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mistral-lora-Paiwan-summarization
aslinguist/mistral-lora-Paiwan-summarization
mistral-lora-Paiwan-summarization is a machine learning model from aslinguist. 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 17, 2025
- Updated
- Apr 17, 2025
- SHA
- a7bdf3da
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. -->
mistral-lora-Paiwan-summarization
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.7086
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 9 | 2.9467 |
| 3.2223 | 2.0 | 18 | 2.6801 |
| 2.6 | 3.0 | 27 | 2.4662 |
| 2.1872 | 4.0 | 36 | 2.3692 |
| 1.875 | 5.0 | 45 | 2.3591 |
| 1.5634 | 6.0 | 54 | 2.3972 |
| 1.2323 | 7.0 | 63 | 2.5054 |
| 0.9206 | 8.0 | 72 | 2.7086 |
Framework versions
- PEFT 0.15.0
- Transformers 4.51.2
- Pytorch 2.2.2+cu118
- Datasets 3.5.0
- Tokenizers 0.21.1
- Library
- peft
- License
- apache-2.0
- Created
- Apr 17, 2025
- Updated
- Apr 17, 2025