Instructions to use svjack/Qwen2-7B_Function_Call_tiny_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use svjack/Qwen2-7B_Function_Call_tiny_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-7B-Instruct") model = PeftModel.from_pretrained(base_model, "svjack/Qwen2-7B_Function_Call_tiny_lora") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-300/optimizer.pt from svjack/Qwen2-7B_Function_Call_tiny_lora: direct link, hf CLI and curl.
- Browser
- Download file 162 MB
-
https://huggingface.co/svjack/Qwen2-7B_Function_Call_tiny_lora/resolve/main/checkpoint-300/optimizer.pt
- Command line
-
hf download hf://svjack/Qwen2-7B_Function_Call_tiny_lora/checkpoint-300/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/svjack/Qwen2-7B_Function_Call_tiny_lora/resolve/main/checkpoint-300/optimizer.pt
162 MB
- Xet hash:
- 8345c4a8ebe367628868892b1c0fe6ae213dbcb9931aae43ef34b509682dd2be
- Size of remote file:
- 162 MB
- SHA256:
- 69da12865994f4be0a5bb3b1962d3e80e55ba78ef7bb8cfac4a2ccc77e118b03
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