Bidirectional English to Korean translation CLI

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What’s inside?

• Automatic language detection - Intelligently detects input language and translates accordingly
• High-quality translation - CHrF++ 32.96 / BLEU 12.05 on Flores-200 benchmark
• Efficient inference - Runs on modest hardware with merged adapters for speed
• Easy-to-use CLI - Simple command-line interface powered by Fire

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Quick Start

1. Clone the repository

git clone https://github.com/Liquid4All/cookbook.git
cd cookbook/examples/lfm2-english-to-korean

2. Install dependencies

uv sync

3. Run translation example

uv run python main.py --text "$(cat linkedin_post.txt)" --max-new-tokens 1024

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Understanding the Architecture

1. Supervised Fine-tuning (SFT): 100K high-quality Korean-English parallel datasets establish the translation foundation
2. Reinforcement Learning (RL): GRPO optimization with 10K additional samples refines translation quality

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Model Components

• Base Model: gyung/lfm2-1.2b-koen-mt-v4-100k - SFT fine-tuned LFM2 1.2B
• Adapter: gyung/lfm2-1.2b-koen-mt-v5-rl-10k-adapter - LoRA adapter trained with GRPO
• Automatic Detection: Regular expression pattern matching for Korean text (Hangul syllables, Jamo)

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CLI Usage

uv run python main.py [OPTIONS]

Options:
  --text TEXT                    Text to translate (required)
  --model-name TEXT             Base model name (default: gyung/lfm2-1.2b-koen-mt-v4-100k)
  --adapter-name TEXT           Adapter name (default: gyung/lfm2-1.2b-koen-mt-v5-rl-10k-adapter)
  --max-new-tokens INTEGER     Maximum tokens to generate (default: 256)
  --temperature FLOAT          Sampling temperature (default: 0.3)
  --min-p FLOAT               Minimum probability threshold (default: 0.15)
  --repetition-penalty FLOAT   Repetition penalty (default: 1.05)

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Example Usage

uv run python main.py --text "Hello, how are you today?"

uv run python main.py --text "안녕하세요, 오늘 어떻게 지내세요?"

uv run python main.py --text "$(cat your_file.txt)" --max-new-tokens 1024

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Performance Benchmarks

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Further Improvements

• Speed optimization with quantization techniques (GGUF, AWQ, GPTQ)
• llama.cpp integration for faster CPU inference
• Full parameter RL training with expanded compute resources
• Length normalization removal based on recent Qwen team findings
• Extended dataset training with 200K SFT + 25K RL samples

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Performance Optimization

• Quantized model variants for resource-constrained environments
• Streaming inference for real-time translation
• Batch processing for large document translation

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Need help?