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LessThanThreeAI
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Model Details
Model Provider
LessThanThreeAI
Model Tree
google/gemma-4-31B-it
Input Modalities
Output Modalities
Supported Functionality
Tested with vLLM 0.31.0 on an RTX PRO 6000 Blackwell (96 GB):
bash
vllm serve LessThanThreeAI/Gemma-4-31B-Humanlike-Chat --served-model-name humanlike-gemma \ --max-model-len 16384 --language-model-only \ --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4Thinking is off by default, as in Google's Gemma 4 template. Send chat_template_kwargs: {"enable_thinking": true} per request to turn it on; the thinking comes back in its own field. It was trained with effort levels: add the line Reasoning effort: low or Reasoning effort: high to your system prompt, or no line for medium. Sampling: temperature 1.0, top_p 0.95, top_k 64 (Google's defaults in generation_config.json). For SGLang, point --model-path at this repo.
The weights take 57.8 GiB in vLLM. On the 96 GB test card at --gpu-memory-utilization 0.88, that left 22.7 GiB for the cache, 26,969 tokens. With less memory, take the FP8 or GPTQ-Int4 repo, or a GGUF from the main card.
Same check as every file on the main card: 5 fresh test chats against the reference (the merged BF16 weights in vLLM, which match Google's base plus the Humanlike adapter at runtime: KL 0.0005), served on vLLM 0.31.0, one NVIDIA RTX PRO 6000 Blackwell Server Edition (96 GB).
| File | KL vs reference | Top-1 agreement | Greedy turns identical | Check fails (greedy / sampled) | Decode tok/s |
|---|---|---|---|---|---|
| BF16 | reference | reference | reference | 0 / 0 | 23 |
Commissions open, DM codebottle on Discord (https://discord.com/users/320486798859960322). I build custom finetunes like this one: characters, product voices, distillation into smaller models, and domain or use-case specific models.
Apache-2.0, inherited from Google's Gemma 4 release.