Summarization
Transformers
PyTorch
English
llama
text-generation
Meeting
Summarization
text-generation-inference
Instructions to use MeetPEFT/MeetPEFT-7B-16K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MeetPEFT/MeetPEFT-7B-16K with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="MeetPEFT/MeetPEFT-7B-16K")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MeetPEFT/MeetPEFT-7B-16K") model = AutoModelForCausalLM.from_pretrained("MeetPEFT/MeetPEFT-7B-16K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
datasets:
- huuuyeah/meetingbank
- pszemraj/qmsum-cleaned
language:
- en
pipeline_tag: summarization
tags:
- Meeting
- Summarization
MeetPEFT: Parameter Efficient Fine-Tuning on LLMs for Long Meeting Summarization
We use quantized LongLoRA to fine-tune a Llama-2-7b model and extend the context length from 4k to 16k.
The model is fine-tuned on MeetingBank and QMSum datasets.