Instructions to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF", filename="Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.IQ4_XS.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-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": "mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
- SGLang
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with Ollama:
ollama run hf.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF to start chatting
- Pi
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
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": "mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-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 mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF-Q4_K_M
List all available models
lemonade list
auto-patch README.md
Browse files|
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### quants_skip: -->
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<!-- ### skip_mmproj: -->
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static quants of https://huggingface.co/jenerallee78/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated
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---
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base_model: jenerallee78/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated
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language:
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- en
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- zh
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library_name: transformers
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license: agpl-3.0
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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tags:
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- abliteration
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- uncensored
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- qwen3.6
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- qwen
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- qwen-vl
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- moe
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- abliterix
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- ega
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- expert-granular-abliteration
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- multimodal
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- vision
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- image-text-to-text
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- conversational
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### quants_skip: -->
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<!-- ### skip_mmproj: -->
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static quants of https://huggingface.co/jenerallee78/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated
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<!-- provided-files -->
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF).***
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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF/resolve/main/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.Q4_K_S.gguf) | Q4_K_S | 20.0 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF/resolve/main/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.Q8_0.gguf) | Q8_0 | 37.0 | fast, best quality |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time.
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<!-- end -->
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