Hey everyone — I’ve been building a desktop toolchain to make fine-tuning + deploying local LLMs feel more like a normal app workflow, and I wanted to share it.

What I made

NTTuner (fine-tuning + deployment GUI)

A desktop GUI app that covers the full fine-tuning workflow end-to-end:

• LoRA fine-tuning (GPU via Unsloth, with CPU fallback)
• Automatic GGUF conversion
• Direct import into Ollama
• Real-time training logs (non-blocking UI)
• Reproducible config saving

NTCompanion (dataset builder)

A dataset creation tool designed for quickly turning websites into usable training data:

• Universal web scraper for dataset generation
• Smart extraction to pull actual content (not menus / boilerplate)
• 6-factor quality scoring to filter junk
• Outputs directly in the format NTTuner expects
• GitHub repository cloning and processing

Why I built it

I got tired of the same loop every time I wanted to fine-tune something locally:

• bounce between CLI tools + Python scripts
• manually clean datasets
• manually convert to GGUF
• manually import into Ollama

I wanted a workflow where I could just:
build dataset → drag & drop → fine-tune → model shows up in Ollama.

Key features

NTTuner

• Drag-and-drop JSONL dataset support
• Auto-detects GPU and installs the correct dependencies
• Training runs in the background without freezing the UI
• Saves training configs as JSON for reproducibility
• One-click export to Ollama (with quantization)

NTCompanion

• Multi-threaded crawling (1–50 workers configurable)
• Filters out junk like navigation menus, cookie banners, etc.
• Presets for common content types (recipes, tutorials, docs, blogs)
• Supports major chat templates (Llama, Qwen, Phi, Mistral, Gemma)

Technical notes

• GUI built with DearPyGUI (responsive + GPU accelerated)
• Training via Unsloth for 2–5x speedups on compatible GPUs
• Graceful CPU fallback when GPU isn’t available
• Scraping/parsing with BeautifulSoup
• Optional Bloom filter for large crawls

Requirements

• Python 3.10+
• 8GB RAM minimum (16GB recommended)
• NVIDIA GPU w/ 8GB+ VRAM recommended (CPU works too)
• Windows / Linux / macOS

Example workflow

1. Scrape ~1000 cooking recipes using NTCompanion
2. Quality filter removes junk → outputs clean JSONL
3. Drag JSONL into NTTuner
4. Choose a base model (ex: Llama-3.2-3B-Instruct)
5. Start training
6. Finished model automatically appears in Ollama
7. Run: ollama run my-cooking-assistant

Links

• NTTuner: https://github.com/noosed/NTTuner
• NTCompanion: https://github.com/noosed/NTCompanion

Current limitations

• JavaScript-heavy sites aren’t perfect yet (no headless browser support)
• GGUF conversion has some manual steps in CPU-only training cases
• Quality scoring works best on English content right now

What’s next

I’m currently working on:

• Better JS rendering support
• Multi-language dataset support
• Fine-tuning presets for common use cases
• More export targets / model formats

If anyone tries it, I’d love feedback — especially on what would make this more useful in your fine-tuning workflow.

TL;DR: Built a desktop GUI that makes local LoRA fine-tuning + deployment mostly drag-and-drop, plus a dataset scraper tool that outputs training-ready JSONL.