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AtomicChat/
Qwen3.6-27B-UDT-MTP-GGUF

Name: Qwen3.6-27B-UDT-MTP-GGUF
Availability: InStock

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Base model: Qwen/Qwen3.6-27B

Qwen3.6 27B, 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

27.8B parameters: the weights this repo quantizes.
Context length: 262,144 tokens (256K), as published by Qwen.
64 layers: Dense decoder.
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 27B chat template is applied. Without it the model can emit malformed turns.

Model Overview

Property	Value
Base model	`Qwen/Qwen3.6-27B`
Parameters	27.8B
Layers	64
Context length	262,144 tokens (256K)
Vocabulary	248,320
Modalities	Text, Image
Architecture	Dense decoder, 24 attention heads over 4 KV heads, `Qwen3_5ForConditionalGeneration`
This repo	GGUF quants (imatrix) and a vision mmproj

[!NOTE] Qwen3.6 27B 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 27B locally with:

Atomic Chat: the easiest path. Open the app, search AtomicChat/Qwen3.6-27B-UDT-MTP-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AtomicChat/Qwen3.6-27B-UDT-MTP-GGUF:None --jinja -c 8192
Ollama: ollama run hf.co/AtomicChat/Qwen3.6-27B-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/Qwen3.6-27B. Pass images through llama-mtmd-cli or llama-server with the projector.

Run in llama.cpp

bash

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

bash

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/Qwen3.6-27B-UDT-MTP-GGUF:None \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

Download Qwen/Qwen3.6-27B (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus.
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.