Gemma 4 31B

Headlines · 3

1. 2d
   Gemma-4-31B-Humanlike-Chat fine-tune released

   Developer LessThanThreeAI released Gemma-4-31B-Humanlike-Chat, a fine-tune of Google’s Gemma 4 31B trained with SFT, on-policy distillation, and self-play. The author says it produces a more human-like chat style and published GGUF, BF16, FP8, and GPTQ-Int4 weights, along with training details and self-reported benchmark results. The reported behavioral advantages have not been independently verified.

   r/LocalLLaMA · 🔥 2
2. Oct 24
   Monadize Fine-Tunes Gemma 4 31B for More Diverse Names

   Developer u/zoraruha fine-tuned Gemma 4 31B to create Monadize, aiming to reduce repetitive names by shifting next-token probability distributions and using single-character tokenization for new names. In a developer-run test at temperature 1, the base model generated repeated names, while Monadize produced 60 unique names across 30 character-name and 30 city-name generations. The developer describes Monadize as experimental, warns that unexpected behavior may occur and recommends keeping thinking disabled because it was fine-tuned that way.

   r/LocalLLaMA · 🔥 0
3. Oct 18
   Corbenic's Galahad puts KV cache on server SSDs; its 14x claim draws scrutiny

   TechRadar reports that Belgian startup Corbenic AI's Galahad, in beta since October 1 2026, writes a model's KV cache to encrypted local NVMe storage and reloads it after GPU memory is cleared or the server restarts, so the model need not reread the same text. Founder Sietse Schelpe says it needs only Linux, an Nvidia GPU and a standard NVMe drive, with no special hardware. The report notes, however, that the headline 14x figure owes most to a retrieval component called Blaise, which hands the model roughly 668 tokens instead of the whole corpus; with the KV component Taliesin alone, vLLM took 15.56 seconds per question versus an 8.11-second baseline, and SGLang 22.02 versus 10.29. It also notes that LMCache, Nvidia Dynamo, WEKA and Phison already persist KV cache to storage, and that Corbenic's numbers come from the founder's own runs on rented cloud GPUs in self-published preprints, with raw logs not yet released.

   TechRadar · 🔥 0

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