GLM-4.7-Flash-Aggressive

Instructions to use joermd/GLM-4.7-Flash-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

GLM-4.7-Flash-Aggressive

File size: 1,825 Bytes

a21fff6

---
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.