--- license: apache-2.0 base_model: - Qwen/Qwen3.8-27B pipeline_tag: image-text-to-text tags: - unsloth - GAIN Training - COLD-FUSION - finetune library_name: transformers --- [DavidAU] FOLLOW | ALL MODELS | COLLECTIONS | TRENDING | FAQs | SUPPORT | Buy Me A Coffee (😁) GGUFS, examples, how to modify reasoning settings (jinja) and detailed model card are here: https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF --- IMPORTANT: The COLD FUSION (GAIN+Unsloth) method of training maintains 99% of performance of BF16, at both 8 bit and 4 bit levels. FINALS STATUS - WE HAVE A WINNER - GGUFs RELEASED: - FINALS: Benching of "1B", finals, prep for GGUF release. Outstanding performance at 4 bit at xhigh, med and low settings. All the power of the new Qwen 3.8, at 1/10 to 1/2 the thinking/output generation. - 1B is the winner, by a landslide. Perfect generation (and thinking token reduction 1/10 to 1/2) at xhigh, med and low settings too. - 1B maintains/exceeds Qwen 3.8 detail level, even at "low" reasoning effort. - Model's natural mode of operation : High detail, sharp robust thinking then get the job done. Automatically uses minimal "verbage" with max detail in output too. - "Cook 1" already exceeded all 7 core Qwen 3.8 benchmarks (posted below) ; we expect "COOK 1B" (same training/datasets as "COOK 1") to be equal to these, maybe higher. - MTP speed (q4ks, non imatrix, no caching) : 91 T/S (5090), with 55.7% token MTP acceptance; RECORD: 100 t/s, 59.9% token MTP acceptance. - Generations to be posted from test quant shortly ; awaiting final benches. - Prepping NEO Imatrix datasets for GGUF (MTP and regular) release. - Source release will follow GGUF release. STATUS: - IT IS COOKING... #1 done, now in testing [test results/generation(s) below ]] || initial benches posted ("NEEDLE": MOVED) ... - 2nd cook in progress, to assess / refine detail levels in reasoning/output. Cook #2/2B complete. In testing/benching. - Cook of #1B in progress: redo with adjustments to address issues related to Jinja/reasoning prompt injection "xhigh" (cook #1 works great, test to see if 1B is better). Now in testing / will be benched. - 2 strong candiates ATM, in final testing/revision/adjustments. - More details below. --- COLD FUSION? This model uses the COLD FUSION (GAIN+Unsloth) fine tuning and training methods as noted here: --- https://huggingface.co/DavidAU/Qwen3.6-27B-V1.1-FF711-Darker-Hero-GAIN-H2.0 https://huggingface.co/DavidAU/Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF And the OFF THE SCALE version: 2000+ likes, 2.9 million+ downloads, 71 quant repos, exceeds all Qwen 3.6 27B performance levels AND metrics (confirmed by third party testing): https://huggingface.co/DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF --- About this model: Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 [working title] --- This training is about assessing root Qwen3.8-27B and a trained version on known datasets using the COLD FUSION (GAIN+UNSLOTH) training tech which was invented by my team during the R & D of "Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic". The "GAIN" is the core invented component, then coupled with Unsloth's trainers/systems => AKA -> COLD FUSION. The training (datasets) itself is focused on raising general intelligence of the model and reducing thinking tokens to 1/10 to 1/2 of "normal qwens". This is a very light, but strongly focused tune. The reduction in thinking tokens may need additional balancing to maintain output level detail - this is unclear at the moment. Qwen 3.8-27B, and trained version[s] of Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 will be benched, compared and human tested. A second "cook" is in progress to address/assess thinking/output specifically detail levels in both VS untuned Qwen 3.8. This is prep for more advanced Fable Fusion 711 27B "3.8" version(s) ; which has a far more complex and longer training pipeline (6 stages, with multiple sub-stages) and takes 7-10 days to "run"; run time is due to hardware limits. Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic is an example a model built using this pipeline, as well as the TWO 40B Qwen 3.6s ("Eleanor" (10 stages), and "Grand Intelligence" (9 stages)) versions and the newest (soon to be released) "Qwen3.6-27B-V1.1-FF711-Darker-Hero-GAIN-H2.0" (working title, 8 stages). ---

