Instructions to use yashcse21/financial-sentiment-grpo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yashcse21/financial-sentiment-grpo-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "yashcse21/financial-sentiment-grpo-lora") - Notebooks
- Google Colab
- Kaggle
Financial Sentiment GRPO (baseline)
Fine-tuned with reinforcement learning on Financial PhraseBank (sentences_50agree).
AI League #5 Problem Statement 2 POC.
loss_type=grpo,scale_rewards=True(standard GRPO baseline)- Verifiable label reward in
<answer>tags - Base model:
unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit
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Model tree for yashcse21/financial-sentiment-grpo-lora
Base model
Qwen/Qwen2.5-0.5B Finetuned
Qwen/Qwen2.5-0.5B-Instruct Quantized
unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit