--- language: - en - zh license: apache-2.0 tags: - unsloth - fine tune - heretic - uncensored - abliterated - ara - MTP GGUF Quants - Regular GGUF Quants - qwen3.6 - multi-stage tuned - thinking - reasoning - all use cases - coder - creative - creative writing - all genres - story - writing - fiction - roleplaying - bfloat16 - all use cases - multi-stage-tune - multi-state-merge datasets: - DavidAU/Polar-STRICT-Datasets - DavidAU/F451-STRICT-Datasets pipeline_tag: image-text-to-text --- Important: This is the first fine tune to exceed 700 "arc-c" (The OpenAI, Claude and Gemini "zone of intelligence") in both 8 bit and 4 bit. This repo contains both "regular" and "MTP" Neo MAX Imatrix quants.

Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF

The strongest, smartest open source multi-stage model fine tune for consumer hardware ever and BUILT on consumer hardware via Unsloth. The first model of this size/type to breach "700" ARC-C in both 8 bit and 4 bit; hench the "711" in the name. This model (both 4 bit and 8 bit) exceeds the base Qwen 3.6 27B in 6 out of 7 benchmarks, and matches it on the 7th AND exceeds all 7 benchmarks for Qwen3.6-35B-A3B. The 700 "intelligence club" is reserved for OpenAI, Claude and Gemini closed source models. This is the one they fear. This is a multi-stage fine tune, multi-fine tune, and multi-stage merge. A Colab between myself (multiple fine tunes, including multi-stage), Nightmedia (merge/benching), TeichAI (Polaris Dataset), armand0e (Light fable 5 traces) and trohrbaugh (heretic'ing the model). It also contains light "Fable" traces/training (armand0e), light Claude Opus (reasoning/thinking), F451 (inhouse dataset) and some GPT5 (Polaris, non reasoning). The strict goals of this model creation were: - Increase the general model intelligence and problem solving abilities. - DO NOT modify/damage or change the core model outside this goal. - ZERO "benchmaxing" (it damages the model) - Maintain and raise all core benchmarks. CORE MISSION:: Improve instruction following and problem solving. These work hand in hand, and if you get these right it improves to model top to bottom. It took a lot of tests on Qwen 3.5 9Bs to get the methods right. And it worked and can be used on other models too. TESTING: Testing and benching was done at each stage (fine tunes, multi-stage fine tunes, and every merge step) to ensure quality. Final testing was HUMAN testing. (you can also see benchmarks below too) Human testing means side by side testing of the base/org model and new model. Features: - Improved instruction following. - Overall increase in general intelligence and problem solving. - Better thinking/reasoning. - Even lower/lowest quants are exceptional. - Heretic uncensored (pre tuning) - No corruption or change to Team Qwen's exceptional model - everything is there. - Vision Regular and MTP GGUFS: All quants (regular and MTP) are NEO IMATRIX, which improve accuracy of the quants by an additional 2-4% over normal GGUFs as well as long context performance. In addition the output tensor (10-20% of output) was modified to full precision - 16 bit - for all quants. "MTP" GGUFS (multi-token prediction): - "MTP" GGUFS will have "MTP" in the name as a suffix. - I have also set the MTP tensors to Q8_0 precision for all quants. - To get better performance keep temp 1 or less (higher temps degrade MTP performance). - Likewise with rep pen ; keep at 1 (off). If you raise it performance will suffer. - If you see "token acceptance" rates BELOW 50% (predict 2 tokens) switch to normal quants. SPEED: - On Q4_K_S (4bit) quant, regular