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llama-fin
Jae-star/llama-fin
llama-fin is a text generation model from Jae-star. Use it when you need the model to write or continue text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Generation
- Library
- transformers
- Model type
- llama
- Access
- Public
- Created
- Apr 14, 2025
- Updated
- Apr 14, 2025
- SHA
- 55445e3b
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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. -->
llama-fin
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2086
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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.0634 | 0.1593 | 5000 | 1.6380 |
| 1.5345 | 0.3185 | 10000 | 1.4842 |
| 1.4255 | 0.4778 | 15000 | 1.4151 |
| 1.3929 | 0.6370 | 20000 | 1.3720 |
| 1.3462 | 0.7963 | 25000 | 1.3367 |
| 1.3094 | 0.9555 | 30000 | 1.3087 |
| 1.2835 | 1.1148 | 35000 | 1.2838 |
| 1.2534 | 1.2740 | 40000 | 1.2605 |
| 1.2303 | 1.4333 | 45000 | 1.2407 |
| 1.2187 | 1.5926 | 50000 | 1.2244 |
| 1.2001 | 1.7518 | 55000 | 1.2133 |
| 1.1937 | 1.9111 | 60000 | 1.2086 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.1.0+cu118
- Datasets 3.5.0
- Tokenizers 0.21.1
- Task
- Text Generation
- Library
- transformers
- Type
- llama
- Created
- Apr 14, 2025
- Updated
- Apr 14, 2025