Instructions to use Melvin56/Qwen3-0.6B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Melvin56/Qwen3-0.6B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Melvin56/Qwen3-0.6B-abliterated-GGUF",
	filename="qwen3-0.6b-abliterated-BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Melvin56/Qwen3-0.6B-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Melvin56/Qwen3-0.6B-abliterated-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 Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Melvin56/Qwen3-0.6B-abliterated-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 Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Melvin56/Qwen3-0.6B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Melvin56/Qwen3-0.6B-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Melvin56/Qwen3-0.6B-abliterated-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M

Ollama
How to use Melvin56/Qwen3-0.6B-abliterated-GGUF with Ollama:
```
ollama run hf.co/Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use Melvin56/Qwen3-0.6B-abliterated-GGUF 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 Melvin56/Qwen3-0.6B-abliterated-GGUF 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 Melvin56/Qwen3-0.6B-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Melvin56/Qwen3-0.6B-abliterated-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Melvin56/Qwen3-0.6B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use Melvin56/Qwen3-0.6B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Melvin56/Qwen3-0.6B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-0.6B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-0.6B-abliterated-GGUF / README.md

Melvin56

Update README.md

3103031 verified about 1 year ago

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	---
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
	pipeline_tag: text-generation
	base_model:
	- huihui-ai/Qwen3-0.6B-abliterated
	tags:
	- chat
	- abliterated
	- uncensored
	extra_gated_prompt: >-
	Usage Warnings


	“Risk of Sensitive or Controversial Outputs“: This model’s safety
	filtering has been significantly reduced, potentially generating sensitive,
	controversial, or inappropriate content. Users should exercise caution and
	rigorously review generated outputs.

	“Not Suitable for All Audiences:“ Due to limited content filtering, the
	model’s outputs may be inappropriate for public settings, underage users, or
	applications requiring high security.

	“Legal and Ethical Responsibilities“: Users must ensure their usage
	complies with local laws and ethical standards. Generated content may carry
	legal or ethical risks, and users are solely responsible for any consequences.

	“Research and Experimental Use“: It is recommended to use this model for
	research, testing, or controlled environments, avoiding direct use in
	production or public-facing commercial applications.

	“Monitoring and Review Recommendations“: Users are strongly advised to
	monitor model outputs in real-time and conduct manual reviews when necessary
	to prevent the dissemination of inappropriate content.

	“No Default Safety Guarantees“: Unlike standard models, this model has not
	undergone rigorous safety optimization. huihui.ai bears no responsibility for
	any consequences arising from its use.
	---

	# Melvin56/Qwen3-0.6B-abliterated-GGUF

	Original Model : [huihui-ai/Qwen3-0.6B-abliterated](https://huggingface.co/huihui-ai/Qwen3-0.6B-abliterated)

	Llama.cpp build: 0208355 (5342)

	I used imatrix to create all these quants using this [Dataset](https://gist.github.com/tristandruyen/9e207a95c7d75ddf37525d353e00659c/#file-calibration_data_v5_rc-txt).

	---

	\| \| CPU (AVX2) \| CPU (ARM NEON) \| Metal \| cuBLAS \| rocBLAS \| SYCL \| CLBlast \| Vulkan \| Kompute \|
	\| :------------ \| :---------: \| :------------: \| :---: \| :----: \| :-----: \| :---: \| :------: \| :----: \| :------: \|
	\| K-quants \| ✅ \| ✅ \| ✅ \| ✅ \| ✅ \| ✅ \| ✅ 🐢5 \| ✅ 🐢5 \| ❌ \|
	\| I-quants \| ✅ 🐢4 \| ✅ 🐢4 \| ✅ 🐢4 \| ✅ \| ✅ \| Partial¹ \| ❌ \| ❌ \| ❌ \|
	```
	✅: feature works
	🚫: feature does not work
	❓: unknown, please contribute if you can test it youself
	🐢: feature is slow
	¹: IQ3_S and IQ1_S, see #5886
	²: Only with -ngl 0
	³: Inference is 50% slower
	⁴: Slower than K-quants of comparable size
	⁵: Slower than cuBLAS/rocBLAS on similar cards
	⁶: Only q8_0 and iq4_nl
	```