File size: 1,712 Bytes
5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 5c08ce3 d93aa08 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
library_name: peft
base_model: sarvamai/sarvam-1
tags:
- generated_from_trainer
model-index:
- name: akash_v1
results: []
---
<!-- 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. -->
# akash_v1
This model is a fine-tuned version of [sarvamai/sarvam-1](https://huggingface.co/sarvamai/sarvam-1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 10.6444
## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 10.6339 | 1.0000 | 21071 | 10.6742 |
| 10.6283 | 2.0000 | 42142 | 10.6585 |
| 10.6017 | 3.0000 | 63213 | 10.6522 |
| 10.6133 | 4.0000 | 84284 | 10.6463 |
| 10.5949 | 5.0000 | 105355 | 10.6444 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0 |