DeepSeek-R1-Distill-Qwen-7B

Instructions to use VECTORVV1/DeepSeek-R1-Distill-Qwen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VECTORVV1/DeepSeek-R1-Distill-Qwen-7B",
	filename="DeepSeek-R1-Distill-Qwen-7B-BF16.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/DeepSeek-R1-Distill-Qwen-7B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16

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/DeepSeek-R1-Distill-Qwen-7B:BF16
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16

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/DeepSeek-R1-Distill-Qwen-7B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16

Use Docker

docker model run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16

LM Studio
Jan
Ollama
How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-7B with Ollama:
```
ollama run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16
```

Unsloth Studio new

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-7B 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/DeepSeek-R1-Distill-Qwen-7B 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/DeepSeek-R1-Distill-Qwen-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VECTORVV1/DeepSeek-R1-Distill-Qwen-7B to start chatting

Pi new

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16

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": "DeepSeek-R1-Distill-Qwen-7B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-7B with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16
```

Lemonade

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/DeepSeek-R1-Distill-Qwen-7B:BF16

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Qwen-7B-BF16

List all available models

lemonade list

DeepSeek-R1-Distill-Qwen-7B

File size: 1,781 Bytes

4b3627e

---
license: apache-2.0
tags:
  - uncensored
  - qwen3
  - vision
  - multimodal
language:
  - en
  - zh
---

# Qwen3VL-8B-Uncensored-HauhauCS-Aggressive

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

Qwen3VL-8B 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

This is the **Aggressive** variant with stronger uncensoring. Use this when the Balanced variant refuses too much.

For agentic coding and reliability-critical tasks, use the [Balanced](https://huggingface.co/HauhauCS/Qwen3VL-8B-Uncensored-HauhauCS-Balanced) variant instead.

## Downloads

| File | Quant | Size |
|------|-------|------|
| Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-BF16.gguf | BF16 | 16 GB |
| Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-Q8_0.gguf | Q8_0 | 8.2 GB |
| Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf | Q6_K | 6.3 GB |
| Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf | Q4_K_M | 4.7 GB |
| Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-mmproj-f16.gguf | mmproj | 1.1 GB |

## Specs

- 8B parameters
- 256K context
- Vision-language model (requires mmproj file for image input)
- Based on [Qwen3-VL-8B](https://huggingface.co/Qwen/Qwen3-VL-8B)

## Usage

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

**For vision capabilities**, load both the main model and the mmproj file.

llama.cpp example:
```bash
./llama-cli -m Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf \
  --mmproj Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-mmproj-f16.gguf \
  --image your_image.jpg \
  -p "Describe this image"
```