whittle-next

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whittle-next — GGUF builds (research preview)

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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.

⚠️ Research artifact. A 19.8B qwen4exp-architecture model built by weight surgery on Whittle-tri-14.7B (Qwen3.8-27B depth-compressed 64 -> 32 layers by parallel-compose merging, FFN width uncut, Apache-2.0), then repaired by SFT. It now holds a conversation, follows the chat template, writes fenced code, and stops cleanly — but it is factually thin and its arithmetic is approximate. Treat it as an architecture demonstrator, not an assistant.

Status

Earlier Whittle-Next release (research artefact). The Whittle-Next line continued with Whittle-Next-27B-A3B, parent of the current Whittle-Qwen-3.8-35B-A3B. This repo holds the GGUF builds of the 19.8B lineage; the router104 file below is the build the Qwen3.8-Whittle-Next-19.8B-A11B-chat card points to.

Which weights are which

Serve every file with the settings under Run it; they are required, not suggestions.

Also in this repo, not described on this card (names as in the file listing; sizes as the Hub reports them, binary GiB):

• whittle-next-qwen4exp-SFT-PLE4B-Q4_K_M.gguf (capital SFT, 12.31 GiB) — a second SFT build next to the recommended lowercase sft file (12.29 GiB); the two files differ in size and this card does not say how they differ.
• Root safetensors (model-00001.safetensors … model-00008.safetensors, model.safetensors.index.json) with config.json, generation_config.json, chat_template.jinja, tokenizer.json, tokenizer_config.json.
• hc_init.pt, mini_next.py.
• next-v2/ (hc_ple.pt, ngram_table_4B.pt, train_phase1.log) and next-v3/ (hc_ple_attn.pt, ngram_table_4B.pt, train_final.log).
• recovery/phaseA_step1250/ and recovery/phaseC_step100/ (hc.pt, ple.pt, routers.pt).

Measured

Behaviour (identical 6-probe harness, k=104, ai-2 / 3×RTX 3060)

With the recommended sampling the remaining loop disappears: longform, explanation, code and list probes all returned finish_reason=stop with 4-gram repetition 0.000 (one short story at 0.38).

Why router104 is not the default, despite better numbers. Training the routers jointly with the shared-expert gates, hyper-connections and n-gram projections — and at the k they serve — produced the best offline metrics this project has recorded (held-out CE 4.1466 → 3.9745, fact battery 4/5 → 5/5). But served, it over-thinks and repeats more. The training-time gate was selecting on cross-entropy and a short greedy battery, neither of which measures paragraph-length generation; repetition on that gate rose 0.057 → 0.093 over the same window while CE improved. The router result is real and reproducible — it is a training-objective lesson, not a serving win.

How conversational is it, really?

Mechanically conversational; not yet substantively reliable. It takes a turn, answers, and stops — and the content underneath is often wrong. Verified single-turn probes (recommended build, k=104, serving sampler): Paris ✅, a complete valid fenced HTML page ✅, a coherent non-repeating paragraph ✅ — against "the sky is blue because sunlight shines through the clouds" ❌, 17+25 = 32 ❌, and "list exactly 5 fruits" sometimes answered "1, 2, 3, 4, and 5 are fruits" ❌ (it hears the format and misses the substance).

Untested: every probe is single-turn. Multi-turn context retention — arguably the real test of "conversational" — has not been measured, and we make no claim about it.

The failure mode has moved from broken generation to a small model with damaged knowledge.

Architecture

19.775B parameters, 32 layers × 5120, 3:1 GDN:full-attention, 240 experts (k=104 recommended, 58 trained), 4 hyper-connection residual streams, per-layer n-gram memory over a 6.25M-row × 640 table (≈4B parameters, host-offloadable). Requires a llama.cpp with qwen4exp support; the GGUFs declare output_gate_type: silu, which transformers' qwen4_exp config now supports natively — the Qwen3.5-derived GDN weights need a SiLU output gate, not the sigmoid a Flash-Next model uses.

Run it

Serving settings — these are REQUIRED, not suggestions:

llama-server -m whittle-next-qwen4exp-sft-PLE4B-Q4_K_M.gguf -ngl 99 -c 8192 --jinja \
  --override-kv qwen4exp.expert_used_count=int:104

Request body: temperature 0.7, top_p 0.8, top_k 20, repeat_penalty 1.05.

Two settings do almost all the work, and both were measured on this build:

• Sampling, not greedy. At temperature 0 a paragraph-length answer degenerates (4-gram repetition 0.885 — "the ocean is a combination of water and water…"). At the settings above the same prompt scores 0.000 and ends with finish_reason=stop. Greedy decoding is the single largest cause of looping in this model.
• k=104 active experts (the --override-kv above). Raising k from the trained 58 to 104 is a config-only change that fixed list termination, restored task engagement (a "build a page" request went from a fabricated URL to real fenced HTML), and removed intra-list repetition — with zero gradient steps.

Reasoning is optional: pass chat_template_kwargs: {"enable_thinking": false} for short factual turns. With thinking on, allow ≥700 tokens — the think block is verbose.

Caveats

• Facts are unreliable. "Why is the sky blue" gets a confident wrong answer. The 4B n-gram memory measurably helps in-domain perplexity (86.6 → 33.2 on wikitext-2 at ctx 512) but does not make the model knowledgeable.
• Arithmetic is approximate: 17 + 25 returns 32, 20 or 49 depending on build and mode. The model estimates where the parent computed.
• It will invent URLs and citations rather than decline. Do not trust any link it produces.
• Long code generations can still decay under greedy decoding; use the sampling settings.

Provenance

Lineage: Qwen3.8-27B → Whittle-tri-14.7B (depth-compressed 64 -> 32 layers by parallel-compose merging, FFN width uncut) → this qwen4exp build, repaired by SFT. Apache-2.0.

David Aylward (logic65) & Claude (Anthropic) — designed, debugged and trained together.