10
stars
16
commits
2
linked in READMEs
Jul 22, 2026
updated
README
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
--jinjaso 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.ggufvision projector. With-hfit is pulled automatically; otherwise pass--mmproj. Usellama-mtmd-cliorllama-serverto 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
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/Qwen3.6-27B-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.
Similar repos
10
stars
16
commits
2
linked in READMEs
Jul 22, 2026
updated
README
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
--jinjaso 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.ggufvision projector. With-hfit is pulled automatically; otherwise pass--mmproj. Usellama-mtmd-cliorllama-serverto 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
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/Qwen3.6-27B-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.