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:
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("quimmedes/Deepwen-3.6-bf16", torch_dtype="bfloat16")
4tok = AutoTokenizer.from_pretrained("quimmedes/Deepwen-3.6-bf16")
To enable advanced thinking at maximum effort:
1{
2 "messages": [{"role": "user", "content": "..."}],
3 "chat_template_kwargs": {"reasoning_effort": "max"}
4}
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
- Base model: Qwen/Qwen3.6-35B-A3B — Copyright © Alibaba Group / Qwen Team. All rights to the base model and its weights remain with the original authors.
- 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.
