Instructions to use quimmedes/Deepwen-3.6-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quimmedes/Deepwen-3.6-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="quimmedes/Deepwen-3.6-bf16")
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("quimmedes/Deepwen-3.6-bf16")
model = AutoModelForMultimodalLM.from_pretrained("quimmedes/Deepwen-3.6-bf16", 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 quimmedes/Deepwen-3.6-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quimmedes/Deepwen-3.6-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quimmedes/Deepwen-3.6-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/quimmedes/Deepwen-3.6-bf16

SGLang

How to use quimmedes/Deepwen-3.6-bf16 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 "quimmedes/Deepwen-3.6-bf16" \
    --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": "quimmedes/Deepwen-3.6-bf16",
		"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 "quimmedes/Deepwen-3.6-bf16" \
        --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": "quimmedes/Deepwen-3.6-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use quimmedes/Deepwen-3.6-bf16 with Docker Model Runner:
```
docker model run hf.co/quimmedes/Deepwen-3.6-bf16
```

Deepwen 3.6 (BF16)

Deepwen 3.6 is a fine-tuned derivative of Qwen/Qwen3.6-35B-A3B (MoE: 35B total / ~3B active), forged in DeepSeek traces — its reasoning DNA, effort control and "verify before you answer" discipline come from DeepSeek-V4-Flash-0731. On top of that foundation, it improves Design, Web Graphics and adds specialized skills for AAA GameDev 3D production workflows: procedural geometry, hard-surface shape language, and Blender asset pipelines.

What the model has

Advanced thinking (DeepSeek style) — the biggest source of performance gain, and the reasoning DNA comes from DeepSeek-V4-Flash-0731: its reasoning-effort system, its thinking discipline, its "verify before you answer" culture. The model reasons before it answers, and its thinking comes from two sources:
- Supervised reasoning training: the vast majority of training examples carry a full reasoning chain as part of the target — the model learns to think before it speaks, not just to parrot.
- Reasoning-effort control: a chat template ported from deepseek-ai/DeepSeek-V4-Flash-0731, with three effort levels — low (default), xhigh, and max ("Beyond maximum — exhaustive, relentless... do not stop reasoning until you have independently verified the solution from multiple angles").
Measurable improvements over the base model across procedural generation, hard-surface design, Blender workflows, lighting, web-graphics (Canvas/Three.js/WebGPU) and UI/design — while the original Qwen capabilities are fully preserved. Only improvements, no losses.
Tool calling and agentic behavior — reinforced through the reasoning training (planning, tool selection, structured output), retaining full native tool-calling support from the Qwen base.

Specialized 3D skills

Procedural 3D generation — explicit blockout gating before high-poly, conditional lightmap workflows, combinatorial validation, non-destructive pipelines.
Hard-surface shape language — stance/relational design, primary volume architecture, motif propagation, panel breakup.
Multi-skill asset workflows — Blender modifier-driven gear recipes, tooth profile generation, PBR game-prep, layered lighting legibility.
Web-graphics — WebGPU pipeline architecture, Three.js/WebGL workflows, Canvas 2D.
UI/design systems — layout, component architecture, visual hierarchy, accessibility.

Training overview

Fine-tuned with a curated, multi-skill supervised dataset. The vast majority of training examples carry full reasoning chains — the model learns to think before it answers, not just to parrot. Training focuses on AAA 3D asset production workflows plus web-graphics expertise (WebGPU, Three.js/WebGL, Canvas 2D) and UI/design systems.

Improvements over the base model

Capability	Improvement
Procedural generation	blockout gating, conditional lightmap, and validation workflows: FAIL → PASS across held-out tasks
Replay safety	base competence suite intact
Shape / hard-surface	consistent across held-out objects
Blender workflow	modifier-driven recipes, tooth profile generation, game-prep UV/PBR, non-destructive ordering
Lighting	layered lighting legibility (bounce and ambient)
Web-graphics	Canvas 2D / Three.js / WebGPU code generation measurably improved
UI/design	layout systems, component architecture, visual hierarchy, accessibility

Format

safetensors, bfloat16 (26 shards, ~67 GB)
Base dtype: bf16; Mamba SSM tensors in f32
Includes the full model (no MTP layers stripped at this level)

Usage

Standard transformers / vLLM / SGLang loading:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("quimmedes/Deepwen-3.6-bf16", torch_dtype="bfloat16")
tok = AutoTokenizer.from_pretrained("quimmedes/Deepwen-3.6-bf16")

To enable advanced thinking at maximum effort:

{
  "messages": [{"role": "user", "content": "..."}],
  "chat_template_kwargs": {"reasoning_effort": "max"}
}

Quantized versions

GGUF quantizations with MoQ (mixed) tensor types are published in:

quimmedes/Deepwen-3.6 — levels Q2.5 (IQ2_XXS), Q3, Q4.5, Q5, Q6, Q8, all without MTP (733 tensors), plus vision mmproj (BF16/F16).

Disclosures

The base model is released under the Apache License 2.0; this derivative inherits that license.
Qwen 3.6 is a copyrighted, trademarked model family of Alibaba. "Deepwen 3.6" is an independent fine-tuned derivative and is not affiliated with, endorsed by, or sponsored by Alibaba / Qwen. The "Qwen" name is used solely to identify the base model.
The embedded reasoning-effort prompts are adapted from the chat template of deepseek-ai/DeepSeek-V4-Flash-0731; DeepSeek remains the copyright holder of those prompt texts.
MoQ quantization method: "Mixture of Quantizations" proposed by Waleed Ahmad (https://huggingface.co/w-ahmad).
This model is provided as-is, without warranties of any kind, for research and local experimentation.

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Safetensors

Model size

36B params

Tensor type

BF16