language:
- en
license: apache-2.0
library_name: transformers
tags:
- ruby
- rails
- code-generation
- gguf
- fine-tuned
- lora
- unsloth
pipeline_tag: text-generation
base_model: Qwen/Qwen3-8B
model-index:
- name: qwen3-8b-rails
results: []
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:
- Our internal Rails projects
- 15-step cleaning and deduplication pipeline
- 111K final training samples with contrastive pairs
- Source diversity cap at 20% per repository
Full details in our blog posts:
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