README.md

metadata

license: llama3.2
language:
  - en
base_model: meta-llama/Llama-3.2-1B
pipeline_tag: text-classification
library_name: peft
tags:
  - regression
  - story-point-estimation
  - software-engineering
datasets:
  - clover
metrics:
  - mae
  - mdae
model-index:
  - name: llama-3.2-1b-story-point-estimation
    results:
      - task:
          type: regression
          name: Story Point Estimation
        dataset:
          name: clover Dataset
          type: clover
          split: test
        metrics:
          - type: mae
            value: 4.5
            name: Mean Absolute Error (MAE)
          - type: mdae
            value: 2.741
            name: Median Absolute Error (MdAE)

LLAMA 3 Story Point Estimator - clover

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

Model Details

• Base Model: LLAMA 3.2 1B

• Training Project: clover

• Test Project: clover

• Task: Story Point Estimation (Regression)

• Architecture: PEFT (LoRA)

• Tokenizer: SP Word Level

• Input: Issue titles

• Output: Story point estimation (continuous value)

Usage

from transformers import AutoModelForSequenceClassification
from peft import PeftConfig, PeftModel
from tokenizers import Tokenizer

# Load peft config model
config = PeftConfig.from_pretrained("DEVCamiloSepulveda/2-LLAMA3SP-clover")

# Load tokenizer and model
tokenizer = Tokenizer.from_pretrained("DEVCamiloSepulveda/2-LLAMA3SP-clover")
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/2-LLAMA3SP-clover")

# 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: 21.718 seconds
• Mean Absolute Error (MAE): 4.500
• Median Absolute Error (MdAE): 2.741

Framework versions

• PEFT 0.14.0