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DEVCamiloSepulveda/0-LLAMA3SP-datamanagement

sourceHugging Facellama3.2updated 2y agoView on Hugging Face
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LLAMA 3 Story Point Estimator - datamanagement

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

Model Details

  • —Base Model: LLAMA 3.2 1B
  • —Training Project: datamanagement
  • —Test Project: datamanagement
  • —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 PeftConfig, PeftModel

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

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DEVCamiloSepulveda/0-LLAMA3SP-datamanagement")
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-datamanagement")

# 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: 101.233 seconds
  • —Mean Absolute Error (MAE): 6.511
  • —Median Absolute Error (MdAE): 3.661

Framework versions

  • —PEFT 0.14.0