atharva31ak/sprint-intelligence-lora

🚀 Sprint Intelligence LoRA (Mistral-7B)

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🧠 Model Overview

Sprint Intelligence LoRA is a fine-tuned large language model designed to analyze sprint data and generate structured execution intelligence.

Unlike generic LLMs, this model focuses on:

• Dependency-aware reasoning
• Blocker detection
• Critical path analysis
• Actionable execution planning

👉 It is built as the reasoning core of a larger agentic AI system that acts like a technical project manager.

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📌 Key Capabilities

• 🔗 Detects dependency chains and bottlenecks
• ⚠️ Identifies risks with confidence scoring
• 🎯 Generates prioritized recommendations
• 🧭 Understands execution flow (critical path)
• 📊 Handles noisy / real-world sprint data

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🏗️ Model Details

• Developed by: Atharva
• Model type: Causal Language Model (LLM)
• Base model: mistralai/Mistral-7B-v0.1
• Fine-tuning method: QLoRA (PEFT)
• Language: English
• License: Apache 2.0 (inherits base model license)

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🔬 Training Summary

📊 Dataset

• Total samples: 350
• Base samples: 250
• Edge cases: 100

⚠️ Edge Cases Included

• Missing fields (hours, assignee, priority)
• Broken dependencies (invalid task references)
• Conflicting priorities
• False blockers
• Inconsistent task states
• Ambiguous velocity signals

👉 This ensures robustness in real-world noisy environments.

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⚙️ Training Setup

• Technique: QLoRA (4-bit quantization)
• Library: Transformers + PEFT + BitsAndBytes
• Precision: FP16 compute
• Trainable params: ~6.8M (LoRA adapters)
• Total params: ~7.2B

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🧠 Intended Use

✅ Direct Use

This model can be used to:

• Analyze sprint/task JSON

• Generate structured outputs:

  • risks
  • recommendations
  • reasoning

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🔗 Downstream Use (Recommended)

Best performance is achieved when used inside an agentic pipeline:

• Dependency Agent → builds graph
• Risk Agent → detects blockers
• Critic Agent → validates outputs
• Execution Engine → generates step-by-step plan

👉 The model acts as a reasoning layer, not a standalone system.

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❌ Out-of-Scope Use

• General chat / conversation
• Creative writing
• Non-structured tasks
• Domains outside project execution

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⚠️ Limitations

• May produce:

  • incomplete JSON
  • prompt echo

• Requires:

  • strict prompt formatting
  • post-processing for reliability

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🧪 Evaluation

Compared against:

• Gemini
• Llama 3.1 8B (Groq)

📊 Results

Model	Score
Sprint Intelligence(finetuned Mistral 7B)	10/10
Gemini-2.5-flash	9/10
Llama 3.1 8B (Groq)	4/10

🧠 Why It Performs Better

• Structured reasoning
• Dependency correctness
• Execution-focused outputs
• Integration with agentic system

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🚀 How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

MODEL_NAME = "mistralai/Mistral-7B-v0.1"
ADAPTER_NAME = "atharva31ak/sprint-intelligence-lora"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

base_model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    device_map="auto"
)

model = PeftModel.from_pretrained(base_model, ADAPTER_NAME)

prompt = """
Analyze sprint data and return JSON with:
- risks
- recommendations
- reasoning

Input:
{...your sprint JSON...}
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=300
)

print(tokenizer.decode(outputs[0]))

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🧩 Integration Example

This model is deployed using:

• Modal (GPU inference)
• FastAPI endpoint
• Gradio UI (planned)

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🤖 Agentic AI Integration

This model is part of a multi-agent system:

• Task Analyzer
• Dependency Agent
• Risk Agent
• Critic Agent
• Standup Agent

👉 Agents refine and validate outputs → improving accuracy and explainability.

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🧠 Technical Highlights

• QLoRA fine-tuning (memory efficient)
• Structured JSON generation
• Real-world dataset with edge cases
• Hybrid system (LLM + deterministic logic)

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🌍 Environmental Impact

• Hardware: NVIDIA T4 (Colab + Modal)
• Training time: ~30–40 minutes
• Optimization: 4-bit quantization reduces compute cost significantly

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Demo link - https://drive.google.com/file/d/1jNzmLGwT-kn-7hPCihj1sKV3AmxKldXz/view?usp=sharing

📚 Citation

If you use this model, cite:

@misc{sprint_intelligence_2026,
  author = {Atharva},
  title = {Sprint Intelligence LoRA},
  year = {2026},
  publisher = {Hugging Face}
}

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📬 Contact

For collaboration or questions:

• Hugging Face: https://huggingface.co/atharva31ak

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⚡ One-Line Summary

Fine-tuned Mistral model that converts sprint data into execution-ready intelligence using structured reasoning and agentic AI.

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