should probably proofread and complete it, then remove this comment. -->
2
19 commits
1 linked in READMEs
updated Apr 2, 2023
README
alpaca-lora-65b-en-pt-es-ca
This model is a fine-tuned version of /gaueko1/hizkuntza-ereduak/LLaMA/lm/huggingface/65B on the HiTZ/alpaca_mt ['en', 'pt', 'es', 'ca'] dataset. It achieves the following results on the evaluation set:
- Loss: 0.7271
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 63
- total_train_batch_size: 126
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8069 | 0.06 | 100 | 0.8033 |
| 0.8008 | 0.13 | 200 | 0.7826 |
| 0.7687 | 0.19 | 300 | 0.7721 |
| 0.7719 | 0.25 | 400 | 0.7647 |
| 0.7585 | 0.32 | 500 | 0.7588 |
| 0.7578 | 0.38 | 600 | 0.7537 |
| 0.7505 | 0.44 | 700 | 0.7491 |
| 0.7531 | 0.51 | 800 | 0.7449 |
| 0.7394 | 0.57 | 900 | 0.7416 |
| 0.7368 | 0.63 | 1000 | 0.7387 |
| 0.7412 | 0.69 | 1100 | 0.7361 |
| 0.7344 | 0.76 | 1200 | 0.7288 |
| 0.7383 | 0.82 | 1300 | 0.7281 |
| 0.7378 | 0.88 | 1400 | 0.7274 |
| 0.7204 | 0.95 | 1500 | 0.7271 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
Contributors
19 commits
Similar repos
should probably proofread and complete it, then remove this comment. -->
2
19 commits
1 linked in READMEs
updated Apr 2, 2023
README
alpaca-lora-65b-en-pt-es-ca
This model is a fine-tuned version of /gaueko1/hizkuntza-ereduak/LLaMA/lm/huggingface/65B on the HiTZ/alpaca_mt ['en', 'pt', 'es', 'ca'] dataset. It achieves the following results on the evaluation set:
- Loss: 0.7271
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 63
- total_train_batch_size: 126
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8069 | 0.06 | 100 | 0.8033 |
| 0.8008 | 0.13 | 200 | 0.7826 |
| 0.7687 | 0.19 | 300 | 0.7721 |
| 0.7719 | 0.25 | 400 | 0.7647 |
| 0.7585 | 0.32 | 500 | 0.7588 |
| 0.7578 | 0.38 | 600 | 0.7537 |
| 0.7505 | 0.44 | 700 | 0.7491 |
| 0.7531 | 0.51 | 800 | 0.7449 |
| 0.7394 | 0.57 | 900 | 0.7416 |
| 0.7368 | 0.63 | 1000 | 0.7387 |
| 0.7412 | 0.69 | 1100 | 0.7361 |
| 0.7344 | 0.76 | 1200 | 0.7288 |
| 0.7383 | 0.82 | 1300 | 0.7281 |
| 0.7378 | 0.88 | 1400 | 0.7274 |
| 0.7204 | 0.95 | 1500 | 0.7271 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
Similar repos
Contributors
19 commits