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-Coder-30B-A3B-Instruct
model-index:
- name: qwen3-coder-30b-rails
results: []
qwen3-coder-30b-rails
A 31B parameter Mixture-of-Experts model fine-tuned for Ruby on Rails code generation. Trained on 111,000 samples extracted from our own internal Rails projects.
Built by Bytecode.
Model Details
| Property | Value |
|---|---|
| Base model | Qwen3-Coder-30B-A3B-Instruct |
| Architecture | Qwen3 MoE (31B total, 3B active) |
| Training method | QLoRA (rank 16) via Unsloth |
| Training data | 111K samples from internal Rails projects |
| Training cost | ~$32 (A100 80GB, ~26 hours) |
| Quantization | GGUF Q4_K_M (18.6 GB), Q5_K_M (21.7 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
It generates code that follows these patterns without prompt engineering — the conventions are baked into the weights.
Usage with Ollama
# Download and run
ollama run bytecodehr/qwen3-coder-30b-rails
# Example prompt
ollama run bytecodehr/qwen3-coder-30b-rails "Write a Rails controller for managing user subscriptions with state transitions"
Memory requirements
| Format | GGUF Size | Min RAM | Recommended |
|---|---|---|---|
| Q5_K_M | 21.7 GB | 24 GB | 32 GB |
| Q4_K_M | 18.6 GB | 20 GB | 24 GB |
Rule of thumb: GGUF file size + 2–4 GB for KV cache and overhead.
Training
Trained with LoRA (rank 16, alpha 16) on attention projection layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj). Only 0.78% of parameters were trained.
The dataset pipeline:
- Extracted code from our internal Rails projects
- 15-step cleaning and deduplication pipeline
- 111K final training samples
- Includes 29 contrastive pairs (wrong way vs right way)
- 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. That means more code fits in the context window, generations are faster, and costs are lower. Read our analysis: Why Ruby Is the Better Language for LLM-Powered Development.
Other models
- bytecodehr/qwen3-8b-rails — 8B dense model, runs on laptops (5 GB)
- bytecodehr/qwen2.5-coder-7b-rails — 7B LoRA adapter
- bytecodehr/qwen2.5-coder-3b-rails — 3B LoRA adapter