Instructions to use Melvin56/Qwen3-8B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Melvin56/Qwen3-8B-abliterated-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Melvin56/Qwen3-8B-abliterated-GGUF", filename="qwen3-8b-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/Qwen3-8B-abliterated-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 Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Melvin56/Qwen3-8B-abliterated-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 Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Melvin56/Qwen3-8B-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/Qwen3-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Melvin56/Qwen3-8B-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/Qwen3-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Melvin56/Qwen3-8B-abliterated-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Melvin56/Qwen3-8B-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/Qwen3-8B-abliterated-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M
- Ollama
How to use Melvin56/Qwen3-8B-abliterated-GGUF with Ollama:
ollama run hf.co/Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M
- Unsloth Studio
How to use Melvin56/Qwen3-8B-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/Qwen3-8B-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/Qwen3-8B-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/Qwen3-8B-abliterated-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Melvin56/Qwen3-8B-abliterated-GGUF with Docker Model Runner:
docker model run hf.co/Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M
- Lemonade
How to use Melvin56/Qwen3-8B-abliterated-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Melvin56/Qwen3-8B-abliterated-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-8B-abliterated-GGUF-Q4_K_M
List all available models
lemonade list
File size: 2,783 Bytes
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license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- huihui-ai/Qwen3-8B-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/Qwen3-8B-abliterated-GGUF
Original Model : [huihui-ai/Qwen3-8B-abliterated](https://huggingface.co/huihui-ai/Qwen3-8B-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
``` |