Instructions to use Cshavi/qwen_lora_ft_self_instruct_filttered with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Cshavi/qwen_lora_ft_self_instruct_filttered 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, "Cshavi/qwen_lora_ft_self_instruct_filttered") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Cshavi/qwen_lora_ft_self_instruct_filttered: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Cshavi/qwen_lora_ft_self_instruct_filttered/resolve/main/tokenizer.json
- Command line
-
hf download hf://Cshavi/qwen_lora_ft_self_instruct_filttered/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Cshavi/qwen_lora_ft_self_instruct_filttered/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 29bb1cfd207fe6fbbf7b0052e845b4580a9a45c6ea2e1118211ef2672619dc9b
- Size of remote file:
- 11.4 MB
- SHA256:
- 09ae1170fe6d155dd07f6038b04c251d1ebdab1b020382645cdaba1c25ed63eb
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