Instructions to use arhamk/llama2-qlora-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use arhamk/llama2-qlora-sft 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, "arhamk/llama2-qlora-sft") - Transformers
How to use arhamk/llama2-qlora-sft with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arhamk/llama2-qlora-sft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1f386570dc2c8c1796684c5aac059c3602333fd27fb9477549765f8686e5cf0
- Size of remote file:
- 134 MB
- SHA256:
- 2a3d9403fcfa74f2302f1f015bbe860822348279aa4687c4b2694bb7174f773c
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