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README.md
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license: mit
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---
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license: mit
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---
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# 0425
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This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the alpaca_formatted_ift_eft_Justification dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8213
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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: 5e-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: 3
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 12
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- total_eval_batch_size: 3
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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: 20
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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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| 1.0669 | 0.2018 | 100 | 0.8823 |
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| 0.9156 | 0.4036 | 200 | 0.8593 |
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| 0.9509 | 0.6054 | 300 | 0.8491 |
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| 0.8287 | 0.8073 | 400 | 0.8423 |
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| 0.8772 | 1.0091 | 500 | 0.8390 |
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| 0.9101 | 1.2109 | 600 | 0.8385 |
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| 0.8212 | 1.4127 | 700 | 0.8342 |
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| 0.8721 | 1.6145 | 800 | 0.8327 |
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| 1.0033 | 1.8163 | 900 | 0.8319 |
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| 0.9879 | 2.0182 | 1000 | 0.8276 |
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| 0.964 | 2.2200 | 1100 | 0.8276 |
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| 0.8409 | 2.4218 | 1200 | 0.8264 |
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| 0.8055 | 2.6236 | 1300 | 0.8262 |
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| 1.0026 | 2.8254 | 1400 | 0.8240 |
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| 0.881 | 3.0272 | 1500 | 0.8241 |
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| 1.0058 | 3.2291 | 1600 | 0.8226 |
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| 0.8747 | 3.4309 | 1700 | 0.8205 |
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| 0.8686 | 3.6327 | 1800 | 0.8215 |
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| 0.8838 | 3.8345 | 1900 | 0.8208 |
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| 0.8246 | 4.0363 | 2000 | 0.8218 |
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| 0.8727 | 4.2381 | 2100 | 0.8216 |
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| 0.8737 | 4.4400 | 2200 | 0.8214 |
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| 0.8955 | 4.6418 | 2300 | 0.8214 |
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| 0.8909 | 4.8436 | 2400 | 0.8215 |
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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
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