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

Qwen3-30B-A3B / README.md

VECTORVV1

Duplicate from HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive

6a935c1 6 days ago

preview code

raw

history blame contribute delete

1.83 kB

	---
	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.