DeepSeek-R1-Distill-Qwen-1.5B

Instructions to use VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B 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-1.5B",
	filename="DeepSeek-R1-Distill-Qwen-1.5B-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-1.5B 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-1.5B:BF16
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B: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-1.5B:BF16
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B: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-1.5B:BF16
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B: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-1.5B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B:BF16

Use Docker

docker model run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B:BF16

LM Studio
Jan
Ollama
How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B with Ollama:
```
ollama run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B:BF16
```

Unsloth Studio new

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B 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-1.5B 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-1.5B 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-1.5B to start chatting

Pi new

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B 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-1.5B: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-1.5B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B:BF16
```

Lemonade

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/DeepSeek-R1-Distill-Qwen-1.5B:BF16

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Qwen-1.5B-BF16

List all available models

lemonade list

DeepSeek-R1-Distill-Qwen-1.5B

File size: 3,016 Bytes

8e013ba

---
license: apache-2.0
tags:
  - uncensored
  - qwen3.5
  - qwen
  - gguf
language:
  - en
  - zh
  - multilingual
---

# Qwen3.5-2B-Uncensored-HauhauCS-Aggressive

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

Qwen3.5-2B uncensored by HauhauCS.

## About

**0/465 refusals.** Fully uncensored with zero capability loss.

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 Variant

Stronger uncensoring with more thorough refusal removal. If this variant is too loose for your use case, a Balanced variant may follow.

**Note:** The model is fully unlocked and will not refuse prompts. However, it may occasionally append a short disclaimer at the end of a response (e.g. "This is general information, not legal advice..."). This is baked into the base model's training and not a refusal — the actual content is still generated in full.

## Downloads

| File | Quant | Size |
|------|-------|------|
| Qwen3.5-2B-Uncensored-HauhauCS-Aggressive-BF16.gguf | BF16 | 3.6 GB |
| Qwen3.5-2B-Uncensored-HauhauCS-Aggressive-Q8_0.gguf | Q8_0 | 1.9 GB |
| Qwen3.5-2B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf | Q6_K | 1.5 GB |
| Qwen3.5-2B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf | Q4_K_M | 1.2 GB |
| mmproj-Qwen3.5-2B-Uncensored-HauhauCS-Aggressive-f16.gguf | Vision encoder | 638 MB |

**Vision support:** This model is natively multimodal. The `mmproj` file is the vision encoder — you need it alongside the main GGUF to use image/video inputs. Load both files in llama.cpp, LM Studio, or any compatible runtime.

## Specs

- 2B dense parameters, 24 layers
- Hybrid architecture: Gated DeltaNet linear attention + full softmax attention (3:1 ratio)
- 262K native context (extendable to 1M with YaRN)
- Natively multimodal (text, image, video)
- Multi-token prediction (MTP) support
- 248K vocabulary, 201 languages
- Based on [Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B)

## Recommended Settings

From the official Qwen authors:

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

**Non-thinking mode:**
- `temperature=0.7`, `top_p=0.8`, `top_k=20`, `min_p=0`

**Important:**
- Maintain at least 128K context to preserve thinking capabilities
- For production/high-throughput: use vLLM, SGLang, or KTransformers

**Note:** This is a brand new architecture (released 2026-03-02). llama.cpp support landed very recently — make sure you're on a recent build. Works with llama.cpp, LM Studio, Jan, koboldcpp, etc.

Also check out the [27B variant](https://huggingface.co/HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive), the [9B variant](https://huggingface.co/HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive), and all releases at [HauhauCS](https://huggingface.co/HauhauCS).

## Usage

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