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license: mit |
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# 0502 |
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This model is a fine-tuned version of [/datas/huggingface/Qwen1.5-7B](https://huggingface.co//datas/huggingface/Qwen1.5-7B) on the alpaca_formatted_ift_eft_dft_rft_2048 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8510 |
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## Model description |
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Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: |
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* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated; |
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* Significant performance improvement in Chat models; |
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* Multilingual support of both base and chat models; |
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* Stable support of 32K context length for models of all sizes |
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* No need of `trust_remote_code`. |
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For more details, please refer to the [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). |
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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: 5.5e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- total_eval_batch_size: 2 |
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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_steps: 200 |
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- num_epochs: 5.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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| :-----------: | :----: | :--: | :-------------: | |
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| 1.1252 | 0.2335 | 200 | 1.0653 | |
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| 1.0075 | 0.4670 | 400 | 0.9458 | |
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| 1.2782 | 0.7005 | 600 | 0.9099 | |
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| 0.8558 | 0.9340 | 800 | 0.8929 | |
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| 0.922 | 1.1675 | 1000 | 0.8817 | |
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| 0.8985 | 1.4011 | 1200 | 0.8758 | |
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| 0.8273 | 1.6346 | 1400 | 0.8700 | |
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| 0.9136 | 1.8681 | 1600 | 0.8655 | |
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| 0.9963 | 2.1016 | 1800 | 0.8614 | |
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| 1.0214 | 2.3351 | 2000 | 0.8597 | |
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| 0.8823 | 2.5686 | 2200 | 0.8569 | |
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| 0.9265 | 2.8021 | 2400 | 0.8557 | |
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| 0.8033 | 3.0356 | 2600 | 0.8541 | |
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| 0.992 | 3.2691 | 2800 | 0.8527 | |
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| 0.7903 | 3.5026 | 3000 | 0.8522 | |
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| 0.8686 | 3.7361 | 3200 | 0.8518 | |
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| 0.954 | 3.9696 | 3400 | 0.8515 | |
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| 0.6472 | 4.2032 | 3600 | 0.8513 | |
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| 0.8799 | 4.4367 | 3800 | 0.8510 | |
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| 0.9454 | 4.6702 | 4000 | 0.8510 | |
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| 0.9496 | 4.9037 | 4200 | 0.8510 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
