wjbmattingly/lfm2-vl-450m-catmus

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

Training Progress

Recent Training Steps (Loss Only)

Training Visualizations

Training Progress and Evaluation Metrics

[image]

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

[image]

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

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