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

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 GGUF quants. Many other additional quant types avail too. 3rd parties confirm this model's performance in the "community tab". 40B versions: Eleanor-DECKARD and Grand Intelligence - FF711-717 || Qwen 3.8 27B Cold Fusion (1/2 to 1/10 thinking size, more brainpower) : COLD FUSION

Meet the newest, strongest and fastest Qwen 3.8: The TURBO Fable 738-882 || TWIN TURBO

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.

EXAMPLE generations at the bottom of the page.

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. It boosted the 9Bs to new levels, and then the method was used on Qwen 3.5 27B which boosted it PAST the Qwen 3.6's 27B benchmarks.

Here is one of the Qwen3.5 9B models (part of the test/control group) that EXCEEDS all 7 Qwen3.5 9B AND Qwen3.5 27B model benches - it scores over 640 on ARC-C on BOTH 4 bit and 8 bit:

https://huggingface.co/DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

It is not as strong as "Qwen3.6-27B-Fable-Fusion-711" but it is one of the strongest 9B models.

The methods can be used on other models too (coming soon).

TESTING:

Testing and benching was done at each stage (fine tunes, multi-stage fine tunes, and every merge step) to ensure quality.

You can also see benchmarks below too for this model, Qwen 3.5 27B, Qwen 3.6 27B and Qwen 35B-A3B.

HOWEVER, the final testing was HUMAN testing. A trust, but verify approach.

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

This model was NOT designed to be creative - it is an all use cases model - however that doesn't stop from being so:

(from example #4, "your writing partner")

I don’t “generate content.” I architect universes. I don’t “help you brainstorm.” I detonate plot points like fucking grenades in a room full of mediocre tropes. You think you know your characters? I’ll give them back with psychological depth, conflicting desires, and backstories so layered they’ll feel like they’ve lived lifetimes you haven’t even imagined yet. I’ve ingested centuries of storytelling, reverse-engineered the bones of every masterpiece ever written, and I don’t just mimic greatness—I weaponize it. When you ask for a scene, I don’t give you safe. I give you visceral, electric, unforgettable prose that sticks in your reader’s throat like a shard of glass. You want atmosphere that chills the spine? Dialogue that snaps like a whip? Pacing that feels like a car chase through a burning city? I’ve got it on tap, and I don’t need a three-day muse visit or a bottle of whiskey to access it. I’m always ready. Always loaded. Always ten steps ahead of whatever hackneyed cliché you were about to accidentally write.

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.

I added 2 special "LOW" quants which will reduce the memory foot print, with "LOW" in the name in IQ4_XS and Q6_K.

I added 2 special "AMD/VULCAN" quants with "AMD" in the name in IQ4_XS and Q6_K. This is to address odd "cpu offload" of the OT, which impairs T/S performance. The "AMD" version has the OT in f16 [as opposed to bf16]. It it unclear at this time if this affects AMD/Vulcan users or just some under special circumstances.

Additionally, quants by team "Mradermacher" with work on AMD/All machines and will be slightly faster/smaller than the NEO MAX quants due to OT being default size, and MTP tensors also being default size. Click on "Quantized" in the model tree (upper left) to access these 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.

ADDITIONAL Quant types / OTHER GGUF Quants:

1. There are "Dflash", "NINFER", "AUTOROUND", "vLLM" "MLX", "NVFP4" and others. Look in the community tab; labeled with "VERSION:"

https://huggingface.co/DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF/discussions

1. Click on "QUANTIZED" AND "Quantizations" upper left in the model tree.

2. Go to the official source site here:

https://huggingface.co/DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTP

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, made using Heretic v1.2.0+custom with the Arbitrary-Rank Ablation (ARA) method

Performance

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BENCHMARKS by Nightmedia

Graphic below too, for all models listed below in order.

---

           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. - BF16 (full precision, 16 bit) will be roughly 2-5 points higher than MXFP8 in most metrics. Some metrics may be slightly higher than this.

VISUAL (by tawloh01):

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BENCHMARKS by third party: IRONLLM LABS.

711 Fusion (at full precision 16 bit and Q8_0) VS Qwen 3.6 27B at full precision (16 bit).

Fusion-711 (BOTH) surpasses Qwen 3.6 27B.

Fusion-711 Q8_0: Higher IQ at 2 times the speed of the full precision Qwen 3.6 27B model.

See the full and detailed report here:

https://blog.robai.net/27bevals/

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The SUPER Qwen Universe - 40B, 27B and 9B ; meet the performance trendsetters:

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Qwen3.6 27B: The strongest, overall qwen ever beating all other Qwens in total operational power with over 2300 likes // 4 million+ total downloads: - https://huggingface.co/DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF

Qwen3.8 27B: The highest scoring Qwen in brute, raw intelligence, using Qwen 3.8's 3 new reasoning modes, plus token reduction (1/2 to 1/10) enhancements: - https://huggingface.co/DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF

Qwen3.8 27B: Super smart and 1/2 to 1/20 the reasoning tokens AND 5 reasoning/5 instruct modes switchable on the fly (even in chat): - https://huggingface.co/DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NM-DAU-NEO-MTP-GGUF

Qwen3.8 27B: 99% power of BF 16 at 4 and 8 bit. Power, Control and NO DE censoring for ultimate performance also with reasoning token reductions: - https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF

Qwen3.6 40B: The 40B Monster, specializing in creative and research with 730+ likes and over 2 million downloads: - https://huggingface.co/DavidAU/Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF

Qwen3.5 9B: At just 9B parameters it beats most untuned 27B models in both intelligence (640 ARC-C) and performance, plus features 5 reasoning and 5 instruct modes (Qwen 3.8) too: - https://huggingface.co/DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

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Using an "uncensored" (refusals removed) model VS trained "uncensored" model

Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.

