Item	Details
Base checkpoint	`unsloth/Meta-Llama-3.1-8B`
Fine-tune method	LoRA (PEFT) with Unsloth
Training run	1 epoch • 60 max steps • Custom dataset
Trainable params	LoRA adapters only
Task	Story point estimation (1-20 scale)
Hardware	Google Colab (T4/V100)
License	Llama 3.1
Intended use	Agile development & estimation assistance

Usage

from unsloth import FastLanguageModel

Load the model
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="prxshetty/llama-3.1-8b-story-point-estimator",
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)

Switch to inference mode
FastLanguageModel.for_inference(model)

Format your prompt using Llama 3.1 chat template
prompt = f"""<|begin_of_text|><|start_header_id|>user<|end_header_id|>

You are an expert agile estimation assistant. Your task is to estimate story points for a software development task.

CONTEXT:
Story points are a team-specific, unit-less measure of relative effort required to complete a backlog item in agile software development.

ESTIMATION SCALE:
Range: minimum 1, maximum 20

TRAINING EXAMPLE:
Issue Title: {title}
Issue Description: {description}

INSTRUCTIONS:
Based on the example above, analyze the complexity, technical difficulty, unknowns, and scope of work.
Provide only the story point estimate as a single integer.<|eot_id|><|start_header_id|>assistant<|end_header_id|>

"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=10, do_sample=False)
response = tokenizer.decode(outputs[inputs['input_ids'].shape:], skip_special_tokens=True)

Training Details

Parameter	Value
Epochs	1
Max Steps	60
Batch Size	2 (per device)
Gradient Accumulation	4 steps
Learning Rate	2e-4
Optimizer	AdamW 8-bit
Weight Decay	0.01
Warmup Steps	5

The model was trained using train_on_responses_only to optimize only on the story point outputs, not the input prompts.

Responsible Use

Intended for estimation assistance only. This model provides suggestions based on training patterns and should not replace human judgment in agile planning. Always:

Use as a starting point for team discussions
Validate estimates with domain experts
Consider team-specific velocity and context
Review and adjust based on historical performance

The author and base-model creators accept no liability for project planning decisions based on this model's outputs.

Model Architecture

Built on Meta's Llama 3.1 8B architecture with LoRA (Low-Rank Adaptation) fine-tuning for parameter efficiency. The model uses the standard Llama 3.1 chat template format for consistent inference.