agk4444 / aditya369

Aditya369 — Qwen3-4B emotion classifier (merged)

by AGK FIRE INC

A Qwen3-4B model fine-tuned for emotion classification on the dair-ai/emotion dataset (20k English tweets/short texts). This is the merged version — LoRA weights fused into the base model, still 4-bit quantized. For the standalone LoRA adapter, see agk4444/aditya369-lora .

Labels

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("agk4444/aditya369")
tok = AutoTokenizer.from_pretrained("agk4444/aditya369")

labels = ["sadness", "joy", "love", "anger", "fear", "surprise"]

text = "I am so happy today!"
inputs = tok(text, return_tensors="pt")
pred = model(**inputs).logits.argmax(-1).item()
print(labels[pred])  # joy

Requires bitsandbytes (4-bit weights) and transformers .

Evaluation

Test-set results on dair-ai/emotion (2k held-out examples):

Training

• Base: Qwen/Qwen3-4B , loaded in 4-bit (bitsandbytes)
• Method: LoRA (r=16), fused into base weights after training
• Data: dair-ai/emotion — 16k train / 2k val / 2k test
• 3 epochs (~1,500 steps), gradient checkpointing on
• Trained on a single T4 (Kaggle)

Limitations

• English short-form text (tweets); performance on long documents or other languages is untested.
• Six coarse emotions only — no intensity scores, no mixed-emotion output.
• Inherits the biases of the base model and the tweet dataset.

© 2026 AGK FIRE INC. Released under Apache 2.0.