---
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: chennus-2-70m
  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. -->

# chennus-2-70m

This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8759
- Accuracy: 0.0077

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0256        | 0.1616 | 200  | 1.0056          | 0.0079   |
| 0.9819        | 0.3231 | 400  | 0.9644          | 0.0078   |
| 0.9504        | 0.4847 | 600  | 0.9461          | 0.0077   |
| 0.9296        | 0.6462 | 800  | 0.9290          | 0.0078   |
| 0.9113        | 0.8078 | 1000 | 0.9182          | 0.0077   |
| 0.9121        | 0.9693 | 1200 | 0.9096          | 0.0077   |
| 0.9027        | 1.1309 | 1400 | 0.9026          | 0.0077   |
| 0.8894        | 1.2924 | 1600 | 0.8971          | 0.0077   |
| 0.8934        | 1.4540 | 1800 | 0.8924          | 0.0077   |
| 0.8839        | 1.6155 | 2000 | 0.8876          | 0.0077   |
| 0.8935        | 1.7771 | 2200 | 0.8843          | 0.0077   |
| 0.8847        | 1.9386 | 2400 | 0.8836          | 0.0077   |
| 0.8749        | 2.1002 | 2600 | 0.8819          | 0.0077   |
| 0.8817        | 2.2617 | 2800 | 0.8807          | 0.0077   |
| 0.8712        | 2.4233 | 3000 | 0.8787          | 0.0077   |
| 0.8768        | 2.5848 | 3200 | 0.8775          | 0.0077   |
| 0.8726        | 2.7464 | 3400 | 0.8772          | 0.0077   |
| 0.8789        | 2.9079 | 3600 | 0.8763          | 0.0077   |
| 0.8782        | 3.0695 | 3800 | 0.8765          | 0.0077   |
| 0.8724        | 3.2310 | 4000 | 0.8763          | 0.0077   |
| 0.8812        | 3.3926 | 4200 | 0.8767          | 0.0077   |
| 0.8727        | 3.5541 | 4400 | 0.8762          | 0.0077   |
| 0.8730        | 3.7157 | 4600 | 0.8767          | 0.0077   |
| 0.8694        | 3.8772 | 4800 | 0.8763          | 0.0077   |
| 0.8689        | 4.0388 | 5000 | 0.8760          | 0.0077   |
| 0.8791        | 4.2003 | 5200 | 0.8764          | 0.0077   |
| 0.8788        | 4.3619 | 5400 | 0.8764          | 0.0077   |
| 0.8717        | 4.5234 | 5600 | 0.8763          | 0.0077   |
| 0.8797        | 4.6850 | 5800 | 0.8761          | 0.0077   |
| 0.8744        | 4.8465 | 6000 | 0.8759          | 0.0077   |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2