🚀 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
| 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
🚀 How to Use
1from transformers import AutoTokenizer, AutoModelForCausalLM
2from peft import PeftModel
3
4MODEL_NAME = "mistralai/Mistral-7B-v0.1"
5ADAPTER_NAME = "atharva31ak/sprint-intelligence-lora"
6
7tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
8
9base_model = AutoModelForCausalLM.from_pretrained(
10 MODEL_NAME,
11 device_map="auto"
12)
13
14model = PeftModel.from_pretrained(base_model, ADAPTER_NAME)
15
16prompt = """
17Analyze sprint data and return JSON with:
18- risks
19- recommendations
20- reasoning
21
22Input:
23{...your sprint JSON...}
24"""
25
26inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
27
28outputs = model.generate(
29 **inputs,
30 max_new_tokens=300
31)
32
33print(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
📚 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:
⚡ One-Line Summary
Fine-tuned Mistral model that converts sprint data into execution-ready intelligence using structured reasoning and agentic AI.