GGUFs are about 75 t/s, whereas MTP GGUFs (acceptance at 60%, 2 tokens) can exceed 90 T/S. (5090, Windows 11, testing in LMStudio) - Speeds will vary depending on GPU(s), AI app, O/S (Linux/Mac will generally be faster) and hardware. - "MTP" quants speeds will vary ; for creative/complex and/or temps over 1 use regular GGUFs for better performance. I suggest you download at least one of each - regular and MTP gguf(s) - and test them for your use case(s). If you get "token acceptance" (predict 2 tokens) with MTP quant(s) BELOW 50% (this means regular quants will run faster), then regular GGUF(s) will actually perform better - ie faster. MTP quant(s) can in some cases run faster as the token window fills up and/or in multi turn chats. Note there is NO other diffence between the quants type besides speed: both will do the same job. Model: - 256k context - Gguf quants run in all standard AI apps. - Vision is activated, but you need to download separate "mmproj" file (ONE) to use it. VISION: - Vision (images) tested. - You need an "mmproj" (just one) of these downloaded too, and placed in the same folder as the GGUF for images. Qwen Model Settings (suggested): - Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0 - Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0 - Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0 - Context window min from 8k to 16k. DE-CENSORING STATS Special thanks to: "trohrbaugh" for Heretic'ing the model. # This is a decensored version of [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0+custom with the [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) method ## Performance | Metric | This model | Original model ([Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)) | | :----- | :--------: | :---------------------------: | | **KL divergence** | 0.0469 | 0 *(by definition)* | | **Refusals** | 4/100 | 99/100 | ---

BENCHMARKS by Nightmedia

--- ``` arc/c arc/e boolq hswag obkqa piqa wino Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF [instruct mode] mxfp8 0.711,0.879,0.910,0.790,0.514,0.823,0.763 mxfp4 0.701,0.873,0.909,0.786,0.488,0.813,0.759 Qwen3.6-27B-Instruct: [base, non heretic] mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742 Qwen3.6-35B-A3B-Instruct [base, non heretic] mxfp8 0.581,0.757,0.892,0.751,0.428,0.803,0.688 Qwen3.5-27B-Instruct: [base, non heretic] mxfp8 0.557,0.711,0.868,0.533,0.452,0.706,0.695 ``` NOTES: - Models are tested in "Instruct" mode because this generally works better with the testing harness. - Testing via "thinking" mode also shows the metrics (and changes) but not the true extent. - In actual fact when the model IS in thinking mode, it will exceed INSTRUCT benchmark scores in most cases. --- # Qwen3.6-27B [![Qwen Chat](https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5)](https://chat.qwen.ai) > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. ## Qwen3.6 Highlights This release delivers substantial upgrades, particularly in - **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. ![Benchmark Results](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3.6/Figures/qwen3.6_27b_score.png) For more details, please refer to our blog post [Qwen3.6-27B](https://qwen.ai/blog?id=qwen3.6-27b). ## Model Overview - Type: Causal Language Model with Vision Encoder - Training Stage: Pre-training & Post-training - Language Model - Number of Parameters: 27B - Hidden Dimension: 5120 - Token Embedding: 248320 (Padded) - Number of Layers: 64 - Hidden Layout: 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN)) - Gated DeltaNet: - Number of Linear Attention Heads: 48 for V and 16 for QK - Head Dimension: 128 - Gated Attention: - Number of Attention Heads: 24 for Q and 4 for KV - Head Dimension: 256 - Rotary Position Embedding Dimension: 64 - Feed Forward Network: - Intermediate Dimension: 17408 - LM Output: 248320 (Padded) - MTP: trained with multi-steps - Context Length: 262,144 natively and extensible up to 1,010,000 tokens. ## Benchmark Results ### Language
