@j_golebiowski: How long it takes to build a task-specific local model, start to finish: - 25 hand-written examples (one afternoon) - e…
Summary
Describes a workflow to build a task-specific local model in under a day using 25 hand-written examples, synthetic data expansion, LoRA fine-tuning, and quantization for CPU inference.
View Cached Full Text
Cached at: 07/16/26, 04:05 AM
How long it takes to build a task-specific local model, start to finish:
- 25 hand-written examples (one afternoon)
- expand to ~10k synthetic with a teacher model
- LoRA fine-tune, 4 epochs
- quantize to run on CPU
Total: under a day.
Similar Articles
@neural_avb: Watch this 45 min video to learn how to create synthetic datasets and train tiny (100M params) local language models th…
A 45-minute video tutorial on creating synthetic datasets and training tiny (100M parameter) local language models for narrow tasks, with code and resources provided.
How I'm using local models from real-world coding
The author shares their experience using local AI models for real-world coding tasks.
The power of structured workflows and small local models
The author details their experience building a custom agent loop using a small local model (Qwen3.5 9B) with structured workflows and a map-reduce pattern to manage context limits, replacing Claude Code for most tasks.
what non-coding tasks have you gotten a local model to do autonomously?
The author discusses building a small VLM for desktop GUI automation to move data between apps without APIs, expressing interest in non-coding autonomous use cases for local models.
Getting real work out of a 4B local model: the distill-on-idle pipeline behind an on-device "memory" assistant
Describes a 'distill-on-idle' pipeline that enables a 4B parameter local model to run effectively as an on-device memory assistant, demonstrating practical use of small models.