Applied AI Research — 2026
Run AI
Locally.
Local LLMs. Edge inference. Fine-tuning pipelines.
Real infrastructure — no cloud, no API keys, full control.
// Live node
Local Inference Console
// Setup
Run a Model in 3 Steps
STEP 01
Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
STEP 02
Pull a Model
ollama pull llama3
STEP 03
Run Inference
ollama run llama3
// Fine-tuning
Model Training Pipeline
⚡
LoRA Fine-Tuning
Low-Rank Adaptation enables efficient fine-tuning without retraining the full model. Minimal VRAM, maximal results.
pip install peft transformers accelerate
🗂
Dataset Preparation
Prepare structured JSONL datasets for supervised fine-tuning with instruction-output pairs.
{"instruction":"Explain AI",
"output":"AI simulates..."}
🔬
Training Script
Minimal training loop using HuggingFace Transformers with full evaluation logging.
from transformers import Trainer
trainer.train()
📊
Experiments
Supervised Fine-Tuning, quantization-aware training, benchmark comparisons, memory and throughput profiling.
// Performance
Benchmark Comparisons
Latency (ms) — lower is better
Model Size (GB) — lower is smaller
// HuggingFace
Top 5 Open Source LLMs
HuggingFace · Open Source
Download Models
Select a model and quantization level. Copy the Ollama command or open the HuggingFace page directly.
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