Instructions to use prxshetty/llama-3.1-8b-story-point-estimator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prxshetty/llama-3.1-8b-story-point-estimator with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prxshetty/llama-3.1-8b-story-point-estimator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prxshetty/llama-3.1-8b-story-point-estimator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prxshetty/llama-3.1-8b-story-point-estimator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prxshetty/llama-3.1-8b-story-point-estimator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prxshetty/llama-3.1-8b-story-point-estimator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prxshetty/llama-3.1-8b-story-point-estimator

SGLang

How to use prxshetty/llama-3.1-8b-story-point-estimator with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prxshetty/llama-3.1-8b-story-point-estimator" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prxshetty/llama-3.1-8b-story-point-estimator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prxshetty/llama-3.1-8b-story-point-estimator" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prxshetty/llama-3.1-8b-story-point-estimator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use prxshetty/llama-3.1-8b-story-point-estimator with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prxshetty/llama-3.1-8b-story-point-estimator to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prxshetty/llama-3.1-8b-story-point-estimator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prxshetty/llama-3.1-8b-story-point-estimator to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="prxshetty/llama-3.1-8b-story-point-estimator",
    max_seq_length=2048,
)

Docker Model Runner
How to use prxshetty/llama-3.1-8b-story-point-estimator with Docker Model Runner:
```
docker model run hf.co/prxshetty/llama-3.1-8b-story-point-estimator
```

Llama 3.1 8B Story Point Estimator

A fine-tuned Llama 3.1 8B model specialized for agile story point estimation in software development workflows.

Overview

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.

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Model tree for prxshetty/llama-3.1-8b-story-point-estimator

Base model

unsloth/Meta-Llama-3.1-8B

Adapter

(286)

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