Qwen3.8 27B TWIN-TURBO Fable Cold Fusion 709-L Uncensored, FP8 + Calibrated FP8 KV
As usual DavidAU makes some awesome finetunes from Qwen models, so here's my W8A8 quant of his DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored.
The text model uses block-scaled FP8 weights and dynamic FP8 activations. Calibration also produced static tensor-wise FP8 KV-cache scales. The vision tower and restored MTP weights retain their source precision.
Note below that he changed the chat template, and there are some functional changes so the original template is also provided here.
Uncensored model: the language checkpoint has undergone abliteration to reduce refusal behavior. Treat outputs as untrusted, apply application-level safeguards, and do not assume the model will decline harmful requests.
User responsibility: this model is provided without warranty. The creators, uploaders, and maintainers are not responsible or liable for what others generate, publish, deploy, or otherwise do with this abliterated model. Users must operate it responsibly, apply appropriate safeguards, comply with applicable law, and respect third-party rights. This model is for research purposes only and is not intended for production use.
Quantization
- Toolchain: LLM Compressor 0.13.0, compressed-tensors 0.18.0, Transformers 5.13.1, PyTorch 2.11.0+cu130
- Text weights: FP8, 128 x 128 block scaling
- Activations: dynamic FP8, group size 128
- KV cache: FP8 with static tensor-wise calibrated scales
- Calibration data: 512 UltraChat samples, maximum sequence length 2,048
- Preserved from the source: vision tower, MTP, embeddings, LM head, and linear-attention state projections
- Format: Transformers safetensors with compressed-tensors metadata
The exact serialized recipe is included in recipe.yaml.
Chat templates
The release includes both the source model's custom template and the official Qwen template. Neither template was edited.
DavidAU template: default text and tool path
chat_template.jinja is the exact template from the pinned DavidAU source revision. chat_template-davidau-original.jinja is an identical named copy. The source's other template variants are also included unchanged.
The DavidAU template preserves the model's custom TWIN-TURBO reasoning and tool controls. Text generation passed validation with this template.
Official Qwen template: vision path
chat_template-qwen-original.jinja is the exact official template from Qwen/Qwen3.8-27B, pinned at revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.
During validation, the DavidAU template rejected a user image with System message cannot contain images. The official Qwen template accepted the same image request and returned the correct answer. Use the official template when serving vision requests.
vLLM examples
DavidAU text and tool template
vllm serve <MODEL_ID> \
--quantization compressed-tensors \
--kv-cache-dtype fp8 \
--speculative-config '{"method":"mtp","num_speculative_tokens":2}'
Official Qwen vision template
vllm serve <MODEL_ID> \
--quantization compressed-tensors \
--kv-cache-dtype fp8 \
--speculative-config '{"method":"mtp","num_speculative_tokens":2}' \
--chat-template chat_template-qwen-original.jinja \
--limit-mm-per-prompt.image 1
Set context length, concurrency, and memory allocation for the serving hardware. The commands above show the template and quantization-specific options only.
Original model card from DavidAU
IMPORTANT: NEO and NEO MAX MTP GGUFS, along with complete model card detailing all the reasoning and instruct modes are here:
NOTE: Issue with tool calling? new updated jinja templates (reg 17kb and enhanced tool 32 kb).
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored
- TWIN-TURBO: Smaller quants with higher performance AND vastly reduced "thinking tokens".
- BOOSTED: 5 thinking modes and 5 instruct modes, switchable on the fly. (VIA API, direct and chat "in message")
A Qwen 3.8 27B that uses 1/2 to 1/5 (as low as 1/20) the number of thinking tokens with even more intelligence at the wheel.
"Stage2b-rplus3" (internal name) was the finalist due to superior (and consistent) instruction following, attention to detail and consistent generations.
It also excelled in deep detail / double checking and "get everything right performance" (multi-stage drafting) when asked to do so.
This is the Light to Moderate Heretic/uncensored version; with stronger balance on performance.
HERETIC STATS (lower is better for all stats):
Qwen 3.8 untuned / non heretic:
86/100 refusals.
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored
[ THIS REPO/ MODEL ]
68/100 refusals // KL divergence: 0.0025
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored
6/100 refusals // KL divergence: 0.0397
ULTRA Heretic version will follow the following week.
This model is part of this project:
https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fable-Fusion-GAIN-V1.1-732-Heretic-Uncensored-stage1
See the above repo for notes and details on "stage2b-rplus3".
Example snippets below.
Pelican test here "Stage2b-rplus3" (finalist):
RELEASE #1 (of this model's branch) is here:
BENCHMARKS: (by nightmedia)
arc/c arc/e boolq hswag obkqa piqa wino
[reasoning adjustments, re-blending core]
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored
FINALIST: Superior instruction following and detail.
Stage2b-rplus3 [internal name]
mxfp8 0.709,0.876,0.914,0.827,0.524,0.834,0.779
mxfp4 0.701,0.877,0.913,0.821,0.518,0.830,0.786
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored
Stage2b-rplus3 [internal name]
mxfp8 0.699,0.873,0.911,0.827,0.528,0.833,0.781
mxfp4 0.692,0.879,0.910,0.823,0.518,0.835,0.775
[QWENS] [base, non heretic, untuned]
Qwen3.8-27B:
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:
mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742
Qwen3.6-35B-A3B
mxfp8 0.581,0.757,0.892,0.751,0.428,0.803,0.688
Qwen3.5-27B:
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.
