Update README.md

README.md

@@ -1,3 +1,85 @@
----
-license: mit
----
+---
+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