AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF overview

---

license: apache-2.0

license_link: https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE

thumbnail: https://huggingface.co/AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF/resolve/main/hero.png

base_model:

• Qwen/Qwen3.6-35B-A3B

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: image-text-to-text

library_name: gguf

tags:

• atomic-chat
• qwen3.6
• qwen
• gguf
• llama.cpp
• quantized

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<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>

<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>

<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>

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<img src="https://huggingface.co/AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF/resolve/main/hero.png" alt="Qwen3.6 35B A3B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/Qwen/Qwen3.6-35B-A3B"><strong>Base model: Qwen/Qwen3.6-35B-A3B</strong></a>

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Qwen3.6 35B A3B, self-quantized to GGUF by Atomic Chat. Built straight from Qwen's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

• 36.0B parameters: the weights this repo quantizes.
• Context length: 262,144 tokens (256K), as published by Qwen.
• 40 layers: Mixture-of-Experts.
• Modalities: Text, Image.
• Full imatrix ladder: every quant is calibrated with an importance matrix.
• Agentic Coding:: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
• Thinking Preservation:: we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.

> [!NOTE]

> These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

> [!IMPORTANT]

> Always pass --jinja so the Qwen3.6 35B A3B chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | Qwen/Qwen3.6-35B-A3B |

| Parameters | 36.0B |

| Layers | 40 |

| Experts | 256 routed (top-8) |

| Context length | 262,144 tokens (256K) |

| Vocabulary | 248,320 |

| Modalities | Text, Image |

| Architecture | Mixture-of-Experts, 256 experts (top-8), 16 attention heads over 2 KV heads, Qwen3_5MoeForConditionalGeneration |

| This repo | GGUF quants (imatrix) and a vision mmproj |

> [!NOTE]

> Qwen3.6 35B A3B is multimodal. This repo ships the mmproj-BF16.gguf vision projector. With -hf it is pulled automatically; otherwise pass --mmproj. Use llama-mtmd-cli or llama-server to feed images.

Get started

Run Qwen3.6 35B A3B locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF:None --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF:None
• LM Studio / Jan: search the repo id, download any quant.

Best practices

| Parameter | Value |

|---|---|

| temperature | 1.0 |

| top_p | 0.95 |

| top_k | 20 |

| min_p | 0.0 |

| repetition_penalty | 1.0 |

Qwen's recommended sampling configuration for Qwen/Qwen3.6-35B-A3B. Pass images through llama-mtmd-cli or llama-server with the projector.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/Qwen3.6-35B-A3B-UDT-MTP-GGUF:None \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download Qwen/Qwen3.6-35B-A3B (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus.
4. Quantize the ladder with --imatrix.

License

Original model by Qwen, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.