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fintune-qwen2.5-1.5b-lora
wrtdevcod/fintune-qwen2.5-1.5b-lora
fintune-qwen2.5-1.5b-lora is a machine learning model from wrtdevcod. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
LoRA (r=16, alpha=32) adapter fine-tuned on the Financial PhraseBank dataset (sentences50agree, 4,846 sentences, 80/10/10 split) for 3-class financial sentiment classification (positive / negative / neutral).
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At a glance
- Library
- peft
- Access
- Public
- Created
- Aug 17, 2026
- Updated
- Aug 17, 2026
- SHA
- f559d6a9
Base models
Datasets
Model card
From the Hugging Face model README
FinTune: LoRA-adapted Qwen2.5-1.5B-Instruct for Financial Sentiment
LoRA (r=16, alpha=32) adapter fine-tuned on the Financial PhraseBank dataset (sentences_50agree, 4,846 sentences, 80/10/10 split) for 3-class financial sentiment classification (positive / negative / neutral).
Trained with a custom PyTorch training loop (manual forward pass, loss
computation, backward pass, and optimizer step — no Trainer class) on a
free-tier Colab T4 GPU using 4-bit QLoRA.
Benchmark (held-out test set, N=486)
| Model | Accuracy | Macro F1 |
|---|---|---|
| Base Qwen2.5-1.5B-Instruct | 50.6% | 0.540 |
| Fine-tuned (this adapter) | 88.9% | 0.887 |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "wrtdevcod/fintune-qwen2.5-1.5b-lora")
- Library
- peft
- Created
- Aug 17, 2026
- Updated
- Aug 17, 2026