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") - Notebooks
- Google Colab
- Kaggle
Update adapter_config.json
Browse files- adapter_config.json +1 -1
adapter_config.json
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"base_model_name_or_path":
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"base_model_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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