---
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 ▂▁▁▃▄▂▃▅█
wandb:            eval/runtime ▆▅▁▃█▇▇█▇
wandb: eval/samples_per_second ▃▄█▅▁▂▂▁▂
wandb:   eval/steps_per_second ▃▄█▆▁▂▂▁▂
wandb:             train/epoch ▁▁▃▃▅▅▆▆███▁▁▃▃▅▅▆▆▆
wandb:       train/global_step ▁▁▃▃▅▅▆▆███▁▁▃▃▅▅▆▆▆
wandb:         train/grad_norm ▁▂▂▃▅▂▃█▄
wandb:     train/learning_rate █▆▅▃▁█▆▅▃
wandb:              train/loss █▄▄▃▂▄▃▂▁
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 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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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

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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