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llama3-ViMMRC-Answer
Angelectronic/llama3-ViMMRC-Answer
llama3-ViMMRC-Answer is a machine learning model from Angelectronic. 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
- May 27, 2024
- Updated
- May 27, 2024
- SHA
- 1219477f
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. -->
llama3-ViMMRC-Answer
This model is a fine-tuned version of unsloth/llama-3-8b-Instruct-bnb-4bit on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1419
- Accuracy: 0.885662
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
ViMMRC train and test set
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.2677 | 0.3306 | 10 | 0.1883 |
| 0.4922 | 0.6612 | 20 | 0.2020 |
| 0.4551 | 0.9917 | 30 | 0.1609 |
| 0.4292 | 1.3223 | 40 | 0.2353 |
| 0.4361 | 1.6529 | 50 | 0.1758 |
| 0.4323 | 1.9835 | 60 | 0.1515 |
| 0.4232 | 2.3140 | 70 | 0.1451 |
| 0.411 | 2.6446 | 80 | 0.1424 |
| 0.413 | 2.9752 | 90 | 0.1419 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- peft
- License
- apache-2.0
- Created
- May 27, 2024
- Updated
- May 27, 2024