eulerforge 0.1.0

Unified MoE+LoRA experimentation kit

EulerForge

🇰🇷 한국어: README.ko.md

EulerForge is a unified fine-tuning toolkit for HuggingFace-compatible LLMs. It brings together LoRA, Mixture-of-LoRAs, MoE-expert LoRA, and native-MoE fine-tuning under a single YAML-driven CLI, with built-in support for SFT, DPO, ORPO, RM, and PPO.

One preset, one command — from a base model to a deployable checkpoint.

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Features

• Five training paths in one pipeline: SFT → DPO / ORPO → RM → PPO
• Four injection strategies: dense_lora, mixture_lora, moe_expert_lora, native_moe_expert_lora
• Broad backbone support: Qwen2/3, Llama 2/3, Gemma 3, Gemma 4 (dense + MoE), Mixtral
• 4-bit / 8-bit