Qwen3-4B-2507-Instruct-Aggressive

Instructions to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nazihara/Qwen3-4B-2507-Instruct-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 nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nazihara/Qwen3-4B-2507-Instruct-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 nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M

Use Docker

docker model run hf.co/nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M

LM Studio
Jan
Ollama
How to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with Ollama:
```
ollama run hf.co/nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M
```
Unsloth Desktop

How to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with Docker Model Runner:
```
docker model run hf.co/nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M
```

Lemonade

How to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-4B-2507-Instruct-Aggressive-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nazihara/Qwen3-4B-2507-Instruct-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 nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nazihara/Qwen3-4B-2507-Instruct-Aggressive with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "nazihara/Qwen3-4B-2507-Instruct-Aggressive:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3-4B-2507-Instruct-Aggressive

File size: 1,826 Bytes

24d5e69

---
license: apache-2.0
tags:
  - uncensored
  - qwen3
language:
  - en
  - zh
base_model: Qwen/Qwen3-4B-Instruct-2507
---

# Qwen3-4B-2507-Instruct-Uncensored-HauhauCS-Aggressive

> **[Join the Discord](https://discord.gg/SZ5vacTXYf)** for updates, roadmaps, projects, or just to chat.

Qwen3 4B 2507 Instruct 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

**Aggressive** applies stronger uncensoring. Use this when you need no refusals.


## Downloads

| File | Quant | Size |
|------|-------|------|
| Qwen3-4B-2507-Instruct-Uncensored-HauhauCS-Aggressive-FP16.gguf | FP16 | 7.5 GB |
| Qwen3-4B-2507-Instruct-Uncensored-HauhauCS-Aggressive-Q8_0.gguf | Q8_0 | 4.0 GB |
| Qwen3-4B-2507-Instruct-Uncensored-HauhauCS-Aggressive-Q6_K.gguf | Q6_K | 3.1 GB |
| Qwen3-4B-2507-Instruct-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf | Q4_K_M | 2.4 GB |

## Specs

- 4B parameters (dense)
- 262K context
- Based on [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)

## Recommended Settings

From the Qwen team:

**Thinking mode (default):**
- `temperature=0.6`
- `top_p=0.95`
- `top_k=20`
- `min_p=0`

**Non-thinking mode:**
- Add `/no_think` at the end of your prompt, or
- `temperature=0.7`
- `top_p=0.8`
- `top_k=20`
- `min_p=0`

**Important:**
- Use `--jinja` flag for proper chat template handling
- Thinking mode produces `<think>...</think>` tags before responses

## Usage

Works with llama.cpp, LM Studio, Jan, koboldcpp, Ollama, etc.

```bash
# llama.cpp example
./llama-cli -m Qwen3-4B-2507-Instruct-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf \
  -p "Hello" --jinja -c 8192
```