Qwen3.5-27BQwen3.5-397B-A17BGemma4-31BClaude 4.5 OpusQwen3.6-35B-A3BQwen3.6-27B
Coding Agent
SWE-bench Verified 75.0 76.2 52.0 80.9 73.4 77.2
SWE-bench Pro 51.2 50.9 35.7 57.1 49.5 53.5
SWE-bench Multilingual 69.3 69.3 51.7 77.5 67.2 71.3
Terminal-Bench 2.0 41.6 52.5 42.9 59.3 51.5 59.3
SkillsBench Avg5 27.2 30.0 23.6 45.3 28.7 48.2
QwenWebBench 1068 1186 1197 1536 1397 1487
NL2Repo 27.3 32.2 15.5 43.2 29.4 36.2
Claw-Eval Avg 64.3 70.7 48.5 76.6 68.7 72.4
Claw-Eval Pass^3 46.2 48.1 25.0 59.6 50.0 60.6
QwenClawBench 52.2 51.8 41.7 52.3 52.6 53.4
Knowledge
MMLU-Pro 86.1 87.8 85.2 89.5 85.2 86.2
MMLU-Redux 93.2 94.9 93.7 95.6 93.3 93.5
SuperGPQA 65.6 70.4 65.7 70.6 64.7 66.0
C-Eval 90.5 93.0 82.6 92.2 90.0 91.4
STEM & Reasoning
GPQA Diamond 85.5 88.4 84.3 87.0 86.0 87.8
HLE 24.3 28.7 19.5 30.8 21.4 24.0
LiveCodeBench v6 80.7 83.6 80.0 84.8 80.4 83.9
HMMT Feb 25 92.0 94.8 88.7 92.9 90.7 93.8
HMMT Nov 25 89.8 92.7 87.5 93.3 89.1 90.7
HMMT Feb 26 84.3 87.9 77.2 85.3 83.6 84.3
IMOAnswerBench 79.9 80.9 74.5 84.0 78.9 80.8
AIME26 92.6 93.3 89.2 95.1 92.7 94.1

* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window. We correct some problematic tasks in the public set of SWE-bench Pro and evaluate all baselines on the refined benchmark.
* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 3h timeout, 32 CPU/48 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs.
* SkillsBench: Evaluated via OpenCode on 78 tasks (self-contained subset, excluding API-dependent tasks); avg of 5 runs.
* NL2Repo: Others are evaluated via Claude Code (temp=1.0, top_p=0.95, max_turns=900).
* QwenClawBench: A real-user-distribution Claw agent benchmark; temp=0.6, 256K ctx.
* QwenWebBench: An internal front-end code generation benchmark; bilingual (EN/CN), 7 categories (Web Design, Web Apps, Games, SVG, Data Visualization, Animation, and 3D); auto-render + multimodal judge (code/visual correctness); BT/Elo rating system.
* AIME 26: We use the full AIME 2026 (I & II), where the scores may differ from Qwen 3.5 notes.

### Vision Language
Qwen3.5-27BQwen3.5-397B-A17BGemma4-31BClaude 4.5 OpusQwen3.6-35B-A3BQwen3.6-27B
STEM & Puzzle
MMMU 82.3 85.0 80.4 80.7 81.7 82.9
MMMU-Pro 75.0 79.0 76.9 70.6 75.3 75.8
MathVista mini 87.8 -- 79.3 -- 86.4 87.4
DynaMath 87.7 86.3 79.5 79.7 82.8 85.6
VlmsAreBlind 96.9 -- 87.2 -- 96.6 97.0
General VQA
RealWorldQA 83.7 83.9 72.3 77.0 85.3 84.1
MMStar 81.0 83.8 77.3 73.2 80.7 81.4
MMBenchEN-DEV-v1.1 92.6 -- 90.9 -- 92.8 92.3
SimpleVQA 56.0 67.1 52.9 65.7 58.9 56.1
Document Understanding
CharXiv RQ 79.5 80.8 67.9 68.5 78.0 78.4
CC-OCR 81.0 82.0 75.7 76.9 81.9 81.2
OCRBench 89.4 -- 86.1 -- 90.0 89.4
Spatial Intelligence
ERQA 60.5 67.5 57.5 46.8 61.8 62.5
CountBench 97.8 97.2 96.1 90.6 96.1 97.8
RefCOCO avg 90.9 92.3 -- -- 92.0 92.5
EmbSpatialBench 84.5 -- -- -- 84.3 84.6
RefSpatialBench 67.7 -- 4.7 -- 64.3 70.0
Video Understanding
VideoMME(w sub.) 87.0 87.5 -- 77.7 86.6 87.7
VideoMMMU 82.3 84.7 81.6 84.4 83.7 84.4
MLVU 85.9 86.7 -- 81.7 86.2 86.6
MVBench 74.6 77.6 -- 67.2 74.6 75.5
Visual Agent
V* 93.7 95.8 -- 67.0 90.1 94.7
AndroidWorld 64.2 -- -- -- -- 70.3

* Empty cells (--) indicate scores not yet available or not applicable.

