qwen3-8b-rails

An 8B parameter dense model fine-tuned for Ruby on Rails code generation. Trained on 111,000 samples extracted from our own internal Rails projects. Small enough to run on a laptop.

Built by Bytecode.

Model Details

Property	Value
Base model	Qwen3-8B
Architecture	Qwen3 dense (8B parameters)
Training method	QLoRA (rank 16) via Unsloth
Training data	111K samples from internal Rails projects
Training cost	~$21 (A100 80GB, ~17 hours)
Quantization	GGUF Q4_K_M (5.03 GB)

What it does

This model writes idiomatic Ruby on Rails code following specific conventions:

• Devise authentication
• Namespaced concerns instead of service objects
• Sidekiq instead of Solid Queue
• State-as-records instead of boolean flags
• DaisyUI drawer layouts instead of ActiveAdmin

The 8B model is the lightweight option — fast enough for inline code completion, small enough to run alongside your development server without swapping.

Usage with Ollama

# Download and run
ollama run bytecodehr/qwen3-8b-rails

# Example prompt
ollama run bytecodehr/qwen3-8b-rails "Write a Rails migration for a subscriptions table with plan, status, and billing cycle"

Memory requirements

Format	GGUF Size	Min RAM	Recommended
Q4_K_M	5.03 GB	8 GB	16 GB

Fits comfortably on any modern laptop. GGUF file size + 2–3 GB for KV cache.

Training

Trained with LoRA (rank 16, alpha 16) on attention projection layers. Only 0.78% of parameters were trained. The full training run took ~17 hours on a single A100 80GB GPU.

The dataset:

1. Our internal Rails projects
2. 15-step cleaning and deduplication pipeline
3. 111K final training samples with contrastive pairs
4. Source diversity cap at 20% per repository

Full details in our blog posts:

• Part 1: Dataset Engineering
• Part 2: Training, Quantization, and Deployment

Why Ruby for LLMs?

Ruby uses 42–45% fewer tokens than TypeScript across every major LLM tokenizer. Fewer tokens means more code in the context window, faster generations, and lower costs. Read our analysis: Why Ruby Is the Better Language for LLM-Powered Development.

Other models

• bytecodehr/qwen3-coder-30b-rails — 31B MoE flagship model (18–21 GB)
• bytecodehr/qwen2.5-coder-7b-rails — 7B LoRA adapter
• bytecodehr/qwen2.5-coder-3b-rails — 3B LoRA adapter