atharva31ak/sprint-intelligence-lora

🚀 Sprint Intelligence LoRA (Mistral-7B)

🧠 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.

📌 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

🏗️ 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)

🔬 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.

⚙️ Training Setup

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

🧠 Intended Use

✅ Direct Use

This model can be used to:

• —Analyze sprint/task JSON
• —Generate structured outputs:

• —risks
• —recommendations
• —reasoning

🔗 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.

❌ Out-of-Scope Use

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

⚠️ Limitations

• —May produce:

• —incomplete JSON
• —prompt echo
• —Requires:

• —strict prompt formatting
• —post-processing for reliability

🧪 Evaluation

Compared against:

• —Gemini
• —Llama 3.1 8B (Groq)

📊 Results

🧠 Why It Performs Better

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

🚀 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]))

🧩 Integration Example

This model is deployed using:

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

🤖 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.

🧠 Technical Highlights

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

🌍 Environmental Impact

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

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}
}

📬 Contact

For collaboration or questions:

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

⚡ One-Line Summary

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