Instructions to use xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic")
model = AutoModelForMultimodalLM.from_pretrained("xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic

SGLang

How to use xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic 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 "xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic" \
    --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": "xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic" \
        --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": "xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic with Docker Model Runner:
```
docker model run hf.co/xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic
```

Qwen3.5-4B-MultiDistill-Megamix-8xMerge-UNCENSORED-heretic / reproduce /README.md

xtools-at

Upload reproduce/README.md with huggingface_hub

f5b5185 verified about 1 month ago

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Reproduction guide

This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.

Git installation

This system installed Heretic from a Git repository: https://github.com/p-e-w/heretic.git @ 66a952b49b00bfd1854bdad769fee5fb225b53be

To reproduce the model, you must install Heretic from this exact repository and commit.

Models

Base model: xtools-at/Qwen3.5-4B-MultiDistill-Megamix-8xMerge (Commit: f03e5d9)

Datasets

mlabonne/harmful_behaviors (Commit: 01cead0)
mlabonne/harmless_alpaca (Commit: 02c6a92)

Selected trial

Trial number: 50
Refusals: 3/100 (baseline: 95/100)
KL divergence: 0.0883 (baseline: 0 (by definition))

Environment

Heretic: v2.0.0.dev0 (Origin: Git (https://github.com/p-e-w/heretic.git @ 66a952b49b00bfd1854bdad769fee5fb225b53be))
PyTorch: 2.11.0+cu130
Other dependencies: See requirements.txt.

Contents of this directory

requirements.txt: The exact versions of all Python packages.
config.toml: The exact configuration used, including the RNG seed.
xtools-at--Qwen3--5-4B-MultiDistill-Megamix-8xMerge.jsonl: The Optuna study journal containing the history of all trials.
SHA256SUMS: Cryptographic hashes for all weight files.
reproduce.json: A machine-readable file containing all reproducibility information.

How to reproduce

You can automate this process, including all verification steps, by downloading the reproduce.json file and running heretic --reproduce reproduce.json.

Install the exact version of Heretic indicated in the Environment section above, from its original source.
Install the packages listed in requirements.txt: pip install -r requirements.txt
Install the correct version of PyTorch: pip install torch==2.11.0+cu130 --index-url https://download.pytorch.org/whl/cu130
Place the provided config.toml in your working directory.
Run Heretic without any additional arguments: heretic
Wait for the run to finish, then select trial 50 and export the model.
Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in SHA256SUMS: sha256sum -c SHA256SUMS (or look at the hashes online if you uploaded to Hugging Face)

To use the included Optuna study journal xtools-at--Qwen3--5-4B-MultiDistill-Megamix-8xMerge.jsonl, place it in the checkpoints directory (usually checkpoints/) before running Heretic.

This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.