Instructions to use VECTORVV1/Qwen3-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VECTORVV1/Qwen3-30B-A3B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VECTORVV1/Qwen3-30B-A3B",
	filename="Qwen3-30B-A3B-Q8_0.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 VECTORVV1/Qwen3-30B-A3B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Qwen3-30B-A3B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Qwen3-30B-A3B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Qwen3-30B-A3B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Qwen3-30B-A3B:Q8_0

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 VECTORVV1/Qwen3-30B-A3B:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/Qwen3-30B-A3B:Q8_0

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 VECTORVV1/Qwen3-30B-A3B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/Qwen3-30B-A3B:Q8_0

Use Docker

docker model run hf.co/VECTORVV1/Qwen3-30B-A3B:Q8_0

LM Studio
Jan
Ollama
How to use VECTORVV1/Qwen3-30B-A3B with Ollama:
```
ollama run hf.co/VECTORVV1/Qwen3-30B-A3B:Q8_0
```

Unsloth Studio new

How to use VECTORVV1/Qwen3-30B-A3B 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 VECTORVV1/Qwen3-30B-A3B 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 VECTORVV1/Qwen3-30B-A3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VECTORVV1/Qwen3-30B-A3B to start chatting

Pi new

How to use VECTORVV1/Qwen3-30B-A3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/Qwen3-30B-A3B:Q8_0

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": "Qwen3-30B-A3B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use VECTORVV1/Qwen3-30B-A3B with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/Qwen3-30B-A3B:Q8_0
```

Lemonade

How to use VECTORVV1/Qwen3-30B-A3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/Qwen3-30B-A3B:Q8_0

Run and chat with the model

lemonade run user.Qwen3-30B-A3B-Q8_0

List all available models

lemonade list

GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive

Join the Discord 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 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

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.

Downloads last month: 60

GGUF

Model size

30B params

Architecture

deepseek2

Hardware compatibility

8-bit

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