Instructions to use N8Programs/Qwen3.6-35B-A3B-AntiLoop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N8Programs/Qwen3.6-35B-A3B-AntiLoop with Transformers:

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

pipe = pipeline("image-text-to-text", model="N8Programs/Qwen3.6-35B-A3B-AntiLoop")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("N8Programs/Qwen3.6-35B-A3B-AntiLoop")
model = AutoModelForMultimodalLM.from_pretrained("N8Programs/Qwen3.6-35B-A3B-AntiLoop", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use N8Programs/Qwen3.6-35B-A3B-AntiLoop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N8Programs/Qwen3.6-35B-A3B-AntiLoop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N8Programs/Qwen3.6-35B-A3B-AntiLoop",
		"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/N8Programs/Qwen3.6-35B-A3B-AntiLoop

SGLang

How to use N8Programs/Qwen3.6-35B-A3B-AntiLoop 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 "N8Programs/Qwen3.6-35B-A3B-AntiLoop" \
    --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": "N8Programs/Qwen3.6-35B-A3B-AntiLoop",
		"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 "N8Programs/Qwen3.6-35B-A3B-AntiLoop" \
        --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": "N8Programs/Qwen3.6-35B-A3B-AntiLoop",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use N8Programs/Qwen3.6-35B-A3B-AntiLoop with Docker Model Runner:
```
docker model run hf.co/N8Programs/Qwen3.6-35B-A3B-AntiLoop
```

Qwen3.6-35B-A3B-AntiLoop / README.md

N8Programs

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library_name: transformers
license: apache-2.0
license_link: >-
  https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/995ad96eacd98c81ed38be0c5b274b04031597b0/LICENSE
base_model: Qwen/Qwen3.6-35B-A3B
pipeline_tag: image-text-to-text
tags:
  - qwen3.6
  - moe
  - lora
  - merged
  - antidoom

Qwen3.6-35B-A3B-AntiLoop

This is a BF16 merged-weight variant of Qwen/Qwen3.6-35B-A3B, fine-tuned to recover from pathological self-verification and enumeration loops while preserving ordinary long-form reasoning.

The artifact contains a full Transformers checkpoint; no PEFT adapter is needed at inference time. It retains the base model's multimodal architecture, tokenizer, chat template, MTP weights, and 262,144-token native context length.

Provenance

Base: Qwen/Qwen3.6-35B-A3B
Pinned base revision: 995ad96eacd98c81ed38be0c5b274b04031597b0
Fine-tuning: rank-32 LoRA, alpha 64, dropout 0
Targets: 190 attention-side projections across all 40 language-model layers (150 Gated DeltaNet projections and 40 full-attention projections)
Objective: 0.9 * supervised CE + 0.1 * KL(base || tuned)
Training: one epoch / 178 optimizer steps, seed 0
Training run: n8programs/qwen-antidoom, run dw7mbmce

The LoRA was merged as BF16(W_FP32 + B_FP32 @ (A_FP32 * 2)), one target tensor at a time in small row tiles. merge_manifest.json records the exact adapter hash, base revision, environment, per-target effective deltas, and every output-shard SHA-256. merge_verification.json certifies that all 190 intended tensors equal that formula bit-for-bit after BF16 rounding, every target differs from the base, and every non-target byte is unchanged.

Evaluation

The behavior evaluations below used the same round-2 adapter served on the Qwen3.6-35B-A3B FP8 base with vLLM. They validate the adapter behavior; the merged BF16 artifact was separately validated at the tensor level.

Evaluation	Base/control	AntiLoop
Held-out enumeration-loop rate (285 prompts, calibrated judge)	25%	2%
Answer delivery on the same prompts	77%	98%
GPQA Diamond, thinking enabled	167/198 (84.3%)	166/198 (83.8%)

The paired GPQA difference was -0.5 percentage points (exact two-sided McNemar p = 1.0), consistent with preserved capability at this sample size. The loop evaluation is task-specific and judge-based; it should not be read as a general safety or truthfulness guarantee.

Usage

Use a recent Transformers release with Qwen3.6 support:

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "N8Programs/Qwen3.6-35B-A3B-AntiLoop"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

The checkpoint can also be served directly with recent vLLM/SGLang builds that support the Qwen3.6 MoE architecture. Follow the upstream Qwen3.6 model card for chat templating, thinking-mode controls, multimodal inputs, deployment details, and the base model's limitations.

Limitations

This is a narrow behavioral fine-tune, not a general alignment or safety model.
The merged BF16 checkpoint has not been exhaustively evaluated across every language, modality, tool-use setting, or long-context regime.
Quantization can change behavior; validate the exact deployed quantization.
Outputs may still be incorrect, overconfident, repetitive, or unsafe.

License

Apache 2.0, following the base checkpoint. See LICENSE and the upstream Qwen3.6-35B-A3B model card.