Instructions to use aleegis10/02904e21-5e73-46cc-843d-d0fba5342136 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis10/02904e21-5e73-46cc-843d-d0fba5342136 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-1.7B") model = PeftModel.from_pretrained(base_model, "aleegis10/02904e21-5e73-46cc-843d-d0fba5342136") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from aleegis10/02904e21-5e73-46cc-843d-d0fba5342136: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/aleegis10/02904e21-5e73-46cc-843d-d0fba5342136/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://aleegis10/02904e21-5e73-46cc-843d-d0fba5342136/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aleegis10/02904e21-5e73-46cc-843d-d0fba5342136/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- a02645202086ce3dc216e820f04470d2e506086bd584b4f03c4ae018fa6bb8a5
- Size of remote file:
- 6.84 kB
- SHA256:
- 493dad4b560cf042109188669c92564ee700a0e4a39bf93301405eff97dea472
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