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

--- 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 --- Timeline for "Qwen3.8-27B-Cold-Fusion-GAIN-V1.1": - DONE: Primary cook[s]. ; 1st complete ; primary testing next. || maybe additional "cook(s)". - DONE: BENCHES: Primary cook, Qwen 3.8 27B non trained, and 3.5/3.6 to compare... - DONE: Second "cook" to compare reasoning adjustments and detail levels in thinking/output and REFINE if required. - DONE: Assessment of "cook #2" / 2B, testing, and comparing to build 1/untrained Qwen 3.8-27B - DONE: Cook 1B to address/test issues related from jinja reasoning injection related to "xhigh" / "system prompt" - DONE: Assessing Cook 1 and 1B / 2B ; finals ; next step final tweaking/GGUF release. - DONE: Adjustment(s). - DONE: GGUF repo(s). - DONE: Release of source. - IN PROGRESS: Additional "HERETIC" version (from BASE, Qwen 3.8 untrained model) being built then will be trained too. This will be addressed in future version(s). Prelim Testing Results (training #1, CP #1) // "1B": Cold Fusion GENERATION(S) from TESTING (quant) below. - Drastic reduction in thinking tokens / "caveman" talk. - Dropped to 1/10 to 1/2 number of tokens for thinking. - Zero "wait" and "hesitate" in thinking block. - Output is clean and organized. - Stable. No issues. No looping. - PPL dropped vs BASE Qwen3.8 27B (indicator of COLD FUSION training; normal training: PPL stays the same/rises) 1B: - In progress, address/test oddball jinja injection of "system prompt" due to "xhigh" settings. - "1" works great, see if "1B" works better. - 1B is off the scale. Winner by a landslide. NOTES: - Tested both Qwen 3.8-27B and trained version in Q4KS, non imatrix, same settings, "max thinking mode" (default) - 3 generations each for both models, same prompt to assess function. - Thinking block/output is assessed in terms of function and quality - especially detail level, and language. ---

BENCHMARKS by Nightmedia

--- Important note on Qwen 27B 3.8 bench VS Qwen 3.6/3.5 27B versions: Based on my testing / Qwen's own statements, community statements (ie localllama) and extended benchs for 3.8-27B version (team Qwen) this model is more focused on deeper thinking, coding and agentic functions than previous Qwen versions. ``` arc/c arc/e boolq hswag obkqa piqa wino Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 [non heretic, cook 1B - FINAL PUBLISED] mxfp8 0.655,0.838,0.898,0.751,0.498,0.807,0.738 mxfp4 0.645,0.833,0.887,0.740,0.496,0.799,0.732 Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 [non heretic, cook #1] mxfp8 0.657,0.830,0.900,0.751,0.498,0.807,0.738 mxfp4 0.644,0.829,0.899,0.740,0.496,0.799,0.732 --- UNTUNED Qwen3.8-27B-Instruct: [base, non heretic] mxfp8 0.591,0.782,0.896,0.746,0.448,0.801,0.711 mxfp4 0.581,0.771,0.889,0.738,0.442,0.798,0.713 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. ---

EXAMPLE GENERATION(S):

Q4KS, non imatrix, standard Qwen settings, NO cache compression of any kind. NOTE: Some formatting may be lost on copy/paste/export. --- Example Generation is from BUILD/COOK #1. For examples of COOK 1B - the final winner - see the above "GGUF" repo. --- (Average size including thinking 5k to 7k ; normal Qwen exceeding 16k)



PROMPT:

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



Thought for 22.38 seconds

The user is asking about using radiative cooling to space (specifically nighttime cooling of radiant energy into space) as a method to reduce global temperatures. This is a legitimate and interesting topic in climate science and engineering. Let me think through this carefully.

Radiative cooling to space works by emitting thermal radiation in the atmospheric transparency window (roughly 8-13 micrometers), which passes through the atmosphere to space. The sky is much colder than the ground (about 5-10K effective temperature difference at night), so objects can cool below ambient air temperature. This is a passive cooling technology.

Let me think of 10 distinct and detailed ways this could be applied to reduce global temperatures or mitigate heat:

  1. Large-scale radiative cooling arrays/mirrors - Deploying vast areas of radiative cooling materials or mirrors on the ground or in the atmosphere to enhance outgoing longwave radiation (OLR) to space. This could be done by:

  2. Space-based radiative cooling panels/mirrors - Deploying materials in space (e.g., LEO) that absorb terrestrial longwave radiation and re-emit it to space, effectively acting as a "heat dump" for Earth's excess heat:

  3. Enhanced atmospheric window emissions via aerosol/chemical modification - Modifying the atmosphere to be more transparent in the 8-13μm window, allowing more of Earth's thermal radiation to escape:

  4. Building-integrated radiative cooling systems - Scaling up radiative cooling for buildings and infrastructure:

  5. Geothermal-radiative cooling hybrid systems - Combining ground heat exchange with radiative cooling:

  6. Ocean surface radiative cooling - Deploying radiative cooling materials on ocean surfaces:

  7. Nighttime "cooling towers" using radiative cooling - Designing structures that enhance nighttime radiative cooling:

  8. Ice/snow albedo enhancement combined with radiative cooling - Using radiative cooling to help maintain and enhance ice/snow cover:

  9. Radiative cooling-enhanced geothermal energy systems - Using radiative cooling as a heat sink for geothermal power plants:

  10. Atmospheric radiative cooling enhancement via stratospheric aerosols - Carefully deploying stratospheric particles that enhance OLR:

Let me refine these and provide extensive detail on each. I'll focus on making each one distinct and providing real physics/chemistry/engineering detail.

Let me reconsider and provide the best 10:

  1. Large-scale ground-based radiative cooling arrays

  2. Space-based radiative cooling panels

  3. Building and urban-scale radiative cooling integration

  4. Ocean surface radiative cooling arrays

  5. Radiative cooling-enhanced industrial cooling systems

  6. Radiative cooling for ice/snow preservation and albedo maintenance

  7. Radiative cooling as heat sink for power generation (improving efficiency)

  8. Enhanced atmospheric transparency in the IR window (targeted)

  9. Radiative cooling integrated with desalination and other industrial processes

  10. Radiative cooling for agricultural applications (reducing heat stress, reducing energy for irrigation/cooling)

Actually, let me think more carefully. The user specifically asks about using "night time cooling of radiant energy into space" to reduce global temperatures. Let me focus on mechanisms that actually transfer heat FROM the Earth system TO space, rather than just reducing energy use (though that's still relevant).

Let me structure my response with extensive detail on each method.



OUTPUT:

10 Strategies for Using Nighttime Radiative Cooling to Space to Reduce Global Temperatures

Radiative cooling to space exploits the fact that the sky (effective ~3 K in space) is far colder than Earth's surface (~288 K). Thermal radiation in the atmospheric transparency window (8–13 μm) can escape directly to space without being reabsorbed. Each approach below leverages this thermodynamic gradient.


1. Megascale Ground-Based Radiative Cooling Arrays

Concept: Deploy thousands of square kilometers of engineered radiative cooling films or multi-layer dielectric coatings on flat terrain (deserts, rooftops, agricultural margins). These materials emit strongly in the 8–13 μm atmospheric window while reflecting most visible and near-IR solar radiation.

How it works physically:

Engineering specifics:

Impact pathway:

Challenges:


2. Space-Based Radiative Cooling Panels (Orbital "Heat Sinks")

Concept: Deploy large arrays of radiatively active panels in low Earth orbit (LEO, 400–2,000 km) or higher, positioned to absorb outgoing longwave radiation (OLR) from Earth and re-emit it isotropically into cold space.

How it works physically:

Scale and geometry:

Impact pathway:

Challenges:


3. Building and Urban-Intensive Radiative Cooling Integration

Concept: Integrate high-performance radiative cooling into the architecture and infrastructure of cities to (a) directly extract heat from urban environments to space, (b) reduce mechanical cooling demand (and thus fossil fuel emissions), and (c) mitigate the urban heat island (UHI) effect.

