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--- |
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library_name: transformers |
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license: bigcode-openrail-m |
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base_model: bigcode/starcoder2-15b |
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tags: |
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- alignment-handbook |
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- trl |
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- sft |
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- generated_from_trainer |
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- trl |
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- sft |
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- generated_from_trainer |
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datasets: |
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- HuggingFaceH4/airoboros-3.2 |
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- HuggingFaceH4/Code-Feedback |
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- HuggingFaceH4/orca-math-word-problems-200k |
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- HuggingFaceH4/SystemChat |
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- HuggingFaceH4/capybara |
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model-index: |
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- name: starchat2-15b-v0.1 |
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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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# starchat2-15b-v0.1 |
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This model is a fine-tuned version of [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) on the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6601 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 2e-05 |
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- train_batch_size: 4 |
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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: 32 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 256 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.8402 | 0.1099 | 100 | 0.8307 | |
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| 0.7611 | 0.2198 | 200 | 0.7793 | |
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| 0.7361 | 0.3297 | 300 | 0.7525 | |
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| 0.6854 | 0.4396 | 400 | 0.7337 | |
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| 0.6926 | 0.5495 | 500 | 0.7197 | |
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| 0.7125 | 0.6593 | 600 | 0.7097 | |
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| 0.6662 | 0.7692 | 700 | 0.7015 | |
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| 0.6517 | 0.8791 | 800 | 0.6937 | |
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| 0.6234 | 0.9890 | 900 | 0.6869 | |
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| 0.5925 | 1.0989 | 1000 | 0.6866 | |
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| 0.585 | 1.2088 | 1100 | 0.6832 | |
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| 0.5857 | 1.3187 | 1200 | 0.6798 | |
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| 0.5736 | 1.4286 | 1300 | 0.6746 | |
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| 0.5906 | 1.5385 | 1400 | 0.6723 | |
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| 0.569 | 1.6484 | 1500 | 0.6686 | |
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| 0.5756 | 1.7582 | 1600 | 0.6655 | |
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| 0.545 | 1.8681 | 1700 | 0.6622 | |
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| 0.5505 | 1.9780 | 1800 | 0.6606 | |
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| 0.5149 | 2.0879 | 1900 | 0.6648 | |
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| 0.5234 | 2.1978 | 2000 | 0.6638 | |
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| 0.5239 | 2.3077 | 2100 | 0.6632 | |
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| 0.5142 | 2.4176 | 2200 | 0.6623 | |
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| 0.5086 | 2.5275 | 2300 | 0.6616 | |
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| 0.4998 | 2.6374 | 2400 | 0.6604 | |
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| 0.5029 | 2.7473 | 2500 | 0.6602 | |
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| 0.5146 | 2.8571 | 2600 | 0.6599 | |
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| 0.5293 | 2.9670 | 2700 | 0.6601 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.5.1+rocm6.2 |
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- Datasets 3.5.0 |
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- Tokenizers 0.20.3 |
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