Background
I needed high-quality instruction datasets for fine-tuning local LLMs, but commercial options were prohibitively expensive ($500-2,000 for decent quality datasets).
So I asked myself: What if I could build a system that generates these datasets autonomously while I sleep?
Result: 1,065 professional instruction/response pairs in 72 hours, 100% local, zero API costs.
Here's exactly how I did it.
The Problem with Existing Datasets
When you want to fine-tune a local LLM for specific tasks, you face three options:
- Use generic datasets → Don't match your domain
- Manual creation → Exhausting, slow, doesn't scale
- Buy commercial datasets → Expensive ($500-2,000), limited customization
I wanted code instruction pairs with:
- Python/AI/ML/DevOps/Database topics
- Chain-of-Thought reasoning (not just answers)
- Real-world complexity (no template responses)
- Continuous generation (24/7 if possible)
None of the existing options delivered all four.
The Solution: Multi-Agent Autonomous System
I built a three-agent system inspired by academic research workflows:
┌──────────────────┐
│ Curator │ ← Selects topics from knowledge base
│ (Qwen 7B) │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ Producer │ ← Generates instruction + response + CoT
│ (Qwen 7B) │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ Critic │ ← Validates logic, checks hallucinations
│ (DeepSeek 1.5B)│
└────────┬─────────┘
│
▼
[Accept/Reject]
Agent Roles
Curator Agent:
- Reads ChromaDB vector store of potential topics
- Selects next topic based on diversity + priority
- Prevents duplicates via similarity search
- Think of it as a research librarian
Producer Agent:
- Generates the actual instruction/response pair
- Includes Chain-of-Thought reasoning
- Uses domain knowledge to create realistic scenarios
- Think of it as the expert writer
Critic Agent:
- Reviews Producer's output for:
- Hallucinations
- Logical errors
- Incomplete reasoning
- Generic template responses
- Binary decision: Accept or Reject
- Think of it as the peer reviewer
Tech Stack
Core Components
# 1. Ollama (Local LLM Engine)
# Custom Modelfiles with 8k context:
qwen2.5-coder-8k:7b # 4.7GB, main generator
deepseek-r1-8k:1.5b # 1.1GB, validator
# 2. CrewAI (Agent Orchestration)
from crewai import Agent, Crew, Task
# 3. ChromaDB (Deduplication + Memory)
from chromadb import Client
# 4. Flask (Real-time Dashboard)
from flask import Flask, render_template
Hardware
- AMD Ryzen AI 9 HX 370 (12 cores, 32GB RAM shared as VRAM)
- Mini PC (Geekom A9 Max, ~$899)
- SSD NVMe (for fast model loading)
Total investment: $899 + ~$3.60 electricity for 72 hours
Implementation Details
Challenge 1: Memory Leaks in Long Runs
Problem: Agent instances accumulate state. After ~100 cycles, performance degrades → crashes.
Solution: Recreate all agents every cycle.
from crewai import Agent, Crew, Task
for cycle in range(1000):
# Recreate agents from scratch (prevents memory leaks)
curator = Agent(
role="Topic Curator",
goal="Select next topic to generate",
llm=ollama_qwen
)
producer = Agent(
role="Content Producer",
goal="Generate high-quality instruction pair",
llm=ollama_qwen
)
critic = Agent(
role="Quality Critic",
goal="Validate logic and catch hallucinations",
llm=ollama_deepseek
)
# Define sequential workflow
tasks = [
Task(description="Select topic", agent=curator),
Task(description="Generate content", agent=producer),
Task(description="Validate quality", agent=critic)
]
crew = Crew(agents=[curator, producer, critic], tasks=tasks)
result = crew.kickoff()
# Explicit cleanup
del crew, curator, producer, critic
Result: Zero crashes in 72 hours. Stable RAM at 24.2 GB.
Challenge 2: CrewAI + Ollama Compatibility
Problem: CrewAI v1.12+ requires string-only responses, but Ollama returns objects by default.
Solution: Embed configuration in Modelfiles (permanent fix).
# Modelfile for qwen2.5-coder-8k:7b
FROM qwen2.5-coder:7b
PARAMETER num_ctx 8192
PARAMETER temperature 0.7
PARAMETER top_p 0.9
SYSTEM """You are an expert programmer..."""
Run:
ollama create qwen2.5-coder-8k:7b -f Modelfile_qwen
ollama create deepseek-r1-8k:1.5b -f Modelfile_deepseek
This makes num_ctx: 8192 permanent at the model level. No runtime config needed.
Challenge 3: Duplicate Questions
Problem: Random topic generation creates duplicates.
Solution: ChromaDB similarity search + rejection.
import chromadb
client = chromadb.Client()
collection = client.create_collection("generated_questions")
def is_duplicate(new_question, threshold=0.85):
"""Check if question already exists via embedding similarity"""
results = collection.query(
query_texts=[new_question],
n_results=1
)
if not results['ids']:
return False
similarity = results['distances'][0][0]
return similarity > threshold
# Before saving
if not is_duplicate(instruction):
save_to_dataset(entry)
collection.add(
documents=[instruction],
ids=[unique_id]
)
Result: 452 unique topics across 1,065 entries (zero duplicates).
