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README
License: apache-2.0Summary
- Base model:
Qwen/Qwen3-8B-Base - Method:
Math-RLVR + INFUSER - Code repo: https://github.com/FFishy-git/INFUSER
- Data repo:
Siyuc/infuser-data
Evaluation
Released Checkpoint Scores
| Category | Benchmark | Score |
|---|---|---|
| General | MMLU-Pro | 65.80% |
| General | GPQA-Diamond | 43.54% |
| General | SuperGPQA | 36.43% |
| General | BBEH | 13.51% |
| Math & physics | MATH500 | 84.85% |
| Math & physics | AIME2024 | 21.46% |
| Math & physics | AIME2025 | 17.71% |
| Math & physics | HMMT | 9.64% |
| Math & physics | OlympiadBench (Math) | 52.08% |
| Math & physics | OlympiadBench (Phys) | 12.71% |
| Medical | MedQA | 63.79% |
| Medical | MedXpertQA | 14.00% |
| Coding | HumanEval+ | 76.91% |
| Coding | LiveCodeBench v1-5 | 27.10% |
Comparison Summary
Category and overall means use the same benchmark groups as the paper.
| Category | This model | Math-RLVR + INFUSER avg | Science-only INFUSER avg |
|---|---|---|---|
| General reasoning | 39.82% | 39.37% | 40.62% |
| Math & physics reasoning | 33.07% | 32.52% | 31.49% |
| Medical | 38.89% | 39.39% | 40.52% |
| Coding | 52.00% | 52.49% | 53.29% |
| Overall (14 benchmarks) | 38.54% | 38.31% | 38.50% |
Usage
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo_id = "Siyuc/INFUSER-rlvr-Qwen3-8B-base"tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained(repo_id)
Notes
The repository root is intentionally flattened so the tokenizer files, config files, and model shard files are available directly at the top level for standard transformers loading.
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