Instructions to use hongyeeliu/Control_Summaries_Qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hongyeeliu/Control_Summaries_Qwen with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "hongyeeliu/Control_Summaries_Qwen") - Notebooks
- Google Colab
- Kaggle
Control Summaries Qwen (LoRA adapter)
This is a LoRA adapter for controllable summarization trained for the paper Learning to Control Summaries with Score Ranking.
Model Details
- Model type: LoRA adapter for a causal language model (summarization control)
- Language(s) (NLP): English
- Finetuned from model: Qwen-7B
Model Sources
- Repository: https://github.com/Hyfred/Control-Summaries-with-Ranking
- Paper: Learning to Control Summaries with Score Ranking
Uses
Direct Use
Use this adapter with the base model for controllable summarization (more complete / more concise / balanced prompts).
How to Get Started
Please refer to the code repository for usage and evaluation scripts: https://github.com/Hyfred/Control-Summaries-with-Ranking
Bias, Risks, and Limitations
The model inherits limitations and potential biases from the base model and training data. Summaries can be incomplete or contain errors. Review outputs before use in critical settings.
- PEFT 0.14.0
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