Downloads · 30 days
6
15% of all-time downloads
fr-ruund-nllb
eliezermga/fr-ruund-nllb
fr-ruund-nllb is a machine learning model from eliezermga. 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 transformers. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
41
Public
Parameters
615M
2.5 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.5 GB · 99%
At a glance
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- m2m_100
- Access
- Public
- Created
- Jun 12, 2026
- Updated
- Jun 12, 2026
- SHA
- 5dfbc186
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. -->
fr-ruund-nllb
This model is a fine-tuned version of facebook/nllb-200-distilled-600M on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5624
- Bleu: 26.02
- Chrf: 53.95
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf |
|---|---|---|---|---|---|
| 8.4900 | 1.0 | 102 | 3.4702 | 1.97 | 24.19 |
| 5.9821 | 2.0 | 204 | 2.5571 | 8.0 | 35.97 |
| 4.7875 | 3.0 | 306 | 2.1340 | 11.95 | 41.12 |
| 4.0765 | 4.0 | 408 | 1.9205 | 16.16 | 45.03 |
| 3.5864 | 5.0 | 510 | 1.8011 | 19.4 | 47.85 |
| 3.2950 | 6.0 | 612 | 1.7248 | 20.64 | 49.1 |
| 3.0101 | 7.0 | 714 | 1.6681 | 22.29 | 50.61 |
| 2.8216 | 8.0 | 816 | 1.6316 | 23.22 | 51.44 |
| 2.6885 | 9.0 | 918 | 1.6067 | 23.66 | 51.96 |
| 2.5958 | 10.0 | 1020 | 1.5905 | 24.02 | 52.34 |
| 2.4861 | 11.0 | 1122 | 1.5783 | 24.4 | 52.97 |
| 2.3989 | 12.0 | 1224 | 1.5652 | 24.52 | 53.01 |
| 2.3291 | 13.0 | 1326 | 1.5633 | 24.79 | 53.18 |
| 2.2925 | 14.0 | 1428 | 1.5598 | 24.74 | 53.17 |
| 2.2736 | 15.0 | 1530 | 1.5613 | 24.87 | 53.33 |
Framework versions
- Transformers 5.10.2
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- Type
- m2m_100
- License
- cc-by-nc-4.0
- Created
- Jun 12, 2026
- Updated
- Jun 12, 2026