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--- |
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library_name: transformers |
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license: other |
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base_model: meta-llama/Llama-3.2-3B-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: ScienceLLaMA-3B |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ScienceLLaMA-3B |
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<p align="center"> |
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• 🤗 <a href="https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M" target="_blank">Data </a> |
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• 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-3b" target="_blank">ScienceLLaMA-3B </a> |
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• 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-1b" target="_blank">ScienceLLaMA-1B </a> |
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• 🐱 <a href="https://github.com/dvlab-research/Logits-Based-Finetuning" target="_blank">Code</a> |
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• 📃 <a href="https://arxiv.org/abs/2505.24461" target="_blank">Paper</a> |
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</p> |
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This model is a fine-tuned with **Logits-Based Finetuning** on the [JingyaoLi/Science-Logits-1.2M](https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M), which integrates the strengths of supervised learning and knowledge distillation by combining teacher logits with ground truth labels. This preserves both correctness and linguistic diversity. |
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<div style="text-align: center;"> |
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<img src="./images/example.png" alt="example" /> |
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</div> |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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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: 4 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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### Framework versions |
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- Transformers 4.45.0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.1 |
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