⚠️ NEEDS POST TRAINING. A research preview, published as the record of a
compression method and its measurements rather than as a finished assistant. Further
healing and instruction tuning runs are planned over time, and these checkpoints will
improve as those land.
Qwen3.8-Whittle-tri (14.7B)
A 27B compressed to 14.7B by training, not cutting. It writes code, answers
factual questions, never loops, and fits on two consumer 8GB cards.
Download q38_tri_repaired_q8.gguf (15.7GB) and run it. That is the whole setup.
Standard [gdn, gdn, gdn, attention] blocks at exactly 3:1, so it loads in any
llama.cpp build with Qwen3.5 support. No fork, no patches, no special flags.
On 16GB of VRAM keep about 24 of the 32 layers on GPU; with 24GB it fits whole.
What it is good at
Factual recall is close to intact: capitals, elements, dates, who wrote what.
Code is solid across Python, C, JavaScript, SQL and HTML, and markdown fencing
is reliable (8/8 on our fencing probe). It is loop-free: zero repetition
failures in six generations at both greedy and temperature 0.7, which is not
true of the larger models in this family.
What it is bad at
Arithmetic and multi-step reasoning. It scores 3.6% on GSM8K. It will often
state the right rule and then compute the wrong answer. Do not use it for maths,
data analysis, or anything where a number has to be right.
Non-English is unreliable outside French and Spanish. Long agent sessions were
not the target of this model's training.
Honest positioning
39-prompt battery: 31/39. That ties a variant 2.1B larger and beats the
19.2B cut, but two models in the family score higher (36/39 at 16.8B, 35/39 at
20.8B). Pick this one if you want the smallest thing that still works, or if
looping has bitten you before. Pick the bigger ones if you want maximum quality.
This is a research preview. It needs real post-training before it reasons, and
that is a funding problem rather than a technical one.
How it was made, briefly
The 64 layers of Qwen3.8-27B were distilled into 32: adjacent recurrent layers
were merged in pairs, and eight full-attention stations were absorbed into their
neighbours rather than deleted. Nothing was dropped. Full methodology, every
script, the adapters and the measurements are in the developer card:
Whittle dev
Collections: models you can run and research and dev
Support this work
Independent research on consumer hardware. Every donation becomes GPU hours, and
every GPU hour ends up as a public model or a public measurement.
☕ ko-fi.com/davida81328
Base model by the Qwen team (Apache 2.0). Built and measured by David Aylward
with Claude (Fable 5, Anthropic) as co-author.