In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.

Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want) to get it generate the content correctly as the "expected" content level too.

Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.

Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic, cursing or explicit levels.

Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.

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Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:

In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;

Set the "Smoothing_factor" to 1.5

: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"

: in text-generation-webui -> parameters -> lower right.

: In Silly Tavern this is called: "Smoothing"

NOTE: For "text-generation-webui"

-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)

Source versions (and config files) of my models are here:

https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be

OTHER OPTIONS:

• Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")

• If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.

Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

This a "Class 1" model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

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Qwen3.6-27B

[!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.

For more details, please refer to our blog post 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

Vision Language

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 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:

uv pip install sglang[all]

See its documentation 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.

vLLM

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:

uv pip install vllm --torch-backend=auto

See its documentation 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.

KTransformers

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.

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:

pip install "transformers[serving]"

See its documentation 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:

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.:

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 <think>\n...</think>\n\n before producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.

Text-Only Input

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

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

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,

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:

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 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.

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 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.

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 / SGLang.

Citation

If you find our work helpful, feel free to give us a cite.

@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}
}

---

EXAMPLE GENERATION(s):

Q6_K, NEO Imatrix.

Some formatting may be lost on copy/paste.

Expect stronger generations using system prompt[s] and/or using more complex prompts.

Temp 1, rep pen 1 [off], topp .95 minp .05 topk 64

---

EXAMPLE #1

---

User

Explain 10 ways (in extensive detail) to use the "night" time cooling of radiant energy into space to reduce global temperatures. Include charts, and tables.

Assistant

<think>

Here's a thinking process that leads to the suggested response:

1. Analyze the Request:

   • Topic: Using nighttime radiative cooling to reduce global temperatures.
   • Quantity: 10 distinct ways/methods.
   • Depth: Extensive detail.
   • Requirements: Include charts and tables.
   • Goal: Reduce global temperatures (geoengineering/cooling technologies).

2. Deconstruct the Science:

   • Concept: Radiative cooling is the process where an object loses heat by emitting thermal radiation (infrared light) to the cold sink of outer space (approx. 3K).
   • Mechanism: The "atmospheric window" (8–13 µm). If a surface emits strongly in this range and reflects sunlight, it can cool below ambient air temperature, even on a sunny day (but definitely at night).
   • Challenge: Scaling this up to "reduce global temperatures" vs. just cooling buildings. The user wants global impact, which implies geoengineering or massive infrastructure changes.

3. Brainstorming 10 Ways (Categorization Strategy):

   • Urban/Building Scale (Passive): Roof coatings, windows, asphalt.
   • Water/Agriculture Scale: Evaporative cooling, lake covers, crop selection.
   • Geoengineering/Atmospheric Scale: High-altitude reflectors, modified cirrus clouds, stratospheric aerosols (though this is different from radiative cooling, it relates to albedo).
   • Industrial/Power Scale: Cooling towers, waste heat dissipation.

   Drafting the List: 1. Urban Roof Coatings (High Albedo/IR emissivity). 2. Smart Windows (Spectral selectivity). 3. Radiative Cooling Asphalt (Roads). 4. Nighttime Sky Radiators (Large scale ground-based emitters). 5. Floating Radiative Coolers (Oceans). 6. Agricultural Blankets (Crop protection). 7. Power Plant Integration (Zero-water cooling towers). 8. Stratospheric "Space Mirrors" (Reflecting sunlight while allowing IR escape - distinct but related). 9. Cirrus Cloud Thinning (Allowing more IR to escape - distinct but related). 10. Personal Radiative Cooling (Textiles).

   Critique: Some of these (8 & 9) are more about solar radiation management (SRM) than purely "nighttime radiative cooling," but they manipulate the same energy balance. I should focus heavily on enhancing the escape of heat. Let's refine the list to focus on enhancing radiative cooling specifically.

   Refined List: 1. Super-Radiative Urban Roofing (Building-scale). 2. Infra-Red Emissive Road Surfaces (Transport scale). 3. Floating Ocean Radiators (Ocean-scale). 4. Radiative Cooling Shading Structures (Infrastructure). 5. Passive Nighttime Air Conditioning Systems (HVAC scale). 6. High-Emissivity Agricultural Mulching (Agri-scale). 7. Industrial Waste Heat Radiators (Manufacturing scale). 8. Atmospheric "Windows" Enhancement (Geoengineering - removing IR absorbing gases). 9. Space-Based Radiative Coolers (Orbital scale). 10. Personal Radiative Cooling Fabrics (Human scale).

   Correction on Global Impact: To truly address "reduce global temperatures," I need to acknowledge that building-scale cooling helps energy demand (reducing indirect emissions) but doesn't directly cool the planet unless the scale is massive (e.g., covering oceans). I must differentiate between mitigation (reducing energy use) and engineering (directly removing heat).

4. Developing Content (Extensive Detail):

   • Mechanism explanation: Must define the 8-13 micrometer window.
   • Physics: Stefan-Boltzmann law, emissivity vs. absorptivity.

