Instructions to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huihui-ai/Huihui-GLM-5.2-abliterated-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("huihui-ai/Huihui-GLM-5.2-abliterated-GGUF", dtype="auto") - llama-cpp-python
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="huihui-ai/Huihui-GLM-5.2-abliterated-GGUF", filename="UD-IQ1_M/GLM-5.2-UD-IQ1_M-00001-of-00006.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use huihui-ai/Huihui-GLM-5.2-abliterated-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 huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M # Run inference directly in the terminal: llama cli -hf huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M # Run inference directly in the terminal: llama cli -hf huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_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 huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M # Run inference directly in the terminal: ./llama-cli -hf huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_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 huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
Use Docker
docker model run hf.co/huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
- LM Studio
- Jan
- vLLM
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huihui-ai/Huihui-GLM-5.2-abliterated-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huihui-ai/Huihui-GLM-5.2-abliterated-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
- SGLang
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "huihui-ai/Huihui-GLM-5.2-abliterated-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huihui-ai/Huihui-GLM-5.2-abliterated-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "huihui-ai/Huihui-GLM-5.2-abliterated-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huihui-ai/Huihui-GLM-5.2-abliterated-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with Ollama:
ollama run hf.co/huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
- Unsloth Studio
How to use huihui-ai/Huihui-GLM-5.2-abliterated-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 huihui-ai/Huihui-GLM-5.2-abliterated-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 huihui-ai/Huihui-GLM-5.2-abliterated-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for huihui-ai/Huihui-GLM-5.2-abliterated-GGUF to start chatting
- Pi
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with Docker Model Runner:
docker model run hf.co/huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
- Lemonade
How to use huihui-ai/Huihui-GLM-5.2-abliterated-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull huihui-ai/Huihui-GLM-5.2-abliterated-GGUF:UD-IQ1_M
Run and chat with the model
lemonade run user.Huihui-GLM-5.2-abliterated-GGUF-UD-IQ1_M
List all available models
lemonade list
Update README.md
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**Note** We will directly perform ablation on the GGUF files from [unsloth/GLM-5.2-GGUF](https://huggingface.co/unsloth/GLM-5.2-GGUF). Some weights quantized in Q5_K and Q6_K will be converted to Q8_0. This time, the weights of the first 12 layers and all expert modules were not ablated, so the final weights will increase, but the change will not be significant. The UD-IQ1_M is only 3GB larger than the original.
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Some weights quantized in Q5_K and Q6_K, Q8_0 will be converted to MXFP4 (UD-Q2_K_MXFP4)
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## Download and merge
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**Note** We will directly perform ablation on the GGUF files from [unsloth/GLM-5.2-GGUF](https://huggingface.co/unsloth/GLM-5.2-GGUF). Some weights quantized in Q5_K and Q6_K will be converted to Q8_0. This time, the weights of the first 12 layers and all expert modules were not ablated, so the final weights will increase, but the change will not be significant. The UD-IQ1_M is only 3GB larger than the original.
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Some weights quantized in Q5_K and Q6_K, Q8_0 will be converted to MXFP4 (UD-Q2_K_XL -> UD-Q2_K_MXFP4)
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## Download and merge
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