How it works physically:

Engineering specifics:

Impact pathway:

Challenges:


4. Ocean Surface Radiative Cooling Arrays

Concept: Deploy vast arrays of radiative cooling materials floating on the ocean surface, extracting thermal energy from the upper ocean mixed layer and radiating it to space.

How it works physically:

Scale:

Impact pathway:

Challenges:


5. Radiative Cooling-Enhanced Industrial and Power Plant Heat Rejection

Concept: Replace or augment conventional cooling towers (evaporative or once-through water cooling) with radiative cooling systems that reject waste heat directly to space, reducing the thermodynamic penalty of power generation and industrial processes.

How it works physically:

Engineering specifics:

Impact pathway:

Challenges:


6. Ice, Snow, and Glacier Preservation via Radiative Cooling

Concept: Deploy radiative cooling materials on and around ice sheets, glaciers, and seasonal snowpacks to reduce their melting rate, thereby maintaining albedo and preventing the ice-albedo positive feedback loop.

How it works physically:

Scale:

Impact pathway:

Challenges:


7. Radiative Cooling as a Cold Sink for Concentrated Solar Power (CSP) and Geothermal Plants

Concept: Use nighttime radiative cooling to space to provide a cold heat sink for Concentrated Solar Power (CSP) plants and geothermal power plants, improving their thermodynamic efficiency and enabling continuous operation.

How it works physically:

Engineering specifics:

Impact pathway:

Challenges:


8. Targeted Reduction of Atmospheric Absorption in the 8–13 μm Window

Concept: Modify specific atmospheric constituents (water vapor, CO₂, or trace gases) to increase the transparency of the 8–13 μm atmospheric window, allowing more of Earth's thermal radiation to escape to space.

How it works physically:

Mechanisms:

Impact pathway:

Challenges:


9. Radiative Cooling-Integrated Desalination and Industrial Water Systems

Concept: Use radiative cooling to space as the heat rejection system for solar-thermal desalination and industrial water-heating processes, reducing energy consumption and enabling deployment in arid, clear-sky regions.

How it works physically:

Engineering specifics:

Impact pathway:

Challenges:


10. Agricultural Radiative Cooling for Heat Stress Mitigation and Energy Reduction

Concept: Deploy radiative cooling materials in agricultural systems to reduce crop heat stress, reduce irrigation demand, and lower energy consumption for farm cooling, thereby reducing the agricultural sector's carbon footprint.

How it works physically:

Scale and impact:

Engineering specifics:

Impact pathway:

Challenges:


Summary Table

#

Strategy

Primary Mechanism

Scale

Key Benefit

1

Ground-based arrays

Direct OLR enhancement

10⁴–10⁵ km²

Large-scale heat rejection

2

Space-based panels

Orbital heat sink

10⁴–10⁶ panels

Cloud-independent cooling

3

Building/urban integration

Rooftop radiative coolers

City-scale

UHI reduction, HVAC savings

4

Ocean surface arrays

Ocean heat rejection

10⁴–10⁶ km²

Ocean cooling, SST reduction

5

Industrial/power plant cooling

Radiative heat sink

Plant-scale

Efficiency gains, less fuel

6

Ice/glacier preservation

Ice surface cooling

Continental

Albedo maintenance, slower melt

7

CSP/geothermal cold sink

Radiative condenser

Plant-scale

Higher efficiency, less resource use

8

Atmospheric absorption reduction

Increased window transparency

Global

Direct OLR increase

9

Desalination/industrial water

Radiative brine cooling

Plant-scale

Water-energy synergy

10

Agricultural cooling

Crop canopy radiative cooling

Regional

Yield, water, energy savings



Each strategy leverages the fundamental thermodynamic advantage of radiating to the 3 K cosmic background, but differs in scale, mechanism, and impact pathway. The most promising near-term approaches are likely #1, #3, #5, #7, and #10, while #2 and #4 represent longer-term, higher-impact but more challenging solutions.