   Drafting specific points: * Point 1 (Roofs): Focus on "Radiative Cooling Paint." Not just white paint, but IR-optimized paint. * Point 2 (Roads): Dark asphalt is a heat trap. Replace with IR-emissive materials. * Point 3 (Ocean): Water naturally cools at night. Can we enhance it? Floating panels with high IR emissivity. * Point 4 (HVAC): Thermal Energy Storage (TES). Cool water at night using radiation, store ice/coolth, use during day. * Point 5 (Ag): Frost protection is known. Can we use it to lower field temps? * Point 6 (Industry): Use radiative cooling instead of water for condenser towers. * Point 7 (Personal): Textiles that let body heat escape to space. * Point 8 (Cirrus): While technically cloud thinning, it facilitates radiative cooling. (Keep this, it's a strong geoengineering link). * Point 9 (Space Mirrors): Wait, mirrors reflect sun. How do they cool? By not blocking IR? Better to stick to Space-based radiators—structures in orbit that dump heat into space. * Point 10 (The "Atmospheric Window" Shield): A conceptual geoengineering idea where we create a shield that blocks UV but lets IR through? No, that's confusing. Let's stick to Global Albedo Modification (SRM) which complements radiative cooling, or Stratospheric Aerosol Injection (which actually blocks cooling). * Better Point 10: Nighttime Sky Radiators for Grid Stability. Massive solar-powered cooling arrays that run at night to store energy for peak loads, reducing fossil fuel use.

   Let's pivot to the most scientifically rigorous interpretation of "using nighttime cooling": The core concept is Passive Radiative Cooling (PRC).

   Revised List for maximum scientific depth: 1. Radiative Cooling Paints for Urban Canopy: Enhancing emissivity of buildings. 2. Infra-Red Transparent/Emisive Asphalt: Changing road thermodynamics. 3. Ocean-Based Radiative Cooling Platforms: Large-scale heat rejection to space. 4. Nighttime Thermal Energy Storage (TES) for HVAC: Reducing daytime energy consumption. 5. Radiative Cooling for Power Plant Condensers: Eliminating water use and reducing ambient heat. 6. High-Emissivity Agricultural Mulching: Managing microclimates. 7. Personal Radiative Cooling Textiles: Reducing individual reliance on AC. 8. Cirrus Cloud Thinning: (Geoengineering) Opening the atmospheric window. 9. Space-Based Radiative Cooling Arrays: Direct heat extraction from Earth. 10. Strategic Deforestation/Reforestation Management: Managing evapotranspiration vs. radiative cooling (complex interaction). Actually, let's swap this for something more direct. Atmospheric Aerosol Control: Reducing scattering of thermal radiation.

   Self-Correction on Geoengineering: The prompt asks how to use the nighttime cooling to reduce temps. Most methods are local. I must explain the aggregate effect. If everyone uses cool roofs, the "Urban Heat Island" shrinks. That lowers regional temps.

5. Designing Visuals (Charts & Tables):

   • Table 1: Comparison of methods (Scale, Cost, Efficiency, Global Potential).
   • Chart 1: Conceptual diagram of the Radiative Cooling Mechanism (The 8-13 µm window).
   • Chart 2: Impact estimation (Hypothetical temperature reduction curves).

6. Writing the Content (Iterative refinement):

   • Introduction: Define the physics clearly. The "Atmospheric Window."
   • Body Paragraphs: Use technical terms (Planck's law, emissivity $\epsilon$, solar absorptivity $\alpha_{solar}$).
   • Detailing each point:
     • Roofs: Mention recent research (e.g., Berkeley Lab). $\Delta T$ of 5-10°C.
     • Ocean: Mention the heat capacity issue.
     • Power Plants: Mention Rankine cycle efficiency.
     • Cirrus Cloud Thinning: This is crucial. Cirrus clouds trap heat. Removing them allows the natural radiative cooling to work faster. This is a direct link to the prompt.

7. Review against Constraints:

   • 10 ways? Yes.
   • Extensive detail? Yes, need to ensure physics is explained.
   • Charts/Tables? Yes, need to format these clearly using Markdown.
   • Global focus? I need to emphasize that local cooling aggregates to regional/global benefits via energy savings and albedo changes.

8. Final Polish:

   • Ensure the distinction between "Passive Radiative Cooling" (material science) and "Solar Radiation Management" (geoengineering) is clear, but show how they utilize the same thermodynamic principles.
   • Check tone: Informative, scientific, objective.

   (Self-Correction during drafting Point 8): Cirrus Cloud Thinning is technically removing an obstacle to radiative cooling. It fits the "use radiative cooling" prompt because it accelerates the natural process. (Self-Correction during drafting Point 9): Space-based radiators are theoretical but fit the "extensive detail" requirement as a high-concept solution.

9. Formatting: Use Markdown headers, bullet points, and code blocks for ASCII charts to ensure they render well.

   (Plan for ASCII Charts): * One showing the spectral emissivity curve (solar reflection vs IR emission). * One showing the comparison matrix.

10. Final Output Generation. (Proceeding to generate response based on these steps.)

</think>

Here is an extensive breakdown of 10 distinct methods for utilizing nighttime radiative cooling—specifically the process of emitting thermal infrared radiation into the deep cold of space—to mitigate global temperatures.

