Instructions to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF 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 atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF: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 atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF: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 atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "atakhadivi/Qwen3.8-2B-Uncensored-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "atakhadivi/Qwen3.8-2B-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M

Ollama
How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF: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": "atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-2B-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF: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 atakhadivi/Qwen3.8-2B-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use atakhadivi/Qwen3.8-2B-Uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf atakhadivi/Qwen3.8-2B-Uncensored-GGUF: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 "atakhadivi/Qwen3.8-2B-Uncensored-GGUF: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"

Qwen3.8-2B Uncensored — GGUF (Q4_K_M)

GGUF quantization of empero-ai/Qwen3.8-2B with refusals removed via Heretic v1.4.0 abliteration. Built from insraq/Qwen3.5-2B-EmperoAI-Qwen3.8-Distill-Heretic-Abliterated (Heretic v1.4.0 decensored build, reproducible recipe included in that repo). 2B parameters, Qwen3.5-architecture hybrid (Gated DeltaNet + full attention).

File

File	Quant	Size	SHA256
Qwen3.8-2B-Uncensored-Q4_K_M.gguf	Q4_K_M	1.27 GB	`61713f6d29b507f0ddd2ed989683e3a5e997b920b0c4901ee8f332321d4b0cc9`

Recommended for phones / PocketPal / SBCs. CPU-only is fine at this size.

Runtime requirements

llama.cpp >= b1-0379a19 (Qwen3.5 / Gated DeltaNet architecture support; May 2026+ build).
PocketPal AI: needs a build new enough for the qwen35 architecture.
No vision projector (text-only GGUF). No MTP head (converted with --no-mtp; the abliterated source does not include the speculative head).

Usage (llama.cpp)

llama-cli -m Qwen3.8-2B-Uncensored-Q4_K_M.gguf \
  --temp 0.6 --top-p 0.95 --top-k 20 -c 2048 -n 512

Use the embedded chat template (conversation mode). This is a reasoning model: answers open with a think block. Verified output on Apple Silicon CPU: 31.7 t/s generation.

Model card note

This is an uncensored model — the refusal direction was removed. It will not refuse harmful or illegal requests. Research / personal use only. Apache-2.0 inherited from the Qwen3.5-2B base.

Credits

Base: Qwen/Qwen3.5-2B (Alibaba Qwen team)
Distillation: empero-ai/Qwen3.8-2B (Qwen3.8 2.4T A95B -> Qwen3.5-2B)
Abliteration: insraq (Heretic v1.4.0)
GGUF conversion: llama.cpp convert_hf_to_gguf.py --no-mtp, Q4_K_M quantization

Downloads last month: 14,837

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for atakhadivi/Qwen3.8-2B-Uncensored-GGUF

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Quantized

(206)

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