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license: mit |
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# 0506_9_8 |
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This model is a fine-tuned version of [../../models/Qwen1.5-7B-sft-0502](https://huggingface.co/../../models/Qwen1.5-7B-sft-0502) on the alpaca_formatted_review_new_data_0505_greater_8 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5497 |
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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: 7e-05 |
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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: 4 |
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- total_train_batch_size: 16 |
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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: 13 |
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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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| 0.6358 | 0.7619 | 20 | 0.5865 | |
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| 0.6379 | 1.5238 | 40 | 0.5621 | |
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| 0.6067 | 2.2857 | 60 | 0.5561 | |
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| 0.5339 | 3.0476 | 80 | 0.5515 | |
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| 0.6749 | 3.8095 | 100 | 0.5500 | |
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| 0.6351 | 4.5714 | 120 | 0.5497 | |
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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 |
