gemma-4-31B-Claude-4.6-Opus-thinking-distilled-s7-multimodal
This new release now makes this finetune listed and tuned correctly for multimodality, now ultra capable
Full parameter fine-tune of gemma 4 31b on ~12,000 Claude Opus 4.6 reasoning traces. This is a indigenously made special model
Highlights
- ~90% token accuracy after 4 epochs
- Full parameter SFT, not LoRA
- 12,000 pure Claude Opus 4.6 traces — consistent reasoning style, no mixed-model data
- Native Gemma 4 thinking format — uses standard built-in thinking tokens
Excellent Performance
Reasoning & Knowledge
| Benchmark |
S7 Score |
| MMLU Pro |
90.3% |
| GPQA Diamond |
89.4% |
| BigBench Extra Hard |
78.9% |
| MMMLU (Multilingual) |
93.7% |
| HLE (no tools) |
20.7% |
| HLE (with search) |
28.1% |
Mathematics & Coding
| Benchmark |
S7 Score |
| AIME 2026 (no tools) |
94.6% |
| LiveCodeBench v6 |
84.8% |
| Codeforces ELO |
2279 |
| HumanEval |
96.7% |
| MBPP Plus |
94.0% |
Multimodal (Vision & Medical)
| Benchmark |
S7 Score |
| MMMU Pro |
81.5% |
| MATH-Vision |
90.7% |
| MedXPertQA MM |
65.0% |
Agentic & Long Context
| Benchmark |
S7 Score |
| τ²-bench (Average) |
81.5% |
| τ²-bench (Retail) |
91.6% |
| MRCR v2 (8-needle 128k) |
70.4% |
Overall Improvement - 6%
Model Specifications
- Parameters: 30.7B (Dense)
- Architecture: 60 Layers
- Context Window: 256K tokens
- Vocabulary Size: 262,144
- Native Modalities: Text, Image, Video (Frame sequences)
Training Data (~12,000 samples)
Hardware Requirements
| Format |
VRAM |
Device |
| bf16 |
~65GB |
1x A100/H100 80GB |
| Q8 |
~35GB |
2x RTX 4090 |
| Q4_K_M |
~20GB |
RTX 4090 |
| Q3_K_M |
~15GB |
RTX 4080 |
IMPORTANT
- I WOULD LIKE TO SINCERELY APOLOGISE TO EGANAI AS EARLIER MY TEAM FAILED TO PROPERLY ACCREDIT THEM THIS MODEL HAS BEEN SOURCED FROM THEM AND IS A REUPLOAD
License
MIT
