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---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->


```
wandb: Run history:
wandb:               eval/loss β–‚β–β–β–ƒβ–„β–‚β–ƒβ–…β–ˆ
wandb:            eval/runtime β–†β–…β–β–ƒβ–ˆβ–‡β–‡β–ˆβ–‡
wandb: eval/samples_per_second β–ƒβ–„β–ˆβ–…β–β–‚β–‚β–β–‚
wandb:   eval/steps_per_second β–ƒβ–„β–ˆβ–†β–β–‚β–‚β–β–‚
wandb:             train/epoch β–β–β–ƒβ–ƒβ–…β–…β–†β–†β–ˆβ–ˆβ–ˆβ–β–β–ƒβ–ƒβ–…β–…β–†β–†β–†
wandb:       train/global_step β–β–β–ƒβ–ƒβ–…β–…β–†β–†β–ˆβ–ˆβ–ˆβ–β–β–ƒβ–ƒβ–…β–…β–†β–†β–†
wandb:         train/grad_norm β–β–‚β–‚β–ƒβ–…β–‚β–ƒβ–ˆβ–„
wandb:     train/learning_rate β–ˆβ–†β–…β–ƒβ–β–ˆβ–†β–…β–ƒ
wandb:              train/loss β–ˆβ–„β–„β–ƒβ–‚β–„β–ƒβ–‚β–
wandb: 
wandb: Run summary:
wandb:                eval/loss 0.5546
wandb:             eval/runtime 24.4864
wandb:  eval/samples_per_second 13.722
wandb:    eval/steps_per_second 4.574
wandb:               total_flos 4.700706095198208e+16
wandb:              train/epoch 4
wandb:        train/global_step 1316
wandb:          train/grad_norm 0.67448
wandb:      train/learning_rate 1e-05
wandb:               train/loss 0.3392
wandb:               train_loss 0.40499
wandb:            train_runtime 1677.5794
wandb: train_samples_per_second 7.845
wandb:   train_steps_per_second 0.981



Llama-3.2-3B-ocr-correction-3-instruction-corrected-real-data.json
Average PCIS: -0.00324434
Average Dataset CER: 0.01391665
Average Model CER: 0.01699565
Average Dataset WER: 0.06207812
Average Model WER: 0.07673994


Llama-3.2-3B-ocr-correction-3-instruction-corrected-synth-data.json
Average PCIS: -0.09734535
Average Dataset CER: 0.09836092
Average Model CER: 0.19219267
Average Dataset WER: 0.21986217
Average Model WER: 1.01884786

```

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
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## Uses

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### Direct Use

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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