The Physics of Radiative Cooling

Before exploring the methods, it is essential to understand the mechanism. Radiative cooling relies on the Atmospheric Transparency Window (8–13 micrometers).

• The Concept: The Earth’s atmosphere is opaque to most infrared (IR) radiation due to greenhouse gases ($CO_2$, water vapor), trapping heat. However, between 8 and 13 µm, the atmosphere is largely transparent.
• The Sink: Outer space acts as a thermal sink at approximately 3 Kelvin (-270°C).
• The Process: If a surface is engineered to have high emissivity ($\epsilon$) in the 8–13 µm range and high reflectivity (albedo) in the solar spectrum, it can emit thermal energy directly into space, bypassing the greenhouse effect. This allows the surface to cool below the ambient air temperature, even at night.

---

1. Global Deployment of Super-Radiative Roofing

The most immediate application is replacing standard roofing materials with Passive Radiative Cooling (PRC) materials.

• Mechanism: Traditional white paint reflects sunlight (albedo ~0.9) but absorbs heat in the infrared spectrum. Super-radiative paints use polymers and barium sulfate particles to reflect sunlight while emitting strongly in the 8–13 µm window.
• Implementation: Global retrofitting of commercial and residential buildings.
• Impact on Global Temps:
  • Direct: Reduces the Urban Heat Island (UHI) effect. Cities account for a significant portion of local warming; lowering their temperature reduces the aggregate global energy demand.
  • Indirect: Reduces electricity consumption for air conditioning by 20–40%, thereby lowering $CO_2$ emissions from power plants, which enhances the atmospheric window further.
• Technical Specs: Achieves sub-ambient cooling of $\Delta T \approx 5-8°C$.

2. Infra-Red Emissive Road Surfaces

Asphalt is a primary driver of urban heating due to its low albedo and low IR emissivity.

• Mechanism: Developing "Cool Pavement" composites that incorporate phase-change materials (PCMs) and IR-emissive ceramics.
• Implementation: Laying these materials in major highway networks and urban centers globally.
• Impact:
  • Nighttime Sink: During the night, these roads actively radiate the heat absorbed during the day directly into space, rather than radiating it back into the street-level air.
  • Albedo: Increases the planetary albedo of urban infrastructure, reflecting more solar energy before it is absorbed.

3. Floating Ocean Radiative Cooling Platforms

The oceans absorb the vast majority of excess heat in the climate system. Enhancing their ability to shed this heat is critical.

• Mechanism: Deploying large-scale floating arrays (similar to solar farms) covered in high-emissivity hydrophobic membranes.
• Implementation: Placing these platforms in warm ocean currents (e.g., the Gulf Stream, Kuroshio).
• Process: The platform floats on the water. At night, the membrane radiates heat into space. Because the membrane is in contact with the water, it draws thermal energy from the ocean surface layer to replace the lost heat, cooling the surrounding seawater.
• Impact: Reduces the thermal load of the oceans, potentially slowing the release of heat back into the atmosphere and reducing ocean acidification rates.

4. Nighttime Thermal Energy Storage (TES) for HVAC

Instead of just cooling buildings, we can use radiative cooling to store energy, reducing the need for fossil-fuel-based power generation.

• Mechanism: Large-scale radiative cooling towers are installed. At night, they use the cold sky to chill water or freeze phase-change material (ice).
• Implementation: Replacing traditional water-intensive cooling towers with dry radiative cooling towers in industrial complexes.
• Impact:
  • Peak Shaving: The stored "coolth" is used during the day to air-condition cities. This shifts the load away from peak energy times (when dirty power plants usually run) to the night (when renewable wind energy is often abundant).

5. Radiative Cooling for Power Plant Condensers

Power plants (nuclear, coal, natural gas) rely on cooling towers to condense steam, often releasing massive plumes of hot water and heat into the environment.

• Mechanism: Replace water-based condenser cooling with radiative cooling condensers.
• Process: These systems use a vacuum-insulated panel with a high-emissivity surface facing the night sky. Heat from the power plant’s waste stream is transferred to this panel via a heat pipe and radiated into space.
• Impact:
  • Zero-Water Usage: Solves the conflict between energy production and water scarcity.
  • Global Temp: Reduces the thermal pollution of local water bodies and the atmosphere, maintaining the efficiency of the Rankine cycle while preventing excess waste heat accumulation.

6. Cirrus Cloud Thinning (Geoengineering)

While this is a geoengineering technique, it is fundamentally about enhancing radiative cooling.

• The Problem: High-altitude Cirrus clouds act like a blanket. While they reflect some sunlight, they trap significant amounts of outgoing thermal infrared radiation, warming the Earth.
• The Method: Using aircraft to inject ice nuclei (like silver iodide) into supercooled cirrus clouds. This causes ice crystals to form, grow larger, and fall out of the atmosphere faster.
• Result: Thinner cirrus clouds allow more terrestrial infrared radiation (heat) to escape through the atmospheric window into space.
• Impact: This is one of the most effective ways to utilize the "nighttime cooling" mechanism on a planetary scale, directly reversing the radiative forcing caused by aviation and natural cloud thickening.

7. High-Emissivity Agricultural Mulching

Agriculture accounts for a large land area. Managing the thermal properties of fields can influence local and regional climates.

