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shawgpt-ft
robins80/shawgpt-ft
shawgpt-ft is a machine learning model from robins80. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Apr 3, 2024
- Updated
- Apr 3, 2024
- SHA
- 8f92b516
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. -->
shawgpt-ft
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.2-GPTQ on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8800
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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.5939 | 0.92 | 3 | 3.9660 |
| 4.053 | 1.85 | 6 | 3.4457 |
| 3.4831 | 2.77 | 9 | 2.9940 |
| 2.2697 | 4.0 | 13 | 2.5675 |
| 2.6856 | 4.92 | 16 | 2.3178 |
| 2.368 | 5.85 | 19 | 2.1399 |
| 2.1933 | 6.77 | 22 | 2.0406 |
| 1.5488 | 8.0 | 26 | 1.9478 |
| 2.0081 | 8.92 | 29 | 1.8887 |
| 1.3812 | 9.23 | 30 | 1.8800 |
Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Library
- peft
- License
- apache-2.0
- Created
- Apr 3, 2024
- Updated
- Apr 3, 2024