---
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:
- mulestudio
- 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: 3.652
        name: Mean Absolute Error (MAE)
      - type: mdae
        value: 2.105
        name: Median Absolute Error (MdAE)
---
# LLAMA 3 Story Point Estimator - mulestudio - titanium

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

## Model Details
- Base Model: LLAMA 3.2 1B
- Training Project: mulestudio
- Test Project: titanium
- Task: Story Point Estimation (Regression)
- Architecture: PEFT (LoRA)

- Input: Issue titles
- Output: Story point estimation (continuous value)

## Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftModel

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DEVCamiloSepulveda/000-LLAMA3SP-mulestudio-titanium")
model = AutoModelForSequenceClassification.from_pretrained("DEVCamiloSepulveda/000-LLAMA3SP-mulestudio-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: 3 / 20 epochs
- Batch size: 32
- Training time: 162.250 seconds
- Mean Absolute Error (MAE): 3.652
- Median Absolute Error (MdAE): 2.105