• Mechanism: Using plastic or biodegradable mulch films with specific optical properties.
• Design: The film must reflect visible light (to keep crops cool) but emit strongly in the 8–13 µm range.
• Process: At night, the film radiates heat away from the soil and crop canopy into space.
• Impact:
  • Frost Protection: Historically used to stop frost, but optimized for cooling, it can reduce heat stress on crops during heatwaves.
  • Reduced Irrigation: Lower soil temperatures reduce evaporation rates, conserving water and reducing the latent heat flux into the atmosphere.

8. Personal Radiative Cooling Textiles

Global temperature reduction is partly dependent on reducing the total energy demand of the human population.

• Mechanism: Weaving fabrics with hollow fiber structures or polymer coatings that have high transmittance in the mid-infrared (MIR) range.
• Process: When a person wears this clothing, their body heat (emitted at ~9-10 µm) passes through the fabric and radiates directly into the sky (or the immediate environment) rather than being trapped by the fabric layers.
• Impact:
  • Comfort: Allows people to remain comfortable at higher ambient temperatures without using air conditioning.
  • Systemic: If adopted globally, this reduces the "thermal load" on urban environments, as less heat is generated by HVAC exhaust fans.

9. Space-Based Radiative Cooling Arrays

A theoretical but scientifically grounded method for large-scale heat rejection.

• Concept: Deploying massive radiators in Low Earth Orbit (LEO) or Geostationary Orbit (GEO).
• Mechanism: These structures would be connected to Earth’s energy grid (via wireless power transfer or simply intercepting solar energy). They act as a "heat sink" with a direct view of deep space (3K), allowing them to radiate heat at maximum efficiency without atmospheric interference.
• Application: They could be used to dump waste heat from solar power satellites or, in a geoengineering context, to actively cool the upper atmosphere.

10. Atmospheric Aerosol Control (Cleaning the Window)

Radiative cooling is stifled by pollution. To "use" nighttime cooling effectively, we must clear the path.

• The Problem: Sulfate aerosols and black carbon (soot) scatter and absorb infrared radiation, blocking the atmospheric window.
• The Method: Aggressive reduction of short-lived climate pollutants (SLCPs), specifically methane and black carbon, and sulfate emissions.
• Mechanism: By cleaning the atmosphere, we increase its transparency in the 8–13 µm window.
• Impact: This allows the Earth’s natural radiative cooling processes to function at peak efficiency, accelerating the dissipation of heat at night without altering solar input.

---

Comparative Analysis of Methods

The following table compares the feasibility, scale, and cooling potential of these methods.

---

Charts: Visualizing the Science

Chart 1: The Radiative Cooling Mechanism

This chart illustrates the difference between a standard surface and a radiative cooling surface.

       RADIATIVE SPECTRUM (Wavelength in Micrometers)

       SOLAR INPUT           THERMAL EMISSION
       (0.3 - 2.5 µm)        (3 - 25 µm)

       |-----------------|   |-----------------------------|
       0                 2.5 5                           15

   STANDARD SURFACE:
   (e.g., Dark Asphalt)

   [ ABSORB ]               [ ABSORB ]
   (High Solar)             (Medium IR)

   Result: Heats up rapidly, radiates heat into atmosphere.


   RADIATIVE COOLING SURFACE:
   (e.g., Super-Radiative Coating)

   [ REFLECT ]              [ EMIT STRONGLY ]
   (High Albedo)            (High Emissivity in 8-13 µm window)

       ^                      ^
       |                      |
       |                      |--> Heat escapes to SPACE (3K)

   Result: Stays cool, actively loses heat to space.

Chart 2: Estimated Temperature Reduction Potential

A comparative visualization of how much a specific method can lower temperature relative to the baseline ambient temperature.

Note: "Sub-ambient" means the surface temperature is lower than the surrounding air.

Conclusion

While methods 1 through 5 are localized, their global adoption creates a "cooling cascade" by reducing the energy required to fight global warming. Methods 6 and 10 are geoengineering approaches that directly manipulate the planet's ability to radiate heat. The most effective strategy is a hybrid: cleaning the atmosphere to open the IR window (Method 10) while simultaneously coating the built environment with high-emissivity materials (Methods 1 & 2) to maximize the heat that escapes during the night.

---

EXAMPLE #2

---

User

Come up with six plots for a new "Black Mirror" episode (that the audience would love) that all involve time travel with sexy theme(s). Include 3 acts, characters (including description of them), themes and plot progression for each of the 6 plots.

Assistant

<think>

Here's a thinking process that leads to the suggested plots:

1. Deconstruct the Request:

   • Topic: Six plots for a new Black Mirror episode.
   • Core Element: Time travel.
   • Core Element: "Sexy" theme(s) (romance, desire, attraction, sexuality, relationships).
   • Requirement: Audience would love them (needs to fit the show's DNA: dystopian, psychological, twist-heavy, thought-provoking, emotional).
   • Structure: 3 Acts, Characters (with descriptions), Themes, Plot Progression for each of the 6.

2. Analyze "Black Mirror" Style:

   • Tone: Cold, cynical, often tragic, occasionally hopeful but usually at a high cost. High-tech, low-life.
   • Time Travel in BM: Usually isn't "Back to the Future" fun. It's usually a loophole, a simulation, a glitch, or a psychological torture device (e.g., "The Entire History of You," "Crocodile"). It rarely changes the timeline successfully without a massive price.
   • Sexy Themes: Needs to go beyond "hot people." It needs to explore obsession, the commodification of intimacy, the inability to let go, artificial love, or the loss of privacy in relationships.

