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---
library_name: transformers
license: apache-2.0
base_model: Trelis/SmolLM-135M-Instruct-layer-pruned-90M-raw
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: 99-v9
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. -->
# 99-v9
This model is a fine-tuned version of [Trelis/SmolLM-135M-Instruct-layer-pruned-90M-raw](https://huggingface.co/Trelis/SmolLM-135M-Instruct-layer-pruned-90M-raw) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7495
## 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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.005
- lr_scheduler_warmup_steps: 89
- training_steps: 17894
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.6331 | 0.0500 | 894 | 0.6004 |
| 0.5667 | 0.0999 | 1788 | 0.5463 |
| 0.5423 | 0.1499 | 2682 | 0.5138 |
| 0.5749 | 0.1998 | 3576 | 0.7377 |
| 0.5378 | 0.2498 | 4470 | 0.7542 |
| 0.506 | 0.2998 | 5364 | 0.7902 |
| 0.5561 | 0.3497 | 6258 | 0.7810 |
| 0.5259 | 0.3997 | 7152 | 0.7914 |
| 0.5516 | 0.4496 | 8046 | 0.7611 |
| 0.5131 | 0.4996 | 8940 | 0.6860 |
| 0.5069 | 0.5496 | 9834 | 0.7247 |
| 0.4977 | 0.5995 | 10728 | 0.7375 |
| 0.4976 | 0.6495 | 11622 | 0.7436 |
| 0.5018 | 0.6995 | 12516 | 0.7520 |
| 0.537 | 0.7494 | 13410 | 0.7613 |
| 0.5018 | 0.7994 | 14304 | 0.6922 |
| 0.4891 | 0.8493 | 15198 | 0.7322 |
| 0.4808 | 0.8993 | 16092 | 0.7430 |
| 0.5231 | 0.9493 | 16986 | 0.7546 |
| 0.5103 | 0.9992 | 17880 | 0.7495 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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