Instructions to use Sairam22/matcha-chartqa-lora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sairam22/matcha-chartqa-lora-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/matcha-base") model = PeftModel.from_pretrained(base_model, "Sairam22/matcha-chartqa-lora-adapter") - Transformers
How to use Sairam22/matcha-chartqa-lora-adapter with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sairam22/matcha-chartqa-lora-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sairam22/matcha-chartqa-lora-adapter: direct link, hf CLI and curl.
- Browser
- Download file 5.39 kB
-
https://huggingface.co/Sairam22/matcha-chartqa-lora-adapter/resolve/main/training_args.bin
- Command line
-
hf download hf://Sairam22/matcha-chartqa-lora-adapter/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sairam22/matcha-chartqa-lora-adapter/resolve/main/training_args.bin
5.39 kB
- Xet hash:
- 8cbe70d812971e1114d096e795d8b09fa446b9974ddd12bac1e2e019d060920f
- Size of remote file:
- 5.39 kB
- SHA256:
- 5b5bab1990146cee3ade11648abbba476853a88bead64a3bc289528b2c4fb5ea
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