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", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf1c1d264353ee2684964f535804e0e820cfed89a18bf515b3826cf85fc9ae3c
- Size of remote file:
- 671 MB
- SHA256:
- ed5d379b75f0c2c1c55fadb64af29093e2d08bc60c46f32ccaf6600575d2ed68
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