Johneeee

Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-oQ5e

README

License: apache-2.0

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).

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.

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.

markdown
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.

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)

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README

License: apache-2.0

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]

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.

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.

Important note on Qwen 27B 3.8 bench VS Qwen 3.6/3.5 27B versions:

markdown
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.

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)