PEFT
Safetensors
Transformers
English
mistral
lora
transcript-chunking
text-segmentation
topic-detection
Instructions to use Dc-4nderson/transcript_summarizer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dc-4nderson/transcript_summarizer_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "Dc-4nderson/transcript_summarizer_model") - Transformers
How to use Dc-4nderson/transcript_summarizer_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dc-4nderson/transcript_summarizer_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 622f9e62436d2a22ab5ca97b6ad1739627b4c931ccb83aee77bd72861101dbae
- Size of remote file:
- 6.23 kB
- SHA256:
- c85591e86f33ebe00c3e5e95721579b789318287854612c98a5c9a0ab47055ce
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