Instructions to use nirasha/qwen2.5-7b-fact-inclusion-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nirasha/qwen2.5-7b-fact-inclusion-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "nirasha/qwen2.5-7b-fact-inclusion-lora") - Notebooks
- Google Colab
- Kaggle
Qwen2.5-7B Fact Inclusion LoRA
Task
Given:
- Summary triple
- Supporting text triples
The model predicts:
- is_supported
- supporting evidence
- confidence
- reasoning
Training
Framework: Unsloth + PEFT
Method: LoRA fine-tuning
Dataset: Custom triplet-based fact inclusion annotations
Developed by: nirasha
License: apache-2.0
Finetuned from model : unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with Unsloth
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