Instructions to use JMLizano/FinRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JMLizano/FinRL with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "JMLizano/FinRL") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JMLizano/FinRL: direct link, hf CLI and curl.
- Browser
- Download file 7.12 kB
-
https://huggingface.co/JMLizano/FinRL/resolve/main/training_args.bin
- Command line
-
hf download hf://JMLizano/FinRL/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JMLizano/FinRL/resolve/main/training_args.bin
7.12 kB
- Xet hash:
- ad0c9f4fa4960bcc1a9b5c6a65e26e37393ba87b2d8cc1c1c0465516c8ed5677
- Size of remote file:
- 7.12 kB
- SHA256:
- e2aff62215860fbf8f00dd4289887396fd16560721e4ac703cc618e8b3a9e87d
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