The previous released Qwen3.8-27B-Humanlike-Chat 2.0, a fine-tune that texts humanly and does stuff, is now at 136k downloads for the GGUF repo between v1 and 2.0. Thank you. One of the top comments on the 2.0 post was "I wonder how Gemma 4 31B would do, trained with your method". So here it is: Gemma-4-31B-Humanlike-Chat. The voice of 2.0 on the official Gemma 4 31B from Google. What it does Texts humanly without a prompt. Short lowercase lines. Responds to what you say. Show a blind judge a real chat and two possible next messages. It picks which one came from the real person. The judge took this model's reply for the real person's in 26% of the moments. Google Gemma 4 31B in my tests: 0%. Asks, doesn't guess. Ask it to book something. Without details. It asks for details, it doesn't invent them. When2Call (call, ask or refuse properly): 66. Google base 46. Doesn't call tools if none is suitable. BFCL irrelevance 90. Google 81. Suitable tool and arguments: BFCL simple 96. Same knowledge as Google. MMLU-Pro 82.5. Same as Google base. Official Gemma behind it. Trained from google/gemma-4-31B-it. Apache-2.0. Not abliterated, don't think Gemma-4-31B needs it. Thinking works. Thinks briefly. Then texts back in the same voice. Characters and real tasks. Give it a character card, now it's this character. Ask it for a formal email, or numbered steps, or code. It gives you that. Then texts again. How I trained it Two steps. Same recipe as for 2.0. On 1 H200. SFT, 13,740 rows. 2.0's replies on its training chats. Original answers by Google on instructions and code. Human preference picks from blind side-by-sides. My corrections. On-policy distillation, 12 steps. The model writes its own response. A teacher rates each token. Two teachers. SFT model + hidden instruction to text humanly: chat, characters. Original Google base: Instructions, tools, code. The student doesn't see the hidden instruction. So it texts like that without a prompt. Some self-play in each step. Long chats with simulated friends. That fixed the habit of fading to one-word replies towards the end of long chats (after SFT, 28% of the late replies in long chats were only 1 or 2 words; after distillation, 12%). Numbers vs Google base (gemma-4-31B-it, the one I trained from) same prompts, same run, greedy decoding, thinking off Benchmark Google Gemma 4 31B Humanlike When2Call (call, ask or refuse correctly) 46 66 BFCL irrelevance (don't call a tool when none fits) 81 90 BFCL simple (right tool, right arguments) 95 96 MMLU-Pro (knowledge) 82.5 82.5 (same) Chart with all ten benchmarks in the images. Numbers vs 2.0 Same eval inputs. Thinking off. Benchmark Qwen 2.0 Gemma Humanlike LiveCodeBench (code) 51 66 IFEval (instruction following) 83.4 90.0 When2Call 58 66 BFCL irrelevance 78 90 IFBench (harder instruction following) 43.7 45.7 Does it text humanly? Same benchmark as last time. 150 moments from my chats (not in training). Each model writes the next message. The judge gets the real message and the model's message. No labels. Which one is the person? Model Judge thought it was the real person Gemma 4 31B (Google, the model I trained from) 0% Gemma-4-31B-Humanlike-Chat 26% Google Gemma didn't manage to fool the judge a single time. Links Hugging Face: GGUFs (IQ4_XS, Q4_K_M, Q5_K_M, Q6_K, Q8_0, BF16): https://huggingface.co/LessThanThreeAI/Gemma-4-31B-Humanlike-Chat-GGUF Safetensors for vLLM and SGLang: BF16 https://huggingface.co/LessThanThreeAI/Gemma-4-31B-Humanlike-Chat, FP8 https://huggingface.co/LessThanThreeAI/Gemma-4-31B-Humanlike-Chat-FP8, GPTQ-Int4 https://huggingface.co/LessThanThreeAI/Gemma-4-31B-Humanlike-Chat-GPTQ-Int4. The vLLM command with tool calling is in the model card. Try it in the browser: https://huggingface.co/spaces/LessThanThreeAI/Humanlike-Chat Thank you for asking for a Gemma version, and thank you for the feedback on 2.0. Try it, hope you like it!   submitted by   /u/kvyb [link]   [comments]