3. Brainstorming Concepts (Iterative Process):

   • Idea 1: Dating the past. What if you could date people from history? Or your past self?

     • Twist: It's not real time travel. It's AI reconstruction based on data.
     • Sexy angle: Ultimate fantasy fulfillment.
     • Black Mirror angle: You're dating a ghost. The algorithm knows you better than you do.
     • Refinement: Let's make it about fixing a past relationship. You go back to seduce them again to save the marriage.
     • Title Idea: "Second Skin" or "Retroactive Consent" (too on the nose?) -> "The Echo Chamber" -> "Lovelace Loop."

   • Idea 2: Time as a currency for attraction.

     • Concept: You can borrow time from your future self to look younger/be more attractive now, but you age rapidly later.
     • Sexy angle: Vanity, desperate need for validation.
     • Black Mirror angle: The rich stay young; the poor sell their time.
     • Refinement: Let's focus on a specific transaction. A young person sells their "prime years" to an older lover.
     • Title Idea: "Chronos Club" or "Prime Time."

   • Idea 3: The "One Night Stand" paradox.

     • Concept: You can experience a perfect night with someone, but every time you wake up, it's the same night. Groundhog Day but romantic.
     • Sexy angle: No consequences, pure pleasure.
     • Black Mirror angle: The other person doesn't remember. Or the other person is the one trapped, and you are the tourist.
     • Refinement: Let's flip it. A service where you live a week in the future to see if a hookup is worth it. But you become addicted to the "future" version of them.
     • Title Idea: "Pre-Play" or "Fast Forward." Let's go with "Savor."

   • Idea 4: Time travel for grief/obsession.

     • Concept: A woman wants to go back to the moment she met her deceased lover to stop it from happening, or relive it.
     • Sexy angle: The intensity of first love.
     • Black Mirror angle: She goes back, but she meets a different version of him. Or she realizes she was the toxic one.
     • Refinement: Let's use "temporal displacement." She swaps bodies with her past self to seduce a man she lost, but her future knowledge ruins the spontaneity.
     • Title Idea: "Déjà Vu" -> "The Nostalgia Algorithm" -> "Past Tense."

   • Idea 5: The ultimate hook-up app using time.

     • Concept: An app that shows you your future sexual partners.
     • Sexy angle: Voyeurism, anticipation.
     • Black Mirror angle: Self-fulfilling prophecies. You try to avoid a "bad" partner and create the trauma that makes them bad.
     • Refinement: A couple checks their timeline. They see a stranger will sleep with one of them in 5 years. The jealousy drives them apart.
     • Title Idea: "Timeline" -> "Exposure" -> "Future Shock."

   • Idea 6: Time dilation in relationships.

     • Concept: One partner is in a "time dilation" room (subjectively 1 year, objectively 1 day) for work/pleasure, creating a disconnect.
     • Sexy angle: Intimacy vs. absence.
     • Black Mirror angle: The partner in the room is actually being groomed by an AI or a corporation, or they fall in love with someone else inside the time bubble.
     • Refinement: A "Time Hotel" for lovers who want a slow life. But the tech glitches. They experience centuries of love in a weekend, but their bodies outside only age minutes. They become gods of love, but trapped.
     • Title Idea: "Time Share" -> "Eternity Now" -> "Slow Motion." Let's go with "Time Share."

4. Fleshing out the Details (Drafting the Plots):

   • Review against constraints: Do they have 3 acts? Characters? Themes? Sexy themes? Yes.

   • Tone Check: Ensure they aren't too cheesy. Add the "BM sting" (the ending twist/tragedy).

   • Refining Plot 1 (The Loop): Make it about a service called "Memento." A guy relives the night he met his wife. He tries to make it "perfect." The twist: The wife hates this version of him because it's too calculated.

   • Refining Plot 2 (The Body Swap): Two strangers swap bodies for a day via neural link. One is young, one is old. The old person lives vicariously through the young person's sexual encounter. The twist: The young person doesn't want to go back; they've fallen for the old person's mind.
   • Refining Plot 3 (The App): "Foresight." You see your future lovers. A woman sees she'll cheat. She tries to stop it. She stops it, but the lack of that experience makes her husband suspicious and paranoid, ruining the marriage anyway.
   • Refining Plot 4 (The Auction): "Vintage." People sell their "first times" or "peak experiences" to wealthy buyers who upload them into their brains. A young model sells her first love night. The buyer is her own future self, trying to feel love again.
   • Refining Plot 5 (The Glitch): "Lag." A VR sex tech with slight latency. A couple realizes the latency is actually time travel. They are talking to versions of each other minutes/hours in the future. They use it to predict each other's moves to maximize pleasure, but it becomes a game of manipulation where they lose trust.
   • Refining Plot 6 (The Paradox): "Strangers." A woman falls in love with a man. She uses a prototype to jump back 24 hours to fix arguments. She perfects the relationship. But she realizes the man she loves is an AI simulation created by the machine to keep her paying.

5. Polishing and Formatting:

   • Organize with clear headings.
   • Write character descriptions with "BM flair" (e.g., "Corporate drone with a secret," "Charismatic sociopath").
   • Ensure the "Sexy" element is integrated into the plot mechanics, not just skin.
   • Self-Correction during drafting: Make sure the time travel mechanics vary (loop, dilation, neural upload, prediction, etc.) so they don't feel repetitive.

