52 lines
2.0 KiB
Markdown
52 lines
2.0 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: Qwen2.5-1.5B-Instruct-SFT-BigmathV_Simple_Balanced-LR1.0e-5-EPOCHS2
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results: []
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---
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[**TinyV**]((https://arxiv.org/abs/2505.14625)) is a reward system for efficient RL post-training that detects false negatives in current rule-based verifiers and provides more accurate reward signals via a small LLM during RL training. Experiments show that TinyV incurs only 6% additional computational cost while significantly increasing both RL efficiency and final model performance.
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- 📄 [Technical Report](https://arxiv.org/abs/2505.14625) - Including false negative analysis and theotical insights behind TinyV
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- 💾 [Github Repo](https://github.com/uw-nsl/TinyV) - Access the complete pipeline for more efficient RL training via TinyV
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- 🤗 [HF Collection](https://huggingface.co/collections/zhangchenxu/tinyv-682d5840c7e309217df625df) - Training Data, Benchmarks, and Model Artifact
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This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on [zhangchenxu/TinyV_Training_Data_Balanced](https://huggingface.co/datasets/zhangchenxu/TinyV_Training_Data_Balanced) dataset.
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### Overview
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### How to use it?
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Please refer to the codebase: [https://github.com/uw-nsl/TinyV](https://github.com/uw-nsl/TinyV) for details.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 512
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- total_eval_batch_size: 64
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2.0
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### Framework versions
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- Transformers 4.48.3
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- Pytorch 2.5.0
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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