LLAMA 3 Story Point Estimator - mulestudio

This model is fine-tuned on issue descriptions from mulestudio and tested on mulestudio for story point estimation.

Model Details

  • Base Model: LLAMA 3.2 1B

  • Training Project: mulestudio

  • Test Project: mulestudio

  • Task: Story Point Estimation (Regression)

  • Architecture: PEFT (LoRA)

  • Input: Issue titles

  • Output: Story point estimation (continuous value)

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftConfig, PeftModel

# Load peft config model
config = PeftConfig.from_pretrained("DEVCamiloSepulveda/0-LLAMA3SP-mulestudio")

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DEVCamiloSepulveda/0-LLAMA3SP-mulestudio")
base_model = AutoModelForSequenceClassification.from_pretrained(
    config.base_model_name_or_path,
    num_labels=1,
    torch_dtype=torch.float16,
    device_map='auto'
)
model = PeftModel.from_pretrained(base_model, "DEVCamiloSepulveda/0-LLAMA3SP-mulestudio")

# Prepare input text
text = "Your issue description here"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=20, padding="max_length")

# Get prediction
outputs = model(**inputs)
story_points = outputs.logits.item()

Training Details

  • Fine-tuning method: LoRA (Low-Rank Adaptation)
  • Sequence length: 20 tokens
  • Best training epoch: 1 / 20 epochs
  • Batch size: 32
  • Training time: 38.202 seconds
  • Mean Absolute Error (MAE): 3.728
  • Median Absolute Error (MdAE): 2.755

Framework versions

  • PEFT 0.14.0
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