Qwen3.6-Whittle-25B-A3B
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Qwen3.8-Whittle-16B
logic65> ### ☕ Support this work > Whittle is built by one person on a grocery budget and rented GPU hours. If this research is useful to you, or you want to see it finished: > **ko-fi.com/davida81328**. Every hour of GPU time goes straight into the next checkpoint, and every checkpoint, table and log lands in these repos. **A 27B whittled down to 16.8B with a logit lens and a pricing table, then healed with one A100 evening. It now outscores every intermediate cut, including ones 4B larger, at 20 tokens/second on two consumer 8GB GPUs.** Whittle is Qwen3.8-27B minus 20 of its 64 layers (whole interval-blocks, chosen by measured boundary cost) and minus 25% of every remaining MLP's width (weakest neurons by ‖down_column‖ × activation-std). That is a −10.1B parameter cut performed with **zero training**, followed by a single 11M-token QLoRA heal on a fully clean-lineage mix (synthetic fact frames, programmatic short-think arithmetic, hand-written code drills, public-domain long pages).
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View details ↗> ### ☕ Support this work > Whittle is built by one person on a grocery budget and rented GPU hours. If this research is useful to you, or you want to see it finished: > **ko-fi.com/davida81328**. Every hour of GPU time goes straight into the next checkpoint, and every checkpoint, table and log lands in these repos. Quantized builds of Whittle MoE 27B (A18B), a post hoc mixture of experts carved from Qwen3.8-27B and taught when to stop talking. v2.1 passed every release bar: 8 percent single turn loop rate (down from 69 at first release), 22 percent structured (down from 75), zero truncated answers, knowledge battery 28 of 39. Method, measurements, and the failures along the way are on the main model card. Earlier line (Aug 2026); the current Whittle models are the Whittle-Next / Whittle-Qwen-3.8 line: Whittle-Qwen-3.8-35B-A3B.
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View details ↗whittle-next
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View details ↗Whittle-Next-26B-A3B
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View details ↗Whittle-Next-27B-A3B
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View details ↗Whittle-Next-27B-A3B-GGUF
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View details ↗whittle-next-moe-test
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View details ↗Whittle-Qwen-3.8-35B-A3B
logic65基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗> ### ☕ Support this work > Whittle is built by one person on a grocery budget and rented GPU hours. If this research is useful to you, or you want to see it finished: > **ko-fi.com/davida81328**. Every hour of GPU time goes straight into the next checkpoint, and every checkpoint, table and log lands in these repos. Ready-to-run **llama.cpp** quantisations of logic65/Whittle-Qwen-3.8-35B-A3B: a 35.1 B-total, ~3 B-active mixture-of-experts in the Qwen3.8-Flash-Next (`qwen4_exp`) format with a **10 B-parameter n-gram memory that is load-bearing** (the body measurably needs it; see the base card). Distilled from Qwen3.8-27B at the logit level on 25k verified teacher traces; built on Whittle-Next-27B-A3B. **Runs on stock llama.cpp, no patches.** The full card, measurements and caveats are on the base repo; this repo is the download. **Current.** These files are the **Phase-2 step 32010 root** (*p2-s32010*, 30 Sep 2026) of the current Whittle flagship: the complete Phase-2 distillation from Qwen3.8-27B (every usable teacher row seen once), a memory phase, and a 410-step fix for the llama.cpp web page's browser tools. The previous (p2-s6000) ladder was replaced in place; the earlier full weights stay under `bf16-s6000/`, `bf16-agentfix2/`, `bf16-lw5/`, `bf16-lw2/` and `bf16-tbl1/` on the base repo.
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View details ↗Whittle-Qwen-3.8-45B-A3B
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View details ↗Whittle-Reflex-25B-A3B-GGUF
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