Deepseek Ocr2 Chart v1

--- library_name: peft license: apache-2.0 base_model: deepseek-ai/DeepSeek-OCR-2 tags: - base_model:adapter:deepseek-ai/DeepSeek-OCR-2 - lora - transformers - ml-intern pipeline_tag: text-generation model-index: - name: deepseek-ocr2-chart-v1 results: [] --- # deepseek-ocr2-chart-v1 This model is a fine-tuned version of [deepseek-ai/DeepSeek-OCR-2](https://huggingface.co/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 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.3769 | 0.5714 | 1 | 1.2913 | | 1.3769 | 1.7143 | 3 | 0.9567 | | 0.9714 | 2.8571 | 5 | 0.7840 | | 0.9714 | 4.0 | 7 | 0.7222 | | 0.9714 | 4.5714 | 8 | 0.7006 | | 0.5974 | 5.7143 | 10 | 0.6978 | ### Framework versions - PEFT 0.19.1 - Transformers 4.46.3 - Pytorch 2.6.0+cu124 - Datasets 4.8.5 - Tokenizers 0.20.3 ## Generated by ML Intern This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. - Try ML Intern: https://smolagents-ml-intern.hf.space - Source code: https://github.com/huggingface/ml-intern ## Usage ```python 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.