quimmedes / Deepwen-3.6

huggingface.co
Total runs: 13.2K
24-hour runs: 0
7-day runs: 181
30-day runs: 13.1K
Model's Last Updated: August 10 2026
text-generation

Introduction of Deepwen-3.6

Model Details of Deepwen-3.6

Deepwen 3.6

Deepwen 3.6 is a fine-tuned derivative of Qwen/Qwen3.6-35B-A3B (Mixture-of-Experts, ~35B total / ~3B active), specialized for AAA GameDev 3D production workflows : procedural geometry, hard-surface shape language, and Blender/Unreal asset pipelines.

What the model has
  • Advanced thinking (DeepSeek style) — the model reasons before it answers. Its thinking comes from two sources:
    • Supervised reasoning training : 93.6% of its reasoning-focused training examples (103/110) carry a full reasoning chain as part of the target.
    • 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").
  • 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.
Improvements over the base model

Paired evaluations on held-out tasks (same server, same seeds):

Capability Improvement
Procedural generation blockout_gate : PARTIAL → PASS ; conditional_lightmap : FAIL → PASS ; 5 units improved vs 2 regressed
Replay safety base competence suite 6/6 intact (no regressions)
Shape / hard-surface no catastrophic flips across held-out objects
Blender gear recipe modifier_workflow, tooth_profile_generation, game_prep_uv_pbr, non_destructive_order
Lighting layered lighting legibility (bounce and ambient)
Quantizations (MoQ)

All files quantized with the Mixture of Quantizations (MoQ) method proposed by Waleed Ahmad : per-tensor type selection (attention/embeddings at higher precision, MLP/experts at more aggressive types) instead of a single type for every tensor.

File Approx. size Notes
Deepwen-3.6-Q2-MoQ.gguf ~10 GB aggressive MoQ mix
Deepwen-3.6-Q3-MoQ.gguf ~13 GB 3.0 bpw target
Deepwen-3.6-Q4.5-MoQ.gguf ~21 GB 4.5 bpw target, sweet spot for local use
Deepwen-3.6-Q5-MoQ.gguf ~22 GB 5.0 bpw target
Deepwen-3.6-Q6-MoQ.gguf ~27 GB
Deepwen-3.6-Q8-MoQ.gguf ~36 GB near-lossless
Deepwen-3.6-BF16.gguf ~70 GB original merged weights, bf16
Usage
llama-server -m Deepwen-3.6-Q4.5-MoQ.gguf --host 0.0.0.0 --port 8080
# OpenAI-compatible: /v1/chat/completions

To enable advanced thinking at maximum effort:

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

Works with llama.cpp (b3050+), LM Studio, Ollama, Jan.

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.

Runs of quimmedes Deepwen-3.6 on huggingface.co

13.2K
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24-hour runs
80
3-day runs
181
7-day runs
13.1K
30-day runs

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https://huggingface.co/quimmedes/Deepwen-3.6

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