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
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Download auxiliary_decoder/README.md from InternScience/ChartVLM-large: direct link, hf CLI and curl.
- Browser
- Download file 464 Bytes
-
https://huggingface.co/InternScience/ChartVLM-large/resolve/main/auxiliary_decoder/README.md
- Command line
-
hf download hf://InternScience/ChartVLM-large/auxiliary_decoder/README.md
-
curl -L -o README.md https://huggingface.co/InternScience/ChartVLM-large/resolve/main/auxiliary_decoder/README.md
464 Bytes
metadata
library_name: peft
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
Framework versions
- PEFT 0.4.0