---
base_model:
- LiquidAI/LFM2-VL-450M
---

# model_step_10000

## Model Description

This model is a fine-tuned version of **LiquidAI/LFM2-VL-450M** using the brute-force-training package.

- **Base Model**: LiquidAI/LFM2-VL-450M
- **Training Status**: 🔄 In Progress
- **Generated**: 2025-08-18 22:55:43
- **Training Steps**: 10,000

## Training Details

### Dataset
- **Dataset**: wjbmattingly/catmus-edited2
- **Training Examples**: 94,000
- **Validation Examples**: 499

### Training Configuration
- **Max Steps**: 100,000
- **Batch Size**: 2
- **Learning Rate**: 1e-05
- **Gradient Accumulation**: 1 steps
- **Evaluation Frequency**: Every 10,000 steps

### Current Performance
- **Training Loss**: 0.294965
- **Evaluation Loss**: 0.455527

## Pre-Training Evaluation

**Initial Model Performance (before training):**
- **Loss**: 1.270437
- **Perplexity**: 3.56
- **Character Accuracy**: 36.8%
- **Word Accuracy**: 17.1%

## Evaluation History

### All Checkpoint Evaluations
| Step | Checkpoint Type | Loss | Perplexity | Char Acc | Word Acc | Improvement vs Pre |
|------|----------------|------|------------|----------|----------|--------------------|
| Pre | pre_training | 1.2704 | 3.56 | 36.8% | 17.1% | +0.0% |
| 10,000 | checkpoint | 0.4555 | 1.58 | 39.0% | 20.1% | +64.1% |

## Training Progress

### Recent Training Steps (Loss Only)
| Step | Training Loss | Timestamp |
|------|---------------|-----------|
| 9,991 | 0.335487 | 2025-08-18T22:55 |
| 9,992 | 0.482083 | 2025-08-18T22:55 |
| 9,993 | 0.439921 | 2025-08-18T22:55 |
| 9,994 | 0.496600 | 2025-08-18T22:55 |
| 9,995 | 0.394777 | 2025-08-18T22:55 |
| 9,996 | 0.394860 | 2025-08-18T22:55 |
| 9,997 | 0.483459 | 2025-08-18T22:55 |
| 9,998 | 0.395718 | 2025-08-18T22:55 |
| 9,999 | 0.601364 | 2025-08-18T22:55 |
| 10,000 | 0.294965 | 2025-08-18T22:55 |

## Training Visualizations

### Training Progress and Evaluation Metrics
![Training Curves](training_curves.png)

*This chart shows the training loss progression, character accuracy, word accuracy, and perplexity over time. Red dots indicate evaluation checkpoints.*

### Evaluation Comparison Across All Checkpoints  
![Evaluation Comparison](evaluation_comparison.png)

*Comprehensive comparison of all evaluation metrics across training checkpoints. Red=Pre-training, Blue=Checkpoints, Green=Final.*

### Available Visualization Files:
- **`training_curves.png`** - 4-panel view: Training loss with eval points, Character accuracy, Word accuracy, Perplexity
- **`evaluation_comparison.png`** - 4-panel comparison: Loss, Character accuracy, Word accuracy, Perplexity across all checkpoints

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# For vision-language models, use appropriate imports

model = AutoModelForCausalLM.from_pretrained("./model_step_10000")
tokenizer = AutoTokenizer.from_pretrained("./model_step_10000")

# Your inference code here
```

## Training Configuration

```json
{
  "dataset_name": "wjbmattingly/catmus-edited2",
  "model_name": "LiquidAI/LFM2-VL-450M",
  "max_steps": 100000,
  "eval_steps": 10000,
  "num_accumulation_steps": 1,
  "learning_rate": 1e-05,
  "train_batch_size": 2,
  "val_batch_size": 2,
  "train_select_start": 0,
  "train_select_end": 94000,
  "val_select_start": 94001,
  "val_select_end": 94500,
  "train_field": "Textualis",
  "val_field": "Textualis",
  "image_column": "im",
  "text_column": "text",
  "user_text": "Transcribe this medieval manuscript line.",
  "max_image_size": 200
}
```

## Model Card Metadata

- **Base Model**: LiquidAI/LFM2-VL-450M
- **Training Framework**: brute-force-training
- **Training Type**: Fine-tuning
- **License**: Inherited from base model
- **Language**: Inherited from base model

---

*This model card was automatically generated by brute-force-training on 2025-08-18 22:55:43*