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wflying
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Model Details
Model Provider
wflying
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
enable_thinking=False)All results below were obtained in non-thinking mode. The table reports the evaluation scores for the released model and two Qwen3.5 baselines.
| Model | AIME24 | AIME25 | HMMT25 | GPQA-Diamond | zebralogicbench | LiveCodeBench-v6 |
|---|---|---|---|---|---|---|
| Qwen3.5-35B-A3B | 64.79 | 60 | 48.75 | 82.83 | 90.6 | 71.85 |
| Qwen3.5-4B | 58.33 | 38.33 | 30 | 71.21 | 82.6 | 52.13 |
| Qwen3.5-4B-RL-MATH | 74.38 | 58.54 | 50.42 | 76.89 | 89.8 | 62.65 |
Under this evaluation setup, Qwen3.5-4B-RL-MATH improves over the Qwen3.5-4B baseline by 16.05 points on AIME24, 20.21 on AIME25, 20.42 on HMMT25, 5.68 on GPQA-Diamond, 7.20 on zebralogicbench, and 10.52 on LiveCodeBench-v6.
Use a recent version of transformers with Qwen3.5 support. The example below follows the non-thinking configuration used for evaluation.
python
import torchfrom transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "wflying/Qwen3.5-4B-RL-MATH" model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto",)processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "user", "content": [ { "type": "text", "text": "Solve the problem and put the final answer in \\boxed{}: What is the sum of the first 20 positive integers?", } ], }] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt",).to(model.device) with torch.inference_mode(): generated_ids = model.generate(**inputs, max_new_tokens=512) generated_ids = generated_ids[:, inputs.input_ids.shape[1]:]print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])This model inherits the Apache License 2.0 from the base model. Users must also comply with the applicable terms of the training dataset and dependent components.
import torchfrom transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "wflying/Qwen3.5-4B-RL-MATH" model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto",)processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "user", "content": [ { "type": "text", "text": "Solve the problem and put the final answer in \\boxed{}: What is the sum of the first 20 positive integers?", } ], }] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt",).to(model.device) with torch.inference_mode(): generated_ids = model.generate(**inputs, max_new_tokens=512) generated_ids = generated_ids[:, inputs.input_ids.shape[1]:]print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])