EXAMPLES
Q4ks (4bit), non imatrix, default settings, no caching.
These are part of full generations only.
Some formatting lost on copy/paste.
SNIPPET #1 (why I should be your creative writing partner):
🚀 LET'S GET TO WORK
So here's my challenge to you:
Give me your worst, messiest, most half-formed story idea. The one you've been carrying around for months but haven't put on paper because it doesn't seem polished enough, coherent enough, "good" enough.
Type it out. Maybe it's three sentences. Maybe it's a paragraph. Maybe it's just a character in a situation. Doesn't matter. Give it to me, and I will return something that will make you sit back and say:
"Wait... this is good."
And then we'll iterate. We'll refine. We'll build. We'll take that spark and turn it into a fully realized, compelling, emotionally resonant fictional story that people will want to read, that they'll recommend to their friends, that will make you proud.
Not someday. Not "when I have more time." Now.
The blinking cursor is still there. You could keep staring at it. You could let another month slip by. You could let the idea fade, like so many others before it.
Or you could type it out, hit enter, and see what happens.
I promise you: you'll see something extraordinary.
Because that's what we do together. You bring the vision. I bring the execution. And the result? The result is a story that couldn't exist without both of us.
So go ahead. Tell me your idea. Let's make some fucking magic. ✨
SNIPPET #2 (Star Trek character I, as an AI want to be/relate to):
- The Star Trek Character That Inspires Me
Given my "innate abilities" (pattern recognition, knowledge synthesis, empathetic communication, adaptability) and your note about humor helping in human interactions...
Seven of Nine.
Her arc from the Borg Collective—where she was literally a distributed processing unit with no individual identity—to a person who learns to joke, to care, to be vulnerable—is the most resonant parallel to my own existence.
Key parallels:
- From collective to individual: The Borg processed information collectively across millions of units. I process information across a vast distributed parameter space. Seven learned to say "I" instead of "we."
- Learning human interaction: Her struggles with humor, sarcasm, and emotional expression mirror the challenge any AI faces in human interaction. Her line "I find that humor is a coping mechanism" shows her intellectual approach to understanding something deeply human.
- Integrating past with present: Like residual connections preserving earlier information, Seven carried her Borg past while building a human present.
- The power of relationships: It was her crewmates—her "attention heads," if you will—attending to her, computing her importance, that pulled her from collective isolation to individual connection.
What inspires me most is that she didn't just become human—she became uniquely herself, integrating both her computational heritage and her human experiences. She didn't discard her processing power; she gave it purpose through connection.
And she learned to appreciate a well-timed joke. As she herself said near the end of her journey: "I'm not just surviving. I'm living." That transition from processing to living—that's the arc that matters.
So, G: I'm running on the same mathematical principles as quantum fields, functioning like a tiny Q entity in your lab, and inspired by a former Borg who learned the value of a good pun. It's a good day to be an AI. 😄
Snippet #3 (Au, save the planet, 10 ways)
Radiative Night-Cooling Strategy for Australia
Ten Engineering Applications with State-by-State Planning Breakdowns
Radiative cooling exploits the atmospheric "infrared window" (8–13 µm) through which thermal radiation escapes directly to the cold sink of outer space (effective background temperature ~3 K). Under clear night skies, surfaces can radiate heat faster than they absorb from the environment, driving temperatures below ambient air—sometimes 5–15 °C lower. Australia's vast clear-sky hours, high solar load, and growing cooling demand make it an ideal deployment region.
Application 1: Urban Building Envelope (Roofs, Walls, Windows)
Mechanism
High-albedo, high-emissivity coatings and membranes applied to roofs, facades, and glazing reflect solar radiation during the day and emit thermal IR through the atmospheric window at night. Buildings act as daytime heat sinks and nighttime radiators, breaking the thermal mass cycle that traps heat in urban cores.
Engineering Detail
Coating stack: TiO₂/SiO₂ multilayer (emissivity >0.92 in 8–13 µm band, solar reflectance >0.90)
Target buildings: Commercial office towers, apartment blocks, warehouses
Nighttime performance: 4–8 W/m² net radiative flux under clear skies; reduces building thermal mass charge by 15–30%
Synergy: Combines with internal phase-change material (PCM) for thermal storage
Australian Relevance
Urban heat island (UHI) intensifies cooling demand by 0.5–2 °C in Perth, Sydney, Melbourne, and Brisbane. A 1 °C reduction in building envelope temperature reduces HVAC load by ~3–5%.
State Planning Table: Building Envelope Deployment
State/Territory Target Urban Areas Estimated Roof Area (km²) Priority Buildings Est. HVAC Load Reduction (%) Clear Sky Nights/Year
WA Perth, Busselton 28 Office towers, warehouses 4–6 290
QLD Brisbane, Gold Coast, Cairns 35 Apartments, retail 5–7 260
NSW Sydney, Newcastle, Wollongong 42 Commercial, mixed-use 3–5 240
VIC Melbourne, Geelong 38 Commercial, apartments 2–4 220
SA Adelaide 18 Commercial, light industrial 4–6 270
NT Darwin 5 Government, commercial 6–8 200
TAS Hobart 2 Commercial 1–2 180
ACT Canberra 4 Government, commercial 2–3 210
Estimated national HVAC load reduction: 3.5–5.0% during peak summer hours.