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SnapFill stage-2 fine-tunes (Team 022, internal)

Two phone-ready GGUFs for the form-filling LLM (stage 2). Both are LoRA fine-tunes on the v3 compact prompt, merged and quantized at the llama.cpp commit GenieX 0.8.0 pins (94256114c).

File Base Quant Size Field accuracy, Gemini eval / Vinay (turing, grammar on)
gemma-v3-qat-q4_0.gguf google/gemma-4-E2B-it-qat-q4_0-unquantized Q4_0, trained with Q4_0 fake-quant (QAT) 3.35 GB 0.915 / 0.801 (correct values 0.91 / 0.91, catches 10/10 off-topic)
q17-v3-q4_0im.gguf unsloth/Qwen3-1.7B Q4_0 with imatrix calibration 1.06 GB 0.782 / 0.830 (correct values 0.82 / 0.93)

How to use them:

  • Send the prompt exactly as docs/stage2-prompt-format.md in the team repo specifies:
    • compact messages only (system + user, no few-shots);
    • PromptBuilder(fewshot = false), i.e. ModelDescriptor.fineTuned = true;
    • the v3 grammar from ToolGrammar;
    • topK = 1, thinking off through the chat template;
    • nCtx = 2048.
  • Any other prompt text silently lowers accuracy. Do not add /no_think.
  • Keep gemma-v3-qat as Q4_0. Its f16/Q8_0 exports score 0.25: the adapter only works after Q4_0 rounding.
  • Scores come from Gemini-written eval cases plus Vinay's 72 human-written cases (eval/llm/README.md). They are not yet checked on real speech.
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gemma4
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