---
license: mit
---
# 0425

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.
It achieves the following results on the evaluation set:

- Loss: 0.8213

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- total_eval_batch_size: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
| :-----------: | :----: | :--: | :-------------: |
|    1.0669     | 0.2018 | 100  |     0.8823      |
|    0.9156     | 0.4036 | 200  |     0.8593      |
|    0.9509     | 0.6054 | 300  |     0.8491      |
|    0.8287     | 0.8073 | 400  |     0.8423      |
|    0.8772     | 1.0091 | 500  |     0.8390      |
|    0.9101     | 1.2109 | 600  |     0.8385      |
|    0.8212     | 1.4127 | 700  |     0.8342      |
|    0.8721     | 1.6145 | 800  |     0.8327      |
|    1.0033     | 1.8163 | 900  |     0.8319      |
|    0.9879     | 2.0182 | 1000 |     0.8276      |
|     0.964     | 2.2200 | 1100 |     0.8276      |
|    0.8409     | 2.4218 | 1200 |     0.8264      |
|    0.8055     | 2.6236 | 1300 |     0.8262      |
|    1.0026     | 2.8254 | 1400 |     0.8240      |
|     0.881     | 3.0272 | 1500 |     0.8241      |
|    1.0058     | 3.2291 | 1600 |     0.8226      |
|    0.8747     | 3.4309 | 1700 |     0.8205      |
|    0.8686     | 3.6327 | 1800 |     0.8215      |
|    0.8838     | 3.8345 | 1900 |     0.8208      |
|    0.8246     | 4.0363 | 2000 |     0.8218      |
|    0.8727     | 4.2381 | 2100 |     0.8216      |
|    0.8737     | 4.4400 | 2200 |     0.8214      |
|    0.8955     | 4.6418 | 2300 |     0.8214      |
|    0.8909     | 4.8436 | 2400 |     0.8215      |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1