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 / README.md

nazihara

Duplicate from HauhauCS/Qwen3-4B-2507-Instruct-Uncensored-HauhauCS-Aggressive

24d5e69 6 months ago

preview code

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metadata

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 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

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.

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