Home/Catalog/Hardcore Developers
High DemandHardcore DevelopersShu-Ha-Ri Method

Build Your Own Domain Specific Small Language Model (SLM)

The Domain SLM Mastery Stack™ — Bigger Isn't Always Better, Focused Is Faster

The ONLY masterclass teaching domain-specific SLMs that outperform frontier LLMs by 20-40% on specialized tasks—while running on a $2K laptop with zero API costs.

This is not another course on API integration. This is executive business education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and AI architects. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from API consumer burning $50K-$500K/month to SLM architect owning specialized models in 9 transformative steps.

Each module begins with a TedTalk-style presentation on strategy, then you immediately build it yourself with hands-on coding. You'll master fine-tuning, quantization (4-bit/8-bit), ONNX optimization, and cross-platform deployment from cloud to edge to mobile.

Different from our Frontier AI masterclass: While "Build Frontier AI Systems" teaches you to build and scale large production systems with MoE and MLA, this course focuses on making models smaller, faster, and specialized—achieving 75-87.5% compression while maintaining 90%+ performance. Instead of building bigger to serve millions, you're building smarter to run on $2K laptops with zero API costs. Bigger isn't always better. Focused is faster.

By the end, you won't just understand domain-specific AI—you'll own production-ready specialized models that eliminate vendor dependency, run anywhere, and become your competitive moat.

FROM
API Consumer
$50K-$500K/month burn · Vendor Lock-In
TO
SLM Architect
$0 API costs · Complete Ownership
9 weeks · 45 hours · Run frontier-quality models on $2K laptops
Start Your TransformationSee The Journey
The Domain SLM Mastery Stack™

Your 9-Step Transformation Journey

Each step follows the Shu-Ha-Ri method: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.Watch as you progress from API consumer to model builder, building your competitive moat with every step.

Weeks 1-3

PHASE 1: Foundation

Specialization & Optimization Mastery

FROM
“API consumer burning cash, no control over models, vendor-dependent”
TO
“Domain SLM builder fine-tuning specialized models, optimizing with ONNX, eliminating API costs”
🛡️ Domain Specialization Capability
Ability to build focused models that outperform general LLMs for specific tasks while running on commodity hardware
Weeks 4-6

PHASE 2: Optimization

Compression & Deployment Excellence

FROM
“Models too large for production, can't deploy to edge/mobile, limited by cloud GPUs”
TO
“Compression expert quantizing to 4-bit, deploying anywhere (laptop, mobile, edge, air-gapped)”
🛡️ Cross-Platform Deployment Expertise
Master quantization and ONNX optimization to deploy frontier-quality models on $2K laptops and mobile devices
Weeks 7-9

PHASE 3: Production Mastery

Complete Systems & Advanced Capabilities

FROM
“Standalone models with limited capabilities, no RAG/agent integration, basic inference only”
TO
“Complete AI systems architect building production RAG, agentic AI, and reasoning-enhanced SLMs”
🛡️ The Domain SLM Ownership Stack™
End-to-end capability from fine-tuning → quantization → deployment → production systems—own your AI stack completely

The Complete Transformation Matrix

Each step follows the Shu-Ha-Ri cycle: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.This is the guided progression that transforms API consumers into model builders.

1

Step 1: Domain-Specific AI Strategy & Architecture

FROM (Point A)
“Use general LLMs through APIs, no understanding of when smaller models outperform larger ones”
TO (Point B)
“Architect domain-specific AI strategy, understand transformer internals, choose right model for each use case”
🛡️ Strategic SLM architecture expertise—know when to specialize vs generalize
2

Step 2: Data Mastery & Model Specialization

FROM (Point A)
“Rely on pre-trained general models, no custom domain data, limited to API capabilities”
TO (Point B)
“Prepare domain-specific datasets, fine-tune transformers with LoRA, create specialized models for your field”
🛡️ Domain fine-tuning expertise—transform general models into specialized experts
3

Step 3: Production Inference & Generation Techniques

FROM (Point A)
“Basic text generation, no optimization, inefficient GPU usage, high inference costs”
TO (Point B)
“Master inference optimization, code generation, few-shot learning, batching strategies, DeepSpeed acceleration”
🛡️ Production inference optimization—10x throughput at 1/10th the cost
4

Step 4: Runtime Optimization & Cross-Platform Deployment

FROM (Point A)
“PyTorch-only models, cloud GPU dependency, can't deploy to production edge/mobile environments”
TO (Point B)
“Master ONNX conversion, runtime providers (CPU/CUDA/TensorRT), I/O binding, cross-platform optimization”
🛡️ ONNX deployment mastery—run anywhere from cloud to Raspberry Pi
5

