Instructions to use JunHowie/Fin-R1-GPTQ-Int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHowie/Fin-R1-GPTQ-Int8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="JunHowie/Fin-R1-GPTQ-Int8")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("JunHowie/Fin-R1-GPTQ-Int8") model = AutoModelForMultimodalLM.from_pretrained("JunHowie/Fin-R1-GPTQ-Int8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3994cc3f50f60ab829c0eb619c37cb98f06dca4f42ee44f0c09eafddc8def616
- Size of remote file:
- 11.4 MB
- SHA256:
- 9ffb4bd5c40b82c8e9ee9be5eb1632ad555eb42250ccc4eece6325a37e78ba1a
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