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lfm2-vl-450m-catmus
wjbmattingly/lfm2-vl-450m-catmus
lfm2-vl-450m-catmus is a machine learning model from wjbmattingly. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model is a fine-tuned version of LiquidAI/LFM2-VL-450M using the brute-force-training package.
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At a glance
- Model type
- lfm2-vl
- Access
- Public
- Created
- Aug 13, 2025
- Updated
- Aug 18, 2025
- SHA
- a0c499f2
Base models
Model card
From the Hugging Face model README
final
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: ✅ Complete
- Generated: 2025-08-18 19:08:04
- Training Steps: 10,000
Training Details
Dataset
- Dataset: CATMuS/medieval
- Training Examples: 148,000
- Validation Examples: 1,999
Training Configuration
- Max Steps: 10,000
- Batch Size: 2
- Learning Rate: 5e-06
- Gradient Accumulation: 2 steps
- Evaluation Frequency: Every 1,000 steps
Current Performance
- Training Loss: 1.841697
- Evaluation Loss: 3.094146
Pre-Training Evaluation
Initial Model Performance (before training):
- Loss: 6.023905
- Perplexity: 413.19
- Character Accuracy: 33.1%
- Word Accuracy: 18.0%
Evaluation History
All Checkpoint Evaluations
| Step | Checkpoint Type | Loss | Perplexity | Char Acc | Word Acc | Improvement vs Pre |
|---|---|---|---|---|---|---|
| Pre | pre_training | 6.0239 | 413.19 | 33.1% | 18.0% | +0.0% |
| 1,000 | checkpoint | 4.0098 | 55.14 | 26.1% | 11.8% | +33.4% |
| 2,000 | checkpoint | 3.6743 | 39.42 | 30.7% | 16.3% | +39.0% |
| 3,000 | checkpoint | 3.4875 | 32.70 | 32.1% | 16.0% | +42.1% |
| 4,000 | checkpoint | 3.3974 | 29.88 | 35.8% | 18.6% | +43.6% |
| 5,000 | checkpoint | 3.3062 | 27.28 | 33.5% | 16.7% | +45.1% |
| 6,000 | checkpoint | 3.2316 | 25.32 | 34.9% | 17.9% | +46.4% |
| 7,000 | checkpoint | 3.1867 | 24.21 | 34.1% | 17.9% | +47.1% |
| 8,000 | checkpoint | 3.1549 | 23.45 | 32.3% | 15.8% | +47.6% |
| 9,000 | checkpoint | 3.1265 | 22.80 | 31.6% | 16.6% | +48.1% |
| 10,000 | final | 3.0941 | 22.07 | 34.3% | 17.6% | +48.6% |
Training Progress
Recent Training Steps (Loss Only)
| Step | Training Loss | Timestamp |
|---|---|---|
| 9,991 | 1.941270 | 2025-08-18T19:07 |
| 9,992 | 2.647601 | 2025-08-18T19:07 |
| 9,993 | 3.605345 | 2025-08-18T19:07 |
| 9,994 | 3.034668 | 2025-08-18T19:07 |
| 9,995 | 2.445682 | 2025-08-18T19:07 |
| 9,996 | 3.361138 | 2025-08-18T19:07 |
| 9,997 | 1.670197 | 2025-08-18T19:07 |
| 9,998 | 2.518688 | 2025-08-18T19:07 |
| 9,999 | 2.755938 | 2025-08-18T19:07 |
| 10,000 | 1.841697 | 2025-08-18T19:07 |
Training Visualizations
Training Progress and Evaluation Metrics
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
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, Perplexityevaluation_comparison.png- 4-panel comparison: Loss, Character accuracy, Word accuracy, Perplexity across all checkpoints
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
# For vision-language models, use appropriate imports
model = AutoModelForCausalLM.from_pretrained("./final")
tokenizer = AutoTokenizer.from_pretrained("./final")
# Your inference code here
Training Configuration
{
"dataset_name": "CATMuS/medieval",
"model_name": "LiquidAI/LFM2-VL-450M",
"max_steps": 10000,
"eval_steps": 1000,
"num_accumulation_steps": 2,
"learning_rate": 5e-06,
"train_batch_size": 2,
"val_batch_size": 2,
"train_select_start": 0,
"train_select_end": 148000,
"val_select_start": 148001,
"val_select_end": 150000,
"train_field": "train",
"val_field": "train",
"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 19:08:04
- Type
- lfm2-vl
- Created
- Aug 13, 2025
- Updated
- Aug 18, 2025