Instructions to use Imsachinsingh00/Fine_tuned_LoRA_Mistral_MTSDialog_Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Imsachinsingh00/Fine_tuned_LoRA_Mistral_MTSDialog_Summarization with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "Imsachinsingh00/Fine_tuned_LoRA_Mistral_MTSDialog_Summarization") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Imsachinsingh00/Fine_tuned_LoRA_Mistral_MTSDialog_Summarization: direct link, hf CLI and curl.
- Browser
- Download file 3.51 MB
-
https://huggingface.co/Imsachinsingh00/Fine_tuned_LoRA_Mistral_MTSDialog_Summarization/resolve/main/tokenizer.json
- Command line
-
hf download hf://Imsachinsingh00/Fine_tuned_LoRA_Mistral_MTSDialog_Summarization/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Imsachinsingh00/Fine_tuned_LoRA_Mistral_MTSDialog_Summarization/resolve/main/tokenizer.json
3.51 MB
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