wjbmattingly/lfm2-vl-1.6B-catmus

modelstep10000

Model Description

This model is a fine-tuned version of LiquidAI/LFM2-VL-1.6B using the brute-force-training package.

• —Base Model: LiquidAI/LFM2-VL-1.6B
• —Training Status: 🔄 In Progress
• —Generated: 2025-08-13 13:56:59
• —Training Steps: 10,000

Training Details

Dataset

• —Dataset: CATMuS/medieval
• —Training Examples: 120,000
• —Validation Examples: 29,999

Training Configuration

• —Max Steps: 100,000
• —Batch Size: 10
• —Learning Rate: 1e-05
• —Gradient Accumulation: 4 steps
• —Evaluation Frequency: Every 10,000 steps

Current Performance

• —Training Loss: 0.477249
• —Evaluation Loss: 0.725169

Pre-Training Evaluation

Initial Model Performance (before training):

• —Loss: 6.212058
• —Perplexity: 498.73
• —Character Accuracy: 18.6%
• —Word Accuracy: 2.0%

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

# Your inference code here

Training Configuration

{
  "dataset_name": "CATMuS/medieval",
  "model_name": "LiquidAI/LFM2-VL-1.6B",
  "max_steps": 100000,
  "eval_steps": 10000,
  "num_accumulation_steps": 4,
  "learning_rate": 1e-05,
  "train_batch_size": 10,
  "val_batch_size": 10,
  "train_select_start": 0,
  "train_select_end": 120000,
  "val_select_start": 120001,
  "val_select_end": 150000,
  "train_field": "train",
  "val_field": "train",
  "image_column": "im",
  "text_column": "text",
  "user_text": "Transcribe this medieval manuscript line"
}

Model Card Metadata

• —Base Model: LiquidAI/LFM2-VL-1.6B
• —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 2025-08-13 13:56:59