from transformers import AutoModelForSequenceClassification
from peft import PeftConfig, PeftModel
from transformers import AutoTokenizer

# Load peft config model
config = PeftConfig.from_pretrained("DEVCamiloSepulveda/111-DeepSeekR1SP-appceleratorstudio-mule")

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DEVCamiloSepulveda/111-DeepSeekR1SP-appceleratorstudio-mule")
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/111-DeepSeekR1SP-appceleratorstudio-mule")

# 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: 4 / 20 epochs
Batch size: 32
Training time: 673.869 seconds
Mean Absolute Error (MAE): 3.177
Median Absolute Error (MdAE): 2.539

Framework versions

PEFT 0.14.0