Step 5: Applied SLMs: Code & Biomolecular Intelligence

FROM (Point A)
“Generic models for specialized tasks, no domain-specific applications for code or science”
TO (Point B)
“Build GitHub Copilot-quality code generators, protein/antibody design models, scientific AI applications”
🛡️ Applied domain SLM expertise—solve real-world problems with specialized models
6

Step 6: Advanced Compression & Performance Analysis

FROM (Point A)
“Large models requiring expensive GPUs, no compression techniques, can't run on commodity hardware”
TO (Point B)
“Master 4-bit/8-bit quantization, FlexGen, SmoothQuant, BitNet (1-bit), achieve 75-87.5% compression”
🛡️ Extreme compression expertise—run frontier-quality models on laptops and edge devices
7

Step 7: Production Deployment & Local Execution

FROM (Point A)
“Cloud-only deployment, API dependency, can't run offline or on-premise, privacy concerns”
TO (Point B)
“Deploy with vLLM/FastAPI for production, run locally with Ollama/LM Studio, enable on-premise/air-gapped execution”
🛡️ Complete deployment autonomy—eliminate vendor lock-in, deploy anywhere
8

Step 8: End-to-End AI Systems & Intelligent Retrieval

FROM (Point A)
“Standalone models, no RAG integration, limited context, can't build agentic systems”
TO (Point B)
“Build production RAG with vector DBs, Graph RAG for multi-hop reasoning, agentic AI with memory management”
🛡️ Complete AI systems architecture—build enterprise-grade applications with owned SLMs
9

Step 9: Reasoning Enhancement & Test-Time Optimization

FROM (Point A)
“Basic inference only, no reasoning capabilities, limited to model's base performance”
TO (Point B)
“Integrate test-time compute (chain-of-thought, self-consistency), build reasoning-enhanced SLMs, OptiLLM proxy”
🛡️ The Domain SLM Ownership Stack™—complete mastery from fine-tuning to reasoning-enhanced production systems

The Shu-Ha-Ri Learning Method

Ancient Japanese martial arts philosophy adapted for elite technical education. Each module follows this complete cycle—by Step 9, you've experienced Shu-Ha-Ri nine times, building deeper mastery with every iteration.

📚

Shu (守) - Learn

TedTalk-style masterclass + guided hands-on coding

“Watch attention mechanisms explained, then code them yourself with step-by-step guidance”

🔨

Ha (破) - Break

Modify code, experiment with parameters, adapt to your problems

“Change attention heads from 8 to 12, try different learning rates, debug training instability”

🚀

Ri (離) - Transcend

Apply independently, innovate beyond what's taught

“Design novel architectures for your domain, solve your specific business problems, lead AI initiatives”

This is how you transcend from passive learner to active innovator. This is executive business education merged with hands-on mastery.

Proven Transformation Results

Real outcomes from students who mastered The Domain SLM Mastery Stack™ and eliminated API costs entirely

📈 Career Transformation

75%
Promoted to Senior+ within 12 months
$80K-$150K
Average salary increase
90%
Report being 'irreplaceable' at their company
85%
Lead AI initiatives after completion

💰 Business Impact

$150K/year
Average API cost savings from owning model weights
70%
Eliminate third-party model dependencies entirely
60%
Raise funding citing proprietary technology as moat
3-6 months
Average time to ROI on course investment

What You'll Actually Build

🏗️
Complete GPT
4,000+ lines of PyTorch
🧠
Attention
From scratch, no libraries
📊
Training
100M+ tokens
🎯
Classification
95%+ accuracy
💬
ChatBot
Instruction-following

Choose Your Path to Mastery

All modalities include the complete Domain SLM Mastery Stack™. Choose based on your learning style and goals.

Self-Paced Mastery

$1,997
Lifetime Access
Self-directed learners
  • Lifetime access to 40-60 hours of comprehensive video content
  • 36 hands-on coding segments with Shu-Ha-Ri methodology (Learn → Build → Transcend)
  • Complete code repositories, datasets, and model checkpoints
  • Production deployment templates and ONNX optimization toolkits
  • Community forum access with peer support
  • Monthly group Q&A calls with instructors
  • Email support (48-hour response time)
  • Lifetime updates to all course materials
  • SLM Deployment Checklist and Model Compression Toolkit bonuses
Most Popular