Results After 72 Hours
Metrics
| Metric | Value |
|---|---|
| Total Entries | 1,065 |
| Success Rate | 50.9% (605 accepted / 460 rejected) |
| Unique Topics | 452 |
| Velocity | 14.4 entries/hour |
| RAM Usage | 24.2 GB (stable) |
| CPU Usage | 31.1% average |
| Crashes | 0 |
Distribution by Category
- Python: 346 entries (33%)
- AI/ML: 287 entries (27%)
- Backend/API: 126 entries (12%)
- Database/SQL: 99 entries (9%)
- DevOps/Cloud: 99 entries (9%)
- Other: 108 entries (10%)
Quality Samples
Example 1: Python Development
{
"instruction": "How to create a Dockerfile for a Python application?",
"input": "",
"output": "# Step-by-step guide to create a production-ready Dockerfile:\n\n1. **Choose Base Image**\n - Use official Python image: `python:3.11-slim`\n - For smaller size: `python:3.11-alpine`\n\n2. **Set Working Directory**\n ```
dockerfile\n WORKDIR /app\n
```\n\n3. **Copy Requirements First** (leverage Docker cache)\n ```
dockerfile\n COPY requirements.txt .\n RUN pip install --no-cache-dir -r requirements.txt\n
```\n\n4. **Copy Application Code**\n ```
dockerfile\n COPY . .\n
```\n\n5. **Set Environment Variables**\n ```
dockerfile\n ENV PYTHONUNBUFFERED=1\n ENV PORT=8000\n
```\n\n6. **Expose Port**\n ```
dockerfile\n EXPOSE $PORT\n
```\n\n7. **Define Entrypoint**\n ```
dockerfile\n CMD [\"python\", \"app.py\"]\n
```\n\n**Complete Example:**\n```
dockerfile\nFROM python:3.11-slim\nWORKDIR /app\nCOPY requirements.txt .\nRUN pip install --no-cache-dir -r requirements.txt\nCOPY . .\nENV PYTHONUNBUFFERED=1\nEXPOSE 8000\nCMD [\"uvicorn\", \"main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"]\n
```\n\n**Best Practices:**\n- Use multi-stage builds for smaller images\n- Don't run as root (create user)\n- Use .dockerignore to exclude unnecessary files\n- Pin dependency versions in requirements.txt",
"thought": "User needs containerization setup. Key considerations: 1) Layer caching optimization (copy requirements first), 2) Security (non-root user), 3) Image size (slim vs alpine tradeoff), 4) Production readiness (health checks, signals). Template should be practical and immediately usable."
}
Character counts:
- Output: 928 chars
- Thought: 266 chars
Example 2: AI/ML Engineering
{
"instruction": "Explain the difference between EC2 instances and Lambda functions for ML model deployment",
"input": "",
"output": "**EC2 Instances vs Lambda for ML Deployment:**\n\n**EC2 Instances (Virtual Machines)**\n\n*Pros:*\n- Full control over environment (custom libraries, CUDA, etc.)\n- No execution time limits\n- Better for large models (multi-GB)\n- GPU support (P3, G4 instances)\n- Persistent connections (WebSockets, streaming)\n- Predictable latency\n\n*Cons:*\n- Always running = always paying ($50-500+/month)\n- Manual scaling configuration\n- Need to manage OS, security patches\n- Cold start when scaling from 0\n\n*Best for:*\n- Real-time inference with steady traffic\n- Models >1GB\n- GPU-intensive workloads\n- Long-running predictions (>15 min)\n\n**Lambda Functions (Serverless)**\n\n*Pros:*\n- Pay per invocation ($0.20 per 1M requests)\n- Auto-scaling (0 to thousands)\n- No server management\n- Built-in HA/fault tolerance\n\n*Cons:*\n- 15-minute timeout\n- Limited to 10GB RAM\n- No GPU support\n- Cold starts (1-5 seconds)\n- Package size limit (250MB unzipped)\n\n*Best for:*\n- Batch predictions\n- Small models (<100MB)\n- Sporadic/unpredictable traffic\n- Budget constraints\n\n**Hybrid Approach:**\nUse SageMaker Serverless Inference:\n- Lambda-like pricing\n- Support for larger models\n- Automatic scaling\n- GPU available\n\n**Decision Tree:**\n```
\nModel size >1GB? → EC2\nGPU required? → EC2\nConstant traffic? → EC2\nBudget <$50/mo? → Lambda\nUnpredictable spikes? → Lambda\n
```",
"thought": "User comparing infrastructure options for ML deployment. Critical factors: cost structure (fixed vs pay-per-use), model size constraints, GPU requirements, latency tolerance, traffic patterns. Should provide concrete decision criteria and mention SageMaker Serverless as middle ground. Real-world cost examples help contextualize."
