Instructions to use kineticseas/llama7b-python-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kineticseas/llama7b-python-finetuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded") model = PeftModel.from_pretrained(base_model, "kineticseas/llama7b-python-finetuned") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kineticseas/llama7b-python-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/kineticseas/llama7b-python-finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://kineticseas/llama7b-python-finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kineticseas/llama7b-python-finetuned/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 109657f84db0b661b595c47765e7c2e6e8a35fa9024780ee01cb45ded8b4e5cf
- Size of remote file:
- 4.66 kB
- SHA256:
- c1d8cd1de7d3d13836d56c2d4702be17e746b8aa0070ffa6c363c28c36480721
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