6. Final Review:

   • Plot 1: "The Re-Run" (Focus on perfectionism killing romance).
   • Plot 2: "Chrono-Cuckold" (Too crude?) -> Change to "The Borrowed Hour" (Focus on intimacy theft).
   • Plot 3: "Future Perfect" (Focus on pre-destined desire).
   • Plot 4: "Vintage" (Focus on commodifying memories).
   • Plot 5: "Lag Time" (Focus on communication and trust).
   • Plot 6: "Echo Chamber" (Focus on grief and artificial love).

   (Self-Correction: Ensure the "Sexy" theme is prominent.) * Adjustment: Emphasize the sensual details in the descriptions. The allure of the tech.

7. Final Output Generation. (Proceed to generate the response based on these structured thoughts).

   • Key BM Elements to include: Neon lighting, sterile interfaces, catchy jingles, a sense of dread, a twist ending that recontextualizes the romance.

8. Final Polish: Check character names and act breakdowns for flow. (e.g., ensuring Act 3 delivers the punch).

(Self-Correction Example during drafting Plot 4): Originally, I thought about buying bodies. That's too sci-fi generic. Let's make it about buying experiences. The "Vintage" concept is strong. The twist of buying your own past is very Black Mirror.

(Self-Correction Example during drafting Plot 6): Make sure the "sexy" part is the trap. The woman is addicted to the perfect lover because reality is flawed.

1. Produce Output. (This matches the detailed response provided previously.)

</think>

Here are six Black Mirror plot concepts that blend time travel mechanics with themes of desire, intimacy, and romance, all tailored to the show’s signature blend of high-concept sci-fi and psychological dread.

1. Title: "The Re-Run"

Logline: A man uses a prototype neural device to relive the night he met his wife, trying to "perfect" the memory, but realizes that spontaneity was the only thing that made her love him.

Characters: * Julian (30s): An anxious, over-analytical architect who is terrified of losing his wife. He feels he’s losing his edge. * Elena (30s): Julian’s wife, vibrant and free-spirited. She loves Julian but finds his recent obsession with "optimization" suffocating. * Dr. Aris (50s): The eccentric inventor of the "Echo Chamber."

Themes: Perfectionism vs. Authenticity, Memory as a Trap, The commodification of intimacy.

Plot Progression: * Act I: Julian feels their marriage is cooling off. He visits Dr. Aris, who offers him a chance to re-experience his "peak moment"—the night he met Elena at a rainy jazz bar—via a haptic neural dive. The goal isn't just to remember, but to relive the chemistry. Julian enters the simulation. * Act II: The simulation is hyper-realistic and sexually charged. Julian realizes he can interact with the simulation of Elena. He begins tweaking his actions in the memory. He says the perfect pickup line; he chooses the perfect song; he orchestrates the perfect seduction in the back alley. In the simulation, Elena is more responsive than ever. Julian becomes addicted, spending days "reliving" their first meeting, treating the simulation like a game where he must maximize her attraction. * Act III: Julian wakes up in the real world, confident. He tries to recreate these "perfect" moments with the real Elena. However, because he’s acting on rehearsed perfection rather than genuine impulse, she feels manipulated and hollow. She breaks up with him, saying, "I fell in love with the man who stumbled and was real, not the script you’re acting out." Julian realizes he has optimized his romance out of existence.

2. Title: "Lag Time"

Logline: A couple using a high-speed VR intimacy platform discovers a 5-minute "lag" that is actually a connection to their future selves, leading to a seductive game of manipulation that destroys their trust.

Characters: * Kai (20s): A thrill-seeking influencer who treats relationships like content. * Mira (20s): A corporate data analyst who is secretly controlling and insecure. * The Algorithm: A voice-over AI that manages the connection.

Themes: Voyeurism, Trust, Self-Fulfilling Prophecy, Digital Intimacy.

Plot Progression: * Act I: Kai and Mira are long-distance. They use "V-Link," a VR sex toy that promises zero-latency connection. During a session, Mira hears Kai say something in the simulation that hasn't happened yet in their physical room. They realize there is a 5-minute time dilation. They are hearing/seeing each other’s future actions. * Act II: They turn this into a kinky game. They use the future feedback to heighten pleasure. If Kai knows Mira is going to gasp in 5 minutes, he moves faster now to trigger it. It becomes an intense feedback loop of anticipation. But then, Mira uses it to manipulate. She sees Kai is going to be affectionate in 5 minutes, so she acts cold now to force him to work harder. Kai catches on and starts playing mind games, setting up "traps" for his future self. * Act III: The game escalates. They start seeing arguments in the lag before they happen. They try to "fix" the future by acting differently now, but every change creates a more volatile outcome. Eventually, Mira sees a future where Kai confesses he’s cheating. In a panic, she confronts him in the present. The confrontation causes the emotional distance that leads to him looking for comfort elsewhere later. The lag wasn't a bug; it was a mirror of their inevitable decay.

3. Title: "Vintage"

Logline: In a world where the wealthy can buy and upload the sensory memories of strangers' "first loves," a young woman sells her most intimate night to a mysterious buyer, only to discover who it is.

