Instructions to use InternScience/ChartVLM-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InternScience/ChartVLM-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InternScience/ChartVLM-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download auxiliary_decoder/adapter_config.json from InternScience/ChartVLM-large: direct link, hf CLI and curl.
- Browser
- Download file 469 Bytes
-
https://huggingface.co/InternScience/ChartVLM-large/resolve/main/auxiliary_decoder/adapter_config.json
- Command line
-
hf download hf://InternScience/ChartVLM-large/auxiliary_decoder/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/InternScience/ChartVLM-large/resolve/main/auxiliary_decoder/adapter_config.json
469 Bytes
| { | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "/cpfs01/shared/ADLab/hug_ckpts/vicuna-13b-v1.5", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 16, | |
| "revision": null, | |
| "target_modules": [ | |
| "q_proj", | |
| "v_proj" | |
| ], | |
| "task_type": "CAUSAL_LM" | |
| } |