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AtomicChat / Qwen3.6-27B-GGUF
Model's Last Updated: July 23 2026
text-generation
Introduction of Qwen3.6-27B-GGUF
Model Details of Qwen3.6-27B-GGUF
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 : the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector.
- 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.
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.
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 in the base model; text only in this repo, it ships no vision projector |
| Architecture |
Dense decoder, 24 attention heads over 4 KV heads,
Qwen3_5ForConditionalGeneration
|
| This repo |
GGUF quants (imatrix). Quants:
Q2_K
,
IQ3_M
,
Q3_K_M
,
Q3_K_L
,
IQ4_XS
,
Q4_K_S
,
Q4_K_M
,
UD-Q4_K_XL
,
Q5_K_S
,
Q5_K_M
,
Q6_K
,
Q8_0
|
Scores are Qwen's published results for the base
Qwen/Qwen3.6-27B
, not our own measurements. Quantization preserves the large majority of this;
Q4_K_M
and up stay close to full precision.
Choosing a quant
| Quant | Size | Notes |
|---|---|---|
Q2_K
|
10.7 GB | Smallest K-quant. Minimal RAM, clear quality drop. |
IQ3_M
|
12.6 GB | Beats Q3 at a similar size thanks to imatrix. Best low-RAM pick. |
Q3_K_M
|
13.3 GB | Low quality but usable. |
Q3_K_L
|
14.3 GB | A step above Q3_K_M. |
IQ4_XS
|
15.1 GB | Excellent quality for size. Recommended low-bit. |
Q4_K_S
|
15.6 GB | Compact 4-bit, fast. |
Q4_K_M
|
16.5 GB | Recommended default. Best balance of size, speed and quality. |
UD-Q4_K_XL
|
17.5 GB | Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint. |
Q5_K_S
|
18.7 GB | Higher quality, slightly more compact than Q5_K_M. |
Q5_K_M
|
19.2 GB | Higher quality, low loss. |
Q6_K
|
22.1 GB | Near lossless, noticeably lighter than Q8_0. |
Q8_0
|
28.6 GB | Effectively lossless, reference quality. |
Pick the largest file that fits your (V)RAM with room for context.
Q4_K_MorUD-Q4_K_XLis the sweet spot for most setups;Q6_KorQ8_0for maximum fidelity.
Get started
Run Qwen3.6 27B locally with:
-
Atomic Chat
:
the easiest path. Open the app, search
AtomicChat/qwen36-27b-GGUF, pick a quant, hit Use this model . -
llama.cpp:
llama-server -hf AtomicChat/qwen36-27b-GGUF:Q4_K_M --jinja -c 8192 -
Ollama:
ollama run hf.co/AtomicChat/qwen36-27b-GGUF:Q4_K_M - 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
.
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/qwen36-27b-GGUF:Q4_K_M \
--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. -
UD-Q4_K_XLadditionally pins the token-embedding and output tensors toQ8_0.
License
Original model by Qwen, released under the Apache 2.0 license. Full terms: Apache 2.0 . Quantized by Atomic Chat.
Runs of AtomicChat Qwen3.6-27B-GGUF on huggingface.co
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More Information About Qwen3.6-27B-GGUF huggingface.co Model
Url of Qwen3.6-27B-GGUF
Qwen3.6-27B-GGUF huggingface.co Url
Provider of Qwen3.6-27B-GGUF huggingface.co
Other API from AtomicChat
Updated:August 27 2026
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