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zephyr-7b-sft-lora-accum4-lr5e_5-30
shkang/zephyr-7b-sft-lora-accum4-lr5e_5-30
zephyr-7b-sft-lora-accum4-lr5e_5-30 is a machine learning model from shkang. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Updated Oct 8, 2024
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Dec 10, 2023
- Updated
- Oct 8, 2024
- SHA
- 48092f2e
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
zephyr-7b-sft-lora-accum4-lr5e_5-30
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5210
Model description
More information needed
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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 30.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.8242 | 0.55 | 13 | 1.6628 |
| 1.5291 | 1.57 | 27 | 1.3837 |
| 1.2362 | 2.55 | 40 | 1.1786 |
| 1.1401 | 3.57 | 54 | 1.0978 |
| 1.0666 | 4.55 | 67 | 1.0428 |
| 1.021 | 5.57 | 81 | 1.0063 |
| 0.9852 | 6.55 | 94 | 0.9629 |
| 0.9317 | 7.57 | 108 | 0.9246 |
| 0.8735 | 8.55 | 121 | 0.8885 |
| 0.8147 | 9.57 | 135 | 0.8335 |
| 0.763 | 10.55 | 148 | 0.7860 |
| 0.6926 | 11.57 | 162 | 0.7296 |
| 0.6332 | 12.55 | 175 | 0.6863 |
| 0.5898 | 13.57 | 189 | 0.6491 |
| 0.536 | 14.55 | 202 | 0.6180 |
| 0.5263 | 15.57 | 216 | 0.5764 |
| 0.5071 | 16.55 | 229 | 0.5683 |
| 0.4756 | 17.57 | 243 | 0.5597 |
| 0.4693 | 18.55 | 256 | 0.5342 |
| 0.4414 | 19.57 | 270 | 0.5386 |
| 0.4266 | 20.55 | 283 | 0.5346 |
| 0.4286 | 21.57 | 297 | 0.5155 |
| 0.4256 | 22.55 | 310 | 0.5108 |
| 0.418 | 23.57 | 324 | 0.5230 |
| 0.407 | 24.55 | 337 | 0.5165 |
| 0.411 | 25.57 | 351 | 0.5128 |
| 0.3896 | 26.55 | 364 | 0.5027 |
| 0.39 | 27.57 | 378 | 0.5063 |
| 0.3928 | 28.55 | 391 | 0.4946 |
| 0.3818 | 29.57 | 405 | 0.5063 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
- License
- apache-2.0
- Created
- Dec 10, 2023
- Updated
- Oct 8, 2024