9-Week Live Cohort

$6,997
12 Weeks
Engineers wanting accountability
  • Everything in Self-Paced PLUS:
  • 18 live 2-hour sessions (Tuesdays/Thursdays) over 9 weeks
  • Weekly 1-hour office hours every Friday
  • Private Discord community with 30-50 cohort peers
  • 3 milestone project reviews (weeks 3, 6, 9) with detailed feedback
  • 1:1 mid-program check-in (30 minutes) to ensure you're on track
  • Career Accelerator Workshop ($497 value) — resume, portfolio, interview prep
  • Founder's Pitch Deck Template ($297 value) — fundraise with 'owned AI moat' positioning
  • Alumni network access (400+ SLM architects and founders)
  • Priority email/Discord support (24-hour response)
  • Certificate of completion with portfolio showcase

Founder's Edition

$19,997
6 Months
Founders & technical leaders
  • Everything in Cohort PLUS:
  • 6× private 1-hour coaching sessions (biweekly) with SLM expert
  • Custom SLM architecture design for your specific domain/use case
  • 3× detailed code reviews of your implementations with optimization guidance
  • Hands-on deployment support for your first production SLM
  • Hiring guidance (for founders): JDs, interview questions, candidate assessment
  • Career strategy (for engineers): job search, interview prep, salary negotiation
  • Priority instructor access (24-hour response on Slack/email)
  • Unlimited support during program + 90-day post-program support
  • SLM Hiring Playbook ($997 value) — hire and assess SLM talent
  • Enterprise Sales Kit ($1,497 value) — pitch on-premise AI to Fortune 500

5-Day Immersive Bootcamp

Executive format: Monday-Friday intensive (8am-6pm). Build complete GPT in one week. Limited to 15 participants for maximum attention.

Course Curriculum

15 transformative steps · 45 hours of hands-on content

1

Module 1: Large Language Models Overview

6 lessons · Shu-Ha-Ri cycle

  • Executive Overview: When Small Models Beat Large Ones
  • The Transformer Architecture: A Visual Refresher
  • Evolutions of Transformers
  • The Open Source Revolution
  • Risks and Challenges with Generalist LLMs
  • When Domain-Specific SLMs Provide Greater Business Value
2

Module 2: Tuning for a Specific Domain

8 lessons · Shu-Ha-Ri cycle

  • Data Preparation Fundamentals
  • Data Preparation for BERT Fine-Tuning
  • Data Preparation for GPT Fine-Tuning
  • Data Preparation for RAG Applications
  • Retrieval Augmented Generation with SLMs
  • Fine-Tuning Strategies
  • LoRA: Low-Rank Adaptation for Efficient Training
  • RAG or Fine-Tuning? When to Use Each
3

Module 3: End-to-End Transformer Fine-Tuning

5 lessons · Shu-Ha-Ri cycle

  • Data Preparation for Your Domain
  • Fine-Tuning Process: Step by Step
  • Testing the Fine-Tuned Model
  • Domain-Specific Evaluation Metrics
  • Iterating on Your Results
4

Module 4: Running Inference

9 lessons · Shu-Ha-Ri cycle

  • How to Generate Content with SLMs
  • Text Completion Strategies
  • Few-Shot Learning
  • Code Generation
  • Evaluating Generated Content
  • Inference Cost Calculation
  • Getting the Most from Your GPU
  • Batching Strategies
  • Optimizing GPU Usage with DeepSpeed
5

Module 5: Exploring ONNX

7 lessons · Shu-Ha-Ri cycle

  • The ONNX Format: Why It Matters
  • ONNX Operators and Types
  • The ONNX Runtime
  • ONNX Runtime Providers
  • ONNX for LLMs on CPU
  • ONNX for LLMs on GPU
  • I/O Binding for Performance
6

Module 6: Quantizing for Production

8 lessons · Shu-Ha-Ri cycle

  • Transformer Precision Formats Explained
  • 8-Bit Quantization: Theory and Practice
  • Hands-On 8-Bit Quantization
  • LLM.int8() and Quantization
  • 8-Bit Quantization with ONNX
  • 4-Bit Quantization with GPTQ
  • 4-Bit Quantization with ggml
  • Choosing the Right Precision for Your Use Case
7

Module 7: Generating Python Code

6 lessons · Shu-Ha-Ri cycle

  • Transformers for Programming Language Generation
  • Python Code Generation with CodeGen
  • ONNX Conversion and Quantization for Custom Models
  • Model Evaluation for Code Generation
  • Python Code Generation with Better Models
  • Inference (Coding Assistance) on Commodity Hardware
8

Module 8: Generating Protein Structures

5 lessons · Shu-Ha-Ri cycle

  • Application of Transformers in Chemistry
  • From Natural Language to Protein Structures
  • Antibody Generation with SLMs
  • From CIF Files to Crystal Structures
  • Domain-Specific Models for Scientific Applications
9

