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wjbmattingly/
lfm2-vl-450m-caroline

Name: lfm2-vl-450m-caroline
Availability: InStock

safetensors

lfm2-vl

custom_code

LiquidAI/LFM2-VL-450M

finetune

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model_step_7000

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 20:39:32
Training Steps: 7,000

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: 1e-05
Gradient Accumulation: 1 steps
Evaluation Frequency: Every 500 steps

Current Performance

Training Loss: 0.619257
Evaluation Loss: 0.722366

Pre-Training Evaluation

Initial Model Performance (before training):

Loss: 1.297430
Perplexity: 3.66
Character Accuracy: 27.7%
Word Accuracy: 11.6%

Evaluation History

All Checkpoint Evaluations

Step	Checkpoint Type	Loss	Perplexity	Char Acc	Word Acc	Improvement vs Pre
Pre	pre_training	1.2974	3.66	27.7%	11.6%	+0.0%
500	checkpoint	0.9454	2.57	39.4%	19.9%	+27.1%
1,000	checkpoint	0.8644	2.37	38.7%	19.1%	+33.4%
1,500	checkpoint	0.8402	2.32	38.4%	18.9%	+35.2%
2,000	checkpoint	0.8139	2.26	37.9%	19.8%	+37.3%
2,500	checkpoint	0.7890	2.20	38.5%	18.9%	+39.2%
3,000	checkpoint	0.7793	2.18	39.3%	19.5%	+39.9%
3,500	checkpoint	0.7639	2.15	42.7%	21.4%	+41.1%
4,000	checkpoint	0.7483	2.11	41.2%	20.4%	+42.3%
4,500	checkpoint	0.7466	2.11	37.3%	18.8%	+42.5%
5,000	checkpoint	0.7358	2.09	40.4%	20.5%	+43.3%
5,500	checkpoint	0.7321	2.08	38.1%	18.9%	+43.6%
6,000	checkpoint	0.7276	2.07	38.8%	17.6%	+43.9%
6,500	checkpoint	0.7190	2.05	41.5%	18.9%	+44.6%
7,000	checkpoint	0.7224	2.06	41.6%	18.7%	+44.3%

Training Progress

Recent Training Steps (Loss Only)

Step	Training Loss	Timestamp
6,991	0.846698	2025-08-18T20:39
6,992	0.538150	2025-08-18T20:39
6,993	0.721188	2025-08-18T20:39
6,994	0.819544	2025-08-18T20:39
6,995	0.925656	2025-08-18T20:39
6,996	0.724563	2025-08-18T20:39
6,997	0.738329	2025-08-18T20:39
6,998	0.658910	2025-08-18T20:39
6,999	0.439738	2025-08-18T20:39
7,000	0.619257	2025-08-18T20:39

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, 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_7000")
tokenizer = AutoTokenizer.from_pretrained("./model_step_7000")

# Your inference code here

Training Configuration

json

{
  "dataset_name": "CATMuS/medieval",
  "model_name": "LiquidAI/LFM2-VL-450M",
  "max_steps": 10000,
  "eval_steps": 500,
  "num_accumulation_steps": 1,
  "learning_rate": 1e-05,
  "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 20:39:32

wjbmattingly/lfm2-vl-450m-caroline

model_step_7000

Model Description

Training Details

Dataset

Training Configuration

Current Performance

Pre-Training Evaluation

Evaluation History

All Checkpoint Evaluations

Training Progress

Recent Training Steps (Loss Only)

Training Visualizations

Training Progress and Evaluation Metrics

Evaluation Comparison Across All Checkpoints

Available Visualization Files:

Usage

Training Configuration

Model Card Metadata

wjbmattingly/
lfm2-vl-450m-caroline