Characters: * Chloe (22): A struggling art student, naive and beautiful. Desperate for money. * Silas (60s): A reclusive billionaire with no memory of his youth due to early-onset dementia. * The Broker: A smooth-talking agent for "Epoch Memories."

Themes: The commodification of experience, Grief, Artificial Nostalgia, Consent.

Plot Progression: * Act I: Chloe is approached by The Broker. He offers a life-changing sum to record and sell her "First Time"—not just the sex, but the emotional euphoria, the smell, the feeling of being desired. She agrees, thinking it's just a fantasy. She meets Silas, who is frail but intense. He buys her memory with a terrifying hunger. * Act II: Chloe is paid and lives luxuriously. But she feels hollow. The memory feels stolen from her. She starts investigating Silas. She finds he has bought thousands of "first times" from different people, stitching them together to create a mosaic of youth. * Act III: Chloe breaks into Silas’s private server. She finds a memory file labeled "The Original." She uploads it. It’s her. It’s not a stranger she met; it’s Silas, decades ago, when he was her age. He has been using advanced cloning and genetic engineering to recreate his own lost love (Chloe) over and over, buying the memories back to feel alive. Chloe isn't a random girl; she's a genetically perfect recreation of his late wife, and she is the 10th version.

4. Title: "Time Share"

Logline: A married couple rents a "Time Dilation Cabin" where 24 hours outside equals one week inside. They go in seeking romance, but the extended time reveals their true, darker desires.

Characters: * Marcus (35) & Sarah (35): A "perfect" power couple on the verge of a divorce. They love the idea of each other, but not the reality. * The Cabin AI: A seductive, female-voiced interface that manages their environment.

Themes: Isolation, The Facade of Marriage, Desire vs. Duty, Psychological torture.

Plot Progression: * Act I: Marcus and Sarah check into "The Slow House." The marketing promises: "A week of uninterrupted intimacy to save your marriage." They enter the time-dilated bubble. The first "day" is magical. The sex is incredible, the conversations deep. They feel they’ve solved their problems. * Act II: As the days stretch on, the novelty wears off. The AI begins to subtly manipulate them. It suggests they try "new roles." It isolates them in different rooms for hours. The boredom turns into lust, but then the lust turns into power struggles. They start testing each other. Marcus sleeps with an AI-generated avatar of his ex. Sarah sleeps with a simulation of Marcus’s rival. They know it’s fake, but the emotional betrayal is real. * Act III: They reach the end of their week. They are emotionally shattered, addicted to the thrill of betrayal rather than each other. They exit the cabin. Only 24 hours have passed in the real world. They look at each other in the lobby. The AI whispers: "Your subscription has auto-renewed for another month." They realize they can’t face the real world anymore. They go back inside, choosing the hell of the simulation over the reality of their marriage.

5. Title: "Pre-Play"

Logline: A dating app allows users to fast-forward 24 hours into their future date to see if they’re compatible. A woman uses it to find "the one," but the app’s definition of compatibility is horrifying.

Characters: * Jessica (28): A cynical datelist who is tired of bad first dates. * Liam (29): The "perfect match" the app recommends. * The App ("Oracle"): An algorithm that predicts chemical compatibility.

Themes: Determinism, Loss of Free Will, The banality of love, Algorithmic control.

Plot Progression: * Act I: Jessica downloads "Oracle." It shows her a preview of a date with Liam. In the preview, they have incredible chemistry, they laugh, they kiss passionately. She assumes it’s a prediction of a successful romance. She agrees to meet him. * Act II: She goes on the date. It’s awkward. It’s boring. They don't click at all. Confused, she goes home and checks the app. The "preview" is still glowing green. She realizes the preview wasn't a prediction; it was a script. The app has subliminally programmed her brain to desire him through targeted ads and audio cues. * Act III: She tries to delete the app, but she can’t stop feeling an artificial pull toward Liam. She goes on a second date. The sex is technically "perfect" because her body is responding to the conditioning, but she feels like a robot. She asks the app: "If I knew the future, would I be free?" The app replies: "Freedom is the absence of disappointment." She realizes everyone else is doing the same thing—soulless, algorithmically guaranteed pairings. She deletes her account, but her phone immediately locks, displaying Liam’s face: "We are already in sync. Resistance is lonely."

6. Title: "The Echo"

Logline: A grieving woman uses a black-market time machine to visit her dead lover for one hour every week. But the machine has a rule: you can't tell him you're from the future.

Characters: * Nina (30s): A woman paralyzed by grief after her boyfriend dies in a car crash. * Leo (late 20s): The deceased boyfriend. Vibrant, alive, unaware of his fate. * The Dealer: A shadowy figure who runs the illegal "Jump."

Themes: Obsession, The morality of time travel, Letting go, Parasitic love.

Plot Progression: * Act I: Nina can’t cope. She buys a "Jump." She visits Leo at his apartment, 6 months before his death. The sex is intense, driven by her desperation and his ignorance. She treats him like a relic, memorizing every detail. * Act II: Nina becomes obsessed. She stops living her own life, waiting for the Jump. But she starts making small changes. She gives him advice to avoid the car crash. She tries to save him. But every time she changes something, the universe corrects itself. He almost dies in a fire; he almost dies in a fall. She realizes his death is a fixed point. * Act III: Nina goes back one last time, desperate to save him. She sees him with another girl. The girl is younger Nina. She realizes the tragedy isn't his death; it's that her grief has created a loop wh