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": "VECTORVV1/Qwen3-30B-A3B:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

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 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 VECTORVV1/Qwen3-30B-A3B:Q8_0

Run Hermes

hermes

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

VECTORVV1

HauhauCS commited on 26 days ago

Commit

6a935c1

0 Parent(s):

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

Browse files

Files changed (3) hide show

.gitattributes +39 -0
GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-Q8_0.gguf +3 -0
README.md +64 -0

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+GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive-FP16.gguf filter=lfs diff=lfs merge=lfs -text

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+size 31842799168

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+---
+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.