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license: mit
tags:
  - effect
  - typescript
  - reinforcement-learning
  - grpo
  - fine-tuning
  - qwen
  - unsloth
  - lora

Qwen3-4B — Effect TypeScript Code Generation

Fine-tuned Qwen3-4B model specialized in generating high-quality Effect-style TypeScript code using Reinforcement Learning (GRPO).

Model Details

Developed by: Kodep
Model type: Qwen3-4B with LoRA adapter (rank 64)
Language: TypeScript (Effect framework)
License: MIT
Base model: unsloth/Qwen3-4B
Repository: github.com/belarusian/training

What is this model?

This model generates Effect-style TypeScript code — the popular effect system for functional programming in TypeScript. It's been fine-tuned using GRPO (Group Relative Policy Optimization), a reinforcement learning algorithm that improves code quality through reward-based training.

The model handles:

Effect imports and core patterns (Effect.succeed, Effect.flatMap, etc.)
Effect Schema definitions (Schema, decodeSync, etc.)
Effect service patterns
Proper TypeScript exports and types

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Kodep/qwen3-4b-effect-codegen")
model = AutoModelForCausalLM.from_pretrained(
    "Kodep/qwen3-4b-effect-codegen",
    torch_dtype=torch.float16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are an expert TypeScript developer specializing in the Effect framework."},
    {"role": "user", "content": "Generate an Effect service pattern for a user repository"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=1024, temperature=0.7)
print(tokenizer.decode(output[0], skip_special_tokens=True))

With Unsloth (faster inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="Kodep/qwen3-4b-effect-codegen",
    max_seq_length=4096,
    load_in_4bit=True,
)

# Inference
messages = [
    {"role": "system", "content": "You are an expert TypeScript developer specializing in the Effect framework."},
    {"role": "user", "content": "Generate an Effect Effect pattern for a user repository"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training Details

Training Data

428 TypeScript code samples extracted from:
- effect-smol — 185 samples
- effect — 208 samples
- opencode — 28 samples
- effect-examples — 7 samples
Sources: Real Effect.js library code, OpenCode LLM integrations, and Effect examples

Training Procedure

Data extraction: TypeScript files scraped from Effect repositories, filtered for Effect-specific imports
SFT pre-training (optional): 2 epochs, learning rate 2e-4 — teaches code format
GRPO training: 1 epoch, learning rate 2e-6, batch size 1, gradient accumulation 4x
Reward functions:
- +1.0 Code has <CODE> tags
- +0.5 Has Effect imports
- +0.3 Uses Schema
- +0.2 Has exports
- -0.5 Response too short (<100 chars)

Hyperparameters

Parameter	Value
Base model	Qwen3-4B
LoRA rank	64
Max sequence	4096
SFT lr	2e-4
GRPO lr	2e-6
SFT epochs	2
GRPO epochs	1
Optimizer	adamw_8bit
Gradient accum.	4

Hardware

GPU: NVIDIA GeForce RTX 4090 (24GB VRAM)
CUDA: 13.0
PyTorch: 2.10.0+cu130
Unsloth: 2026.5.8
vLLM: Used for faster inference during GRPO

Risks and Limitations

Fine-tuned on a small dataset (428 samples) — may not cover all Effect patterns
May generate syntactically valid but logically incorrect code
Not suitable for production use without evaluation
Training focused on code format and import patterns, not correctness verification

Citation

@misc{qwen3-4b-effect-codegen,
  author = {Kodep},
  title = {Qwen3-4B Effect TypeScript Code Generation},
  year = {2026},
  url = {https://huggingface.co/Kodep/qwen3-4b-effect-codegen}
}

Kodep
/

qwen3-4b-effect-codegen