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

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

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

Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF

File size: 3,592 Bytes

---
base_model: jenerallee78/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated
language:
- en
- zh
library_name: transformers
license: agpl-3.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- abliteration
- uncensored
- qwen3.6
- qwen
- qwen-vl
- moe
- abliterix
- ega
- expert-granular-abliteration
- multimodal
- vision
- image-text-to-text
- conversational
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
<!-- ### quants:  x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip:  -->
<!-- ### skip_mmproj:  -->
static quants of https://huggingface.co/jenerallee78/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated

<!-- provided-files -->

***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).***

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.
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF/resolve/main/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.Q2_K.gguf) | Q2_K | 13.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF/resolve/main/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.Q3_K_S.gguf) | Q3_K_S | 15.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF/resolve/main/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.Q3_K_M.gguf) | Q3_K_M | 16.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF/resolve/main/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.Q3_K_L.gguf) | Q3_K_L | 18.2 |  |
| [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 |
| [GGUF](https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated-GGUF/resolve/main/Qwen3.6-35B-A3B-Abliterix-EGA-abliterated.Q4_K_M.gguf) | Q4_K_M | 21.3 | fast, recommended |
| [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 |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

<!-- end -->