Instructions to use jayzeng/llm-fun with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jayzeng/llm-fun with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B") model = PeftModel.from_pretrained(base_model, "jayzeng/llm-fun") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-475/optimizer.pt from jayzeng/llm-fun: direct link, hf CLI and curl.
- Browser
- Download file 1.04 GB
-
https://huggingface.co/jayzeng/llm-fun/resolve/main/checkpoint-475/optimizer.pt
- Command line
-
hf download hf://jayzeng/llm-fun/checkpoint-475/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/jayzeng/llm-fun/resolve/main/checkpoint-475/optimizer.pt
1.04 GB
- Xet hash:
- 416efc5ef71f12155bb06245218029aa4b57e86ec289f1d6d2168a35a98d80eb
- Size of remote file:
- 1.04 GB
- SHA256:
- 3409eea3a658828d7c53c1c009dff40a8d879cd2cbeb27a172a88c3dd178dbc9
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