Instructions to use behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_GrounTruth_withPrompt_Seed104 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_GrounTruth_withPrompt_Seed104 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Trelis/Llama-2-7b-chat-hf-sharded-bf16") model = PeftModel.from_pretrained(base_model, "behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_GrounTruth_withPrompt_Seed104") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58bd3d4207ab6657b5a7e8997192ada226534af3ccac4602d2d12fd24356d0a6
- Size of remote file:
- 67.2 MB
- SHA256:
- 529ead54d401c5a13beea27859b2c07c4f11b4cc0a447f9bde8ddf5a7af0e1b8
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