YUNGHUI2024/deepseek-ocr2-chart-v1

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deepseek-ocr2-chart-v1

This model is a fine-tuned version of deepseek-ai/DeepSeek-OCR-2 on an unknown dataset. It achieves the following results on the evaluation set:

• —Loss: 0.6978

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0002
• —trainbatchsize: 1
• —evalbatchsize: 1
• —seed: 42
• —gradientaccumulationsteps: 8
• —totaltrainbatch_size: 8
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 10

Training results

Framework versions

• —PEFT 0.19.1
• —Transformers 4.46.3
• —Pytorch 2.6.0+cu124
• —Datasets 4.8.5
• —Tokenizers 0.20.3

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Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

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• —Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'YUNGHUI2024/deepseek-ocr2-chart-v1'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.