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V0305P8
Litzy619/V0305P8
V0305P8 is a machine learning model from Litzy619. 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 other.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Mar 7, 2024
Parameters
6.8B
14 GB on disk
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.safetensors13.8 GB · 98%
Parameter types
How the weights are stored.
F166.7B · 99%
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. -->
V0305P8
This model is a fine-tuned version of yahma/llama-7b-hf on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0762
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 20
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.877 | 0.09 | 10 | 0.8565 |
| 0.3259 | 0.17 | 20 | 0.1568 |
| 0.1567 | 0.26 | 30 | 0.1546 |
| 0.152 | 0.34 | 40 | 0.1487 |
| 0.1493 | 0.43 | 50 | 0.1432 |
| 0.149 | 0.51 | 60 | 0.1259 |
| 0.1321 | 0.6 | 70 | 0.1074 |
| 0.1234 | 0.68 | 80 | 0.1031 |
| 0.1094 | 0.77 | 90 | 0.0987 |
| 0.11 | 0.85 | 100 | 0.0989 |
| 0.1084 | 0.94 | 110 | 0.0971 |
| 0.1055 | 1.02 | 120 | 0.0967 |
| 0.0937 | 1.11 | 130 | 0.0920 |
| 0.0917 | 1.19 | 140 | 0.0874 |
| 0.0893 | 1.28 | 150 | 0.0819 |
| 0.09 | 1.37 | 160 | 0.0808 |
| 0.0882 | 1.45 | 170 | 0.0827 |
| 0.0832 | 1.54 | 180 | 0.0830 |
| 0.0824 | 1.62 | 190 | 0.0798 |
| 0.0871 | 1.71 | 200 | 0.0800 |
| 0.0861 | 1.79 | 210 | 0.0792 |
| 0.0844 | 1.88 | 220 | 0.0761 |
| 0.0802 | 1.96 | 230 | 0.0780 |
| 0.0691 | 2.05 | 240 | 0.0805 |
| 0.0602 | 2.13 | 250 | 0.0816 |
| 0.056 | 2.22 | 260 | 0.0802 |
| 0.0544 | 2.3 | 270 | 0.0834 |
| 0.0632 | 2.39 | 280 | 0.0769 |
| 0.0629 | 2.47 | 290 | 0.0735 |
| 0.0615 | 2.56 | 300 | 0.0739 |
| 0.0625 | 2.65 | 310 | 0.0756 |
| 0.056 | 2.73 | 320 | 0.0764 |
| 0.0586 | 2.82 | 330 | 0.0761 |
| 0.0576 | 2.9 | 340 | 0.0763 |
| 0.0582 | 2.99 | 350 | 0.0762 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
- License
- other
- Created
- Mar 6, 2024
- Updated
- Mar 7, 2024