Instructions to use havinash-ai/377d8250-51d3-4829-b83f-26d1ea9dfb50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use havinash-ai/377d8250-51d3-4829-b83f-26d1ea9dfb50 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-instruct") model = PeftModel.from_pretrained(base_model, "havinash-ai/377d8250-51d3-4829-b83f-26d1ea9dfb50") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from havinash-ai/377d8250-51d3-4829-b83f-26d1ea9dfb50: direct link, hf CLI and curl.
- Browser
- Download file 25.9 MB
-
https://huggingface.co/havinash-ai/377d8250-51d3-4829-b83f-26d1ea9dfb50/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://havinash-ai/377d8250-51d3-4829-b83f-26d1ea9dfb50/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/havinash-ai/377d8250-51d3-4829-b83f-26d1ea9dfb50/resolve/main/last-checkpoint/optimizer.pt
25.9 MB
- Xet hash:
- 0901e13c305d1a93b0178b15ed354dcba345ebc57691cbb585284ca8f26050d7
- Size of remote file:
- 25.9 MB
- SHA256:
- fe216a4df297ef0c04148ed779465fe7c63f668dd673186e2afcc4c026e5cce0
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