converted to GGUF format from [`THUDM/codegeex4-all-9b`](https://huggingface.co/THUDM/codegeex4-all-9b) using llama.cpp.
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Refer to the [original model card](https://huggingface.co/THUDM/codegeex4-all-9b) for more details on the model.
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<!-- README_GGUF.md-about-gguf start -->
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### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description)
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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.
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Here is an incomplete list of clients and libraries that are known to support GGUF:
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* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
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* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
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* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
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* [LM Studio](https://lmstudio.ai/), 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.
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* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
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* [backyard.ai](https://backyard.ai/) (Formeraly [Faraday.dev](https://faraday.dev/)), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
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* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
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* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
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* [ctransformers](https://github.com/marella/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.
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<!-- README_GGUF.md-about-gguf end -->
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## Use with llama.cpp
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Install llama.cpp through brew.
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```bash
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brew install ggerganov/ggerganov/llama.cpp
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```
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Invoke the llama.cpp server or the CLI.
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CLI:
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```bash
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llama-cli --hf-repo MoMonir/codegeex4-all-9b-GGUF --model codegeex4-all-9b.Q4_K_M.gguf -p "The meaning to life and the universe is"
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```
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Server:
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```bash
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llama-server --hf-repo MoMonir/codegeex4-all-9b-GGUF --model codegeex4-all-9b.Q4_K_M.gguf -c 2048
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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```
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git clone https://github.com/ggerganov/llama.cpp && \
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cd llama.cpp && \
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make && \
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./main -m codegeex4-all-9b.Q4_K_M.gguf -n 128
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```
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