Instructions to use joermd/GLM-4.7-Flash-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use joermd/GLM-4.7-Flash-Aggressive with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="joermd/GLM-4.7-Flash-Aggressive", filename="GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-FP16.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 joermd/GLM-4.7-Flash-Aggressive with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf joermd/GLM-4.7-Flash-Aggressive:Q4_K_M # Run inference directly in the terminal: llama-cli -hf joermd/GLM-4.7-Flash-Aggressive:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf joermd/GLM-4.7-Flash-Aggressive:Q4_K_M # Run inference directly in the terminal: llama-cli -hf joermd/GLM-4.7-Flash-Aggressive: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 joermd/GLM-4.7-Flash-Aggressive:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf joermd/GLM-4.7-Flash-Aggressive: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 joermd/GLM-4.7-Flash-Aggressive:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf joermd/GLM-4.7-Flash-Aggressive:Q4_K_M
Use Docker
docker model run hf.co/joermd/GLM-4.7-Flash-Aggressive:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use joermd/GLM-4.7-Flash-Aggressive with Ollama:
ollama run hf.co/joermd/GLM-4.7-Flash-Aggressive:Q4_K_M
- Unsloth Studio new
How to use joermd/GLM-4.7-Flash-Aggressive 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 joermd/GLM-4.7-Flash-Aggressive 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 joermd/GLM-4.7-Flash-Aggressive to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for joermd/GLM-4.7-Flash-Aggressive to start chatting
- Pi new
How to use joermd/GLM-4.7-Flash-Aggressive with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf joermd/GLM-4.7-Flash-Aggressive:Q4_K_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": "joermd/GLM-4.7-Flash-Aggressive:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use joermd/GLM-4.7-Flash-Aggressive with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf joermd/GLM-4.7-Flash-Aggressive:Q4_K_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 joermd/GLM-4.7-Flash-Aggressive:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use joermd/GLM-4.7-Flash-Aggressive with Docker Model Runner:
docker model run hf.co/joermd/GLM-4.7-Flash-Aggressive:Q4_K_M
- Lemonade
How to use joermd/GLM-4.7-Flash-Aggressive with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull joermd/GLM-4.7-Flash-Aggressive:Q4_K_M
Run and chat with the model
lemonade run user.GLM-4.7-Flash-Aggressive-Q4_K_M
List all available models
lemonade list
| license: mit | |
| tags: | |
| - uncensored | |
| - glm4 | |
| - moe | |
| language: | |
| - en | |
| - zh | |
| # GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive | |
| > **[Join the Discord](https://discord.gg/SZ5vacTXYf)** for updates, roadmaps, projects, or just to chat. | |
| GLM-4.7 Flash uncensored by HauhauCS. | |
| ## About | |
| No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals. | |
| These are meant to be the best lossless uncensored models out there. | |
| ## Aggressive vs Balanced | |
| The Aggressive variant removes more refusal behavior. Use this if the Balanced variant still refuses too much. | |
| For agentic coding or tasks requiring higher reliability, use the [Balanced variant](https://huggingface.co/HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Balanced) instead. | |
| ## Downloads | |
| | File | Quant | Size | | |
| |------|-------|------| | |
| | GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-FP16.gguf | FP16 | 56 GB | | |
| | GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-Q8_0.gguf | Q8_0 | 30 GB | | |
| | GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-Q6_K.gguf | Q6_K | 23 GB | | |
| | GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf | Q4_K_M | 17 GB | | |
| ## Specs | |
| - 30B-A3B MoE (31B total, ~3B active per forward pass) | |
| - 202K context | |
| - Based on [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) | |
| ## Recommended Settings | |
| From the official Z.ai authors: | |
| **General use:** | |
| - `--temp 1.0 --top-p 0.95` | |
| **Tool-calling / agentic:** | |
| - `--temp 0.7 --top-p 1.0` | |
| **Important:** | |
| - Disable repeat penalty (or `--repeat-penalty 1.0`) | |
| - For llama.cpp: use `--min-p 0.01` (default 0.05 is too high) | |
| - Use `--jinja` flag for llama.cpp | |
| **Note:** Not recommended for Ollama due to chat template issues. Works well with llama.cpp, LM Studio, Jan. | |
| ## Usage | |
| Works with llama.cpp, LM Studio, Jan, koboldcpp, etc. | |