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---
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
- mulestudio
metrics:
- mae
- mdae
model-index:
- name: llama-3.2-1b-story-point-estimation
results:
- task:
type: regression
name: Story Point Estimation
dataset:
type: mulestudio
name: mulestudio Dataset
split: test
metrics:
- type: mae
value: 3.566
name: Mean Absolute Error (MAE)
- type: mdae
value: 2.399
name: Median Absolute Error (MdAE)
---
# LLAMA 3 Story Point Estimator - mule - mulestudio
This model is fine-tuned on issue descriptions from mule and tested on mulestudio for story point estimation.
## Model Details
- Base Model: LLAMA 3.2 1B
- Training Project: mule
- Test Project: mulestudio
- 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/00-LLAMA3SP-mule-mulestudio")
model = AutoModelForSequenceClassification.from_pretrained("DEVCamiloSepulveda/00-LLAMA3SP-mule-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: 0 / 20 epochs
- Batch size: 32
- Training time: 18.973 seconds
- Mean Absolute Error (MAE): 3.566
- Median Absolute Error (MdAE): 2.399
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