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ofankit
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Model Details
Model Provider
ofankit
Model Tree
Qwen/Qwen3-4B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase_id = "Qwen/Qwen3-4B-Instruct-2507"adapter_id = "ofankit/finance-formalisation-rlvr-4b-focus"tokenizer = AutoTokenizer.from_pretrained(base_id)base = AutoModelForCausalLM.from_pretrained(base_id,torch_dtype="auto",device_map="auto",)model = PeftModel.from_pretrained(base, adapter_id)
On the repository's held-out finance formalization benchmark, this adapter solved
107 of 153 examples at pass@1 (0.6993). The verified SFT warm start solved 106
of 153 (0.6928) under the same evaluator.
Use this adapter for research on finance-domain formalization, Lean 4 generation, and verifier-guided post-training. Generated proofs must still be checked by Lean. The benchmark is domain-specific and does not establish general mathematical, financial-advice, or production-safety performance.
This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
bibtex
@article{shao2024deepseekmath,title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},year = 2024,eprint = {arXiv:2402.03300},}