}
Character counts:
- Output: 1,876 chars
- Thought: 261 chars
Cost Comparison
My Approach (Local)
- Hardware: $899 one-time (reusable forever)
- Electricity: $3.60 for 72 hours
- Per entry: $0.85 amortized (or $0.003 electricity only)
Commercial APIs (for 1,065 entries)
- GPT-4 API: ~$850 (800 tokens avg × $0.001/token × 1,065)
- Claude API: ~$1,200 (longer responses × higher price)
- OpenRouter DeepSeek: ~$150 (cheapest API option)
Savings: 99%+ (after hardware pays for itself in 2-3 runs)
Lessons Learned
1. Quality Over Quantity
A 50.9% rejection rate seems harsh, but it's a feature, not a bug.
The Critic agent catches:
- ❌ Hallucinated facts ("Python 4.0 was released...")
- ❌ Incomplete reasoning ('Just use library X" without explanation)
- ❌ Generic templates ("Here are 5 steps...")
- ❌ Logic errors (contradictory statements)
Only entries that pass rigorous validation make it through.
Result: Every entry is production-quality.
2. Hardware Performs Better Than Expected
I was skeptical about running two LLMs simultaneously on 32GB RAM. Turns out AMD's shared VRAM architecture works great:
- During generation: 24.2 GB RAM (87% utilization)
- CPU: 31.1% average (not maxed, room for optimization)
- Cycle time: ~6 minutes per entry (2 models sequentially)
- Stability: Zero crashes in 72 hours
For comparison, this would cost $40-100/month in cloud GPU time.
3. Automation Compounds
The system ran while I:
- Slept (8 hours × 3 nights = ~400 entries)
- Worked (8 hours × 3 days = ~400 entries)
- Coded other projects (8 hours × 3 days = ~265 entries)
Total: 1,065 entries with ~10 minutes of manual intervention (checking dashboard once/day).
That's the power of autonomous systems.
Free Datasets Released
I just uploaded 5 LITE versions (50% of data, 100% free) to HuggingFace:
🐍 Python Developer Dataset
173 entries covering:
- Docker/containerization
- Testing patterns
- API development
- Package management
- Best practices
🤖 AI/ML Engineering Dataset
143 entries covering:
- Model deployment (EC2, Lambda, SageMaker)
- MLOps patterns
- Training optimization
- Cloud ML services
- LLM fine-tuning
☁️ DevOps & Cloud Dataset
49 entries covering:
- CI/CD pipelines
- Infrastructure as Code
- Kubernetes/Docker
- Monitoring & logging
- AWS/GCP/Azure
🔌 Backend & API Dataset
63 entries covering:
- REST/GraphQL design
- Authentication patterns
- Database optimization
- Caching strategies
- Microservices
💾 Database & SQL Dataset
49 entries covering:
- Query optimization
- Schema design
- Transactions & ACID
- NoSQL patterns
- Performance tuning
Format: JSONL with instruction, input, output, thought fields
License: MIT (use however you want)
Ready for: HuggingFace Transformers, Axolotl, LLaMA Factory
What's Next?
V2 Features (in progress)
- Multilingual datasets (Spanish, French, German)
- Difficulty levels (Beginner/Intermediate/Advanced)
- Custom domains (on-demand generation for specific niches)
- Video tutorials (system walkthrough + setup guide)
Open-Source Plans
If there's enough interest, I'll release:
- Complete agent definitions
- Modelfile configurations
- ChromaDB setup scripts
- Dashboard code (Flask + real-time metrics)
- Consolidation scripts (JSONL → categorized products)
Update: Based on comments, I'll publish setup guide by end of week.
Try It Yourself
Quick Start
- Download a dataset:
wget https://huggingface.co/datasets/bernabepuente/python-instruction-dataset/resolve/main/dataset.jsonl
- Load in Python:
import json
with open('dataset.jsonl', 'r') as f:
data = [json.loads(line) for line in f]
print(f"Loaded {len(data)} entries")
print(f"Sample: {data[0]}")
- Fine-tune with Transformers:
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
# Load your base model
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
# Prepare dataset
def format_prompt(entry):
return f"Instruction: {entry['instruction']}\n\nResponse: {entry['output']}"
# Train!
# (full training code in dataset README)
Questions I'm Happy to Answer
Drop a comment if you want to know about:
- Optimizing Ollama for long runs
- CrewAI agent patterns for production
- ChromaDB deduplication strategies
- Dataset quality validation
- Hardware recommendations for local LLM workflows
- Monetization experiments (Scale AI, Gumroad, HF Premium)
Conclusion
Building autonomous systems that work while you sleep is incredibly satisfying.
This project taught me:
- Local LLMs can match commercial quality (with good prompts)
- Multi-agent validation catches errors humans would miss
- Hardware investment pays for itself quickly
- Automation compounds exponentially
Total time invested: ~20 hours (setup + monitoring)
Total output: 1,065 professional datasets
Time per entry: 1.1 minutes of human time
Try the datasets and let me know what you think!
Links:
- 🎁 Free Datasets: https://huggingface.co/bernabepuente
- 📧 Contact: (tu email o Twitter)
- 💬 Discuss: Comments below!
Tech Stack:
Ollama • CrewAI • ChromaDB • Flask • Python 3.11 • AMD Ryzen AI 9
License: MIT (all datasets)
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