README.md

metadata

license: llama3.2
language:
  - en
base_model:
  - meta-llama/Llama-3.2-1B
pipeline_tag: text-classification
library_name: transformers
tags:
  - regression
  - story-point-estimation
  - software-engineering
datasets:
  - mule
  - titanium
metrics:
  - mae
  - mdae
model-index:
  - name: llama-3.2-1b-story-point-estimation
    results:
      - task:
          type: regression
          name: Story Point Estimation
        dataset:
          type: titanium
          name: titanium Dataset
          split: test
        metrics:
          - type: mae
            value: 3.505
            name: Mean Absolute Error (MAE)
          - type: mdae
            value: 2.195
            name: Median Absolute Error (MdAE)

LLAMA 3 Story Point Estimator - mule - titanium

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

Model Details

• Base Model: LLAMA 3.2 1B

• Training Project: mule

• Test Project: titanium

• 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 PeftModel

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DEVCamiloSepulveda/000-LLAMA3SP-mule-titanium")
model = AutoModelForSequenceClassification.from_pretrained("DEVCamiloSepulveda/000-LLAMA3SP-mule-titanium")

# 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: 0 / 20 epochs
• Batch size: 32
• Training time: 18.795 seconds
• Mean Absolute Error (MAE): 3.505
• Median Absolute Error (MdAE): 2.195