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---
license: mit
---
# 0502
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.
It achieves the following results on the evaluation set:
- Loss: 0.8510
## Model description
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:
* 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;
* Significant performance improvement in Chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
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).
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.5e-06
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
| :-----------: | :----: | :--: | :-------------: |
| 1.1252 | 0.2335 | 200 | 1.0653 |
| 1.0075 | 0.4670 | 400 | 0.9458 |
| 1.2782 | 0.7005 | 600 | 0.9099 |
| 0.8558 | 0.9340 | 800 | 0.8929 |
| 0.922 | 1.1675 | 1000 | 0.8817 |
| 0.8985 | 1.4011 | 1200 | 0.8758 |
| 0.8273 | 1.6346 | 1400 | 0.8700 |
| 0.9136 | 1.8681 | 1600 | 0.8655 |
| 0.9963 | 2.1016 | 1800 | 0.8614 |
| 1.0214 | 2.3351 | 2000 | 0.8597 |
| 0.8823 | 2.5686 | 2200 | 0.8569 |
| 0.9265 | 2.8021 | 2400 | 0.8557 |
| 0.8033 | 3.0356 | 2600 | 0.8541 |
| 0.992 | 3.2691 | 2800 | 0.8527 |
| 0.7903 | 3.5026 | 3000 | 0.8522 |
| 0.8686 | 3.7361 | 3200 | 0.8518 |
| 0.954 | 3.9696 | 3400 | 0.8515 |
| 0.6472 | 4.2032 | 3600 | 0.8513 |
| 0.8799 | 4.4367 | 3800 | 0.8510 |
| 0.9454 | 4.6702 | 4000 | 0.8510 |
| 0.9496 | 4.9037 | 4200 | 0.8510 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1 |