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starrylay
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Model Details
Model Provider
starrylay
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_id = "Qwen/Qwen3-1.7B"adapter_id = "starrylay/RC-OPD-1.7B" 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)model.eval()Scores are Avg@4 accuracy (%). Each benchmark contains 30 problems, with four generated solutions per problem (120 solutions per benchmark). Results are copied unchanged from the original release, where they were checked against the saved evaluation outputs and per-generation correctness labels; evaluation was not rerun for this migration.
| AIME24 | AIME25 | HMMT25 | Mean |
|---|---|---|---|
| 54.17 | 47.50 | 30.83 | 44.17 |
Avg@4 (%) = 100 × total correct generated solutions / (30 × 4). Mean is computed across the three benchmarks before rounding. These scores are not Pass@4 or majority-vote accuracy.
Each problem is submitted as a single user message with the exact following content:
text
{problem}Please reason step by step, and put your final answer within \boxed{}.{problem} is replaced by the benchmark problem text. There is no additional system message or few-shot example. The user message is formatted using the matching Qwen3 tokenizer's chat template:
python
messages = [{ "role": "user", "content": problem + "\n\nPlease reason step by step, and put your final answer within \\boxed{}.",}]prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True,)| Parameter | Value |
|---|---|
| Inference engine | vLLM with the selected LoRA adapter |
| Thinking mode | Enabled |
Samples per problem (n / val_n) | 4 |
| Temperature | 1.0 |
top_p | 0.95 |
top_k | -1 (disabled) |
min_p |
Dataset identifiers in the evaluation implementation are HuggingFaceH4/aime_2024, yentinglin/aime_2025, and MathArena/hmmt_feb_2025. Each contributes its full 30-problem set; the loader uses the dataset's train split label for these benchmark questions.
Scoring follows the original OPSD evaluator: extract the final \boxed{...} answer and check it with math_verify. If the verifier raises an exception, the evaluator falls back to normalized string comparison. A missing boxed answer is scored incorrect. These reported results use this evaluator without additional LLM regrading.
adapter_model.safetensors and adapter_config.json are available at the repository root for direct PEFT loading. Both files are byte-for-byte identical to the original release. checkpoint_manifest.json records their sizes, SHA256 hashes, and source revision.
| 0.0 |
| Presence penalty | 0.0 |
| Maximum generated tokens | 38,912 |
| Maximum context length | 40,960 |