Module 9: Advanced Quantization Techniques

5 lessons · Shu-Ha-Ri cycle

  • What If a Domain-Specific Model Isn't Small?
  • FlexGen: Offloading to Disk and CPU
  • SmoothQuant: Activation-Aware Quantization
  • BitNet: 1-Bit Language Models
  • Implementing BitNet in Python
10

Module 10: Profiling Insights

4 lessons · Shu-Ha-Ri cycle

  • Profiling ONNX-Ported LLMs
  • Transforming Raw Profiling Data into Insights
  • Optimization of ONNX Graphs for LLMs
  • Identifying Bottlenecks and Fixing Them
11

Module 11: Deployment and Serving

5 lessons · Shu-Ha-Ri cycle

  • vLLM: Offline and Online Serving
  • FastAPI: Building Production APIs
  • Benchmarking Various Models
  • Deploying the Most Performant Model with FastAPI
  • MLC LLM: Cross-Platform Deployment
12

Module 12: Running on Your Laptop

7 lessons · Shu-Ha-Ri cycle

  • Why a Personal Local Assistant?
  • Running LLMs Locally with Ollama
  • Importing Custom Models into Ollama
  • User Privacy in Ollama
  • Running LLMs with LM Studio
  • The LM Studio Python SDK
  • Running LLMs with Jan and Cortex
13

Module 13: Deployment on Mobile Devices

5 lessons · Shu-Ha-Ri cycle

  • Inference on Android Devices
  • MLC LLM Framework for Mobile
  • MLLM Framework
  • Hugging Face Transformers on Mobile
  • Optimizing for Mobile Constraints
14

Module 14: End-to-End LLM Applications

7 lessons · Shu-Ha-Ri cycle

  • Why LLMs Alone Aren't Enough
  • Combining Domain-Specific SLMs with RAG
  • Using Vector Databases with SLMs
  • Building an Agent Powered by an SLM
  • Graph RAG with SLMs
  • RAG + Agentic AI
  • Long- and Short-Term Memory Management
15

Module 15: Test-Time Compute and Reasoning

5 lessons · Shu-Ha-Ri cycle

  • Test-Time Compute: What It Is and Why It Matters
  • The OptiLLM Inference Proxy
  • SLMs with Embedded Test-Time Compute
  • Building a Reasoning Domain-Specific SLM
  • Capstone: Your Production-Ready Domain-Specific SLM

Production-Grade Tech Stack

Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems

For Career Advancers

I help ML engineers and AI specialists build production-ready domain-specific language models that run on commodity hardware, so they can command $250K-$400K salaries and eliminate $50K-$200K monthly API costs without being commoditized as API integrators or locked into vendor dependencies.

For Founders & CTOs

I help technical founders and CTOs build proprietary domain-specific AI models that eliminate 90-99% of API costs, so they can raise funding at 2-3x premium valuations with defensible moats without burning $500K/month on vendor APIs or settling for commodity 'wrapper' business models.

PyTorchHugging FaceONNXvLLMOllamaLM StudioGPTQLoRADeepSpeed

Frequently Asked Questions

Why would I use an SLM instead of modern ChatGPT/Claude/Gemini?

Cost, speed, privacy, and control. SLMs run on your hardware, don't send data to third parties, respond faster for domain-specific tasks, and cost nothing per query after deployment.

What hardware do I need?

A laptop with a decent GPU is sufficient. The course teaches quantization and optimization techniques specifically to enable running on commodity hardware.

Do I need to train models from scratch?

No. You'll learn to fine-tune existing open-source models for your domain. This is far more practical than training from scratch and delivers excellent results.

What domains does this cover?

The principles apply to any domain. We use code generation and protein structures as examples, but you'll learn techniques that work for legal, medical, financial, or any specialized field.

Will I build something that actually works?

Yes. You'll build domain-specific models, quantize them for production, deploy with vLLM or FastAPI, and run on your laptop with Ollama. Real production systems, not toy demos.

How is this different from the Fine-Tuning course?

The Fine-Tuning course focuses on adapting any model with LoRA and QLoRA. This course goes deeper into SLMs specifically—quantization, ONNX optimization, mobile deployment, and domain-specific applications.

Stop Renting AI. Start Owning It.

Join 500+ engineers and founders who've gone from API consumers to model builders—building their competitive moats one step at a time.

Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.

Starting at
$1,997

Self-paced · Lifetime access · 30-day guarantee

Start Your Transformation

This is not just education. This is technological sovereignty.

30-day guarantee
Lifetime updates
Zero API costs forever