## Quickstart For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API. ### Serving Qwen3.6 Qwen3.6 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.6 models. > [!Important] > Inference efficiency and throughput vary significantly across frameworks. > We recommend using the latest framework versions to ensure optimal performance and compatibility. > For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended. > [!Important] > The model has a default context length of 262,144 tokens. > If you encounter out-of-memory (OOM) errors, consider reducing the context window. > However, because Qwen3.6 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities. #### SGLang [SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models. `sglang>=0.5.10` is recommended for Qwen3.6, which can be installed using the following command in a fresh environment: ```shell uv pip install sglang[all] ``` See [its documentation](https://docs.sglang.ai/get_started/install.html) for more details. The following will create API endpoints at `http://localhost:8000/v1`: - **Standard Version**: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs. ```shell python -m sglang.launch_server --model-path Qwen/Qwen3.6-27B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 ``` - **Tool Use**: To support tool use, you can use the following command. ```shell python -m sglang.launch_server --model-path Qwen/Qwen3.6-27B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder ``` - **Multi-Token Prediction (MTP)**: The following command is recommended for MTP: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3.6-27B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 ``` For detailed deployment guide, see the [SGLang Qwen3.5 Cookbook](https://lmsysorg.mintlify.app/cookbook/llm/Qwen/Qwen3.5). #### vLLM [vLLM](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference and serving engine for LLMs. `vllm>=0.19.0` is recommended for Qwen3.6, which can be installed using the following command in a fresh environment: ```shell uv pip install vllm --torch-backend=auto ``` See [its documentation](https://docs.vllm.ai/en/stable/getting_started/installation/index.html) for more details. The following will create API endpoints at `http://localhost:8000/v1`: - **Standard Version**: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs. ```shell vllm serve Qwen/Qwen3.6-27B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 ``` - **Tool Call**: To support tool use, you can use the following command. ```shell vllm serve Qwen/Qwen3.6-27B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder ``` - **Multi-Token Prediction (MTP)**: The following command is recommended for MTP: ```shell vllm serve Qwen/Qwen3.6-27B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}' ``` - **Text-Only**: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache: ```shell vllm serve Qwen/Qwen3.6-27B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only ``` For detailed deployment guide, see the [vLLM Qwen3.5 Recipe](https://docs.vllm.ai/projects/recipes/en/latest/Qwen/Qwen3.5.html). #### KTransformers [KTransformers](https://github.com/kvcache-ai/ktransformers) is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running Qwen3.6 with KTransformers, see the [KTransformers Deployment Guide](https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/Qwen3.5.md). #### Hugging Face Transformers Hugging Face Transformers contains a _lightweight_ server which can be used for quick testing and moderate load deployment. The latest `transformers` is required for Qwen3.6: ```shell pip install "transformers[serving]" ``` See [its documentation](https://huggingface.co/docs/transformers/main/serving) for more details. Please also make sure torchvision and pillow are installed. Then, run `transformers serve` to launch a server with API endpoints at `http://localhost:8000/v1`; it will place the model on accelerators if available: ```shell transformers serve Qwen/Qwen3.6-27B --port 8000 --continuous-batching ``` ### Using Qwen3.6 via the Chat Completions API The chat completions API is accessible via standard HTTP requests or OpenAI SDKs. Here, we show examples using the OpenAI Python SDK. Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.: ```shell pip install -U openai # Set the following accordingly export OPENAI_BASE_URL="http://localhost:8000/v1" export OPENAI_API_KEY="EMPTY" ``` > [!Tip] > We recommend using the following set of sampling parameters for generation > - Thinking mode for general tasks: `temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0` > - Thinking mode for precise coding tasks (e.g. WebDev): `temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0` > - Instruct (or non-thinking) mode: `temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0` > > Please note that the support for sampling parameters varies according to inference frameworks. > [!Important] > Qwen3.6 models operate in thinking mode by default, generating thinking content signified by `\n...\n\n` before producing the final responses. > To disable thinking content and obtain direct response, refer to the examples [here](#instruct-or-non-thinking-mode). #### Text-Only Input ```python from openai import OpenAI # Configured by environment variables client = OpenAI() messages = [ {"role": "user", "content": "Type \"I love Qwen3.6\" backwards"}, ] chat_response = client.chat.completions.create( model="Qwen/Qwen3.6-27B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=0.0, extra_body={ "top_k": 20, }, ) print("Chat response:", chat_response) ``` #### Image Input ```python from openai import OpenAI # Configured by environment variables client = OpenAI() messages = [ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg" } }, { "type": "text", "text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$" } ] } ] response = client.chat.completions.create( model="Qwen/Qwen3.6-27B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=0.0, extra_body={ "top_k": 20, }, ) print("Chat response:", chat_response) ``` #### Video Input ```python from openai import OpenAI # Configured by environment variables client = OpenAI() messages = [ { "role": "user", "content": [ { "type": "video_url", "video_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4" } }, { "type": "text", "text": "How many porcelain jars were discovered in the niches located in the primary chamber of the tomb?" } ] } ] # When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`, # video frame sampling can be configured via `extra_body` (e.g., by setting `fps`). # This feature is currently supported only in vLLM. # # By default, `fps=2` and `do_sample_frames=True`. # With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate. response = client.chat.completions.create( model="Qwen/Qwen3.6-27B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=0.0, extra_body={ "top_k": 20, "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True}, }, ) print("Chat response:", chat_response) ``` #### Instruct (or Non-Thinking) Mode > [!Important] > Qwen3.6 does not officially support the soft switch of Qwen3, i.e., `/think` and `/nothink`. Qwen3.6 will think by default before response. You can obtain direct response from the model without thinking by configuring the API parameters. For example, ```python from openai import OpenAI # Configured by environment variables client = OpenAI() messages = [ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/RealWorld/RealWorld-04.png" } }, { "type": "text", "text": "Where is this?" } ] } ] chat_response = client.chat.completions.create( model="Qwen/Qwen3.6-27B", messages=messages, max_tokens=32768, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, "chat_template_kwargs": {"enable_thinking": False}, }, ) print("Chat response:", chat_response) ``` > [!Note] > If you are using APIs from Alibaba Cloud Model Studio, in addition to changing `model`, please use `"enable_thinking": False` instead of `"chat_template_kwargs": {"enable_thinking": False}`. #### Preserve Thinking By default, only the thinking blocks generated in handling the latest user message is retained, resulting in a pattern commonly as interleaved thinking. Qwen3.6 has been additionally trained to preserve and leverage thinking traces from historical messages. You can enable this behavior by setting the `preserve_thinking` option: ```python from openai import OpenAI # Configured by environment variables client = OpenAI() messages = [...] chat_response = client.chat.completions.create( model="Qwen/Qwen3.6-27B", messages=messages, max_tokens=32768, temperature=0.6, top_p=0.95, presence_penalty=0.0, extra_body={ "top_k": 20, "chat_template_kwargs": {"preserve_thinking": True}, }, ) print("Chat response:", chat_response) ``` > [!Note] > If you are using APIs from Alibaba Cloud Model Studio, in addition to changing `model`, please use `"preserve_thinking": True` instead of `"chat_template_kwargs": {"preserve_thinking": False}`. This capability is particularly beneficial for agent scenarios, where maintaining full reasoning context can enhance decision consistency and, in many cases, reduce overall token consumption by minimizing redundant reasoning. Additionally, it can improve KV cache utilization, optimizing inference efficiency in both thinking and non-thinking modes. ## Agentic Usage Qwen3.6 excels in tool calling capabilities. ### Qwen-Agent We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to quickly build Agent applications with Qwen3.6. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python import os from qwen_agent.agents import Assistant # Define LLM # Using Alibaba Cloud Model Studio llm_cfg = { # Use the OpenAI-compatible model service provided by DashScope: 'model': 'qwen3.6-27b', 'model_type': 'qwenvl_oai', 'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1', 'api_key': os.getenv('DASHSCOPE_API_KEY'), 'generate_cfg': { 'use_raw_api': True, # When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way 'extra_body': { 'enable_thinking': True, 'preserve_thinking': True, }, }, } # Using OpenAI-compatible API endpoint. # functionality of the deployment frameworks and let Qwen-Agent automate the related operations. # # llm_cfg = { # # Use your own model service compatible with OpenAI API by vLLM/SGLang: # 'model': 'Qwen/Qwen3.6-27B', # 'model_type': 'qwenvl_oai', # 'model_server': 'http://localhost:8000/v1', # api_base # 'api_key': 'EMPTY', # # 'generate_cfg': { # 'use_raw_api': True, # # When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way # 'extra_body': { # 'chat_template_kwargs': {'enable_thinking': True, 'preserve_thinking': True} # }, # }, # } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/xxxx/Desktop"] } } } ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'Help me organize my desktop.'}] for responses in bot.run(messages=messages): pass print(responses) # Streaming generation messages = [{'role': 'user', 'content': 'Develop a dog website and save it on the desktop'}] for responses in bot.run(messages=messages): pass print(responses) ``` ### Qwen Code [Qwen Code](https://github.com/QwenLM/qwen-code) is an open-source AI agent for the terminal, optimized for Qwen models. It helps you understand large codebases, automate tedious work, and ship faster. For more information, please refer to [Qwen Code](https://qwenlm.github.io/qwen-code-docs/). ## Processing Ultra-Long Texts Qwen3.6 natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively., e.g., YaRN. YaRN is currently supported by several inference frameworks, e.g., `transformers`, `vllm`, `ktransformers` and `sglang`. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model configuration file: In the `config.json` file, change the `rope_parameters` fields in `text_config` to: ```json { "mrope_interleaved": true, "mrope_section": [ 11, 11, 10 ], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144, } ``` - Passing command line arguments: For `vllm`, you can use ```shell VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1010000 ``` For `sglang` and `ktransformers`, you can use ```shell SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1010000 ``` > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** > We advise modifying the `rope_parameters` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set `factor` as 2.0. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. **Sampling Parameters**: - We suggest using the following sets of sampling parameters depending on the mode and task type: - **Thinking mode for general tasks**: `temperature=1.0`, `top_p=0.95`, `top_k=20`, `min_p=0.0`, `presence_penalty=0.0`, `repetition_penalty=1.0` - **Thinking mode for precise coding tasks (e.g., WebDev)**: `temperature=0.6`, `top_p=0.95`, `top_k=20`, `min_p=0.0`, `presence_penalty=0.0`, `repetition_penalty=1.0` - **Instruct (or non-thinking) mode**: `temperature=0.7`, `top_p=0.80`, `top_k=20`, `min_p=0.0`, `presence_penalty=1.5`, `repetition_penalty=1.0` - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking. - **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. **Long Video Understanding**: To optimize inference efficiency for plain text and images, the `size` parameter in the released `video_preprocessor_config.json` is conservatively configured. It is recommended to set the `longest_edge` parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example, ```json {"longest_edge": 469762048, "shortest_edge": 4096} ``` Alternatively, override the default values via engine startup parameters. For implementation details, refer to: [vLLM](https://github.com/vllm-project/vllm/pull/34330) / [SGLang](https://github.com/sgl-project/sglang/pull/18467). ### Citation If you find our work helpful, feel free to give us a cite. ```bibtex @misc{qwen3.6-27b, title = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model}, author = {{Qwen Team}}, month = {April}, year = {2026}, url = {https://qwen.ai/blog?id=qwen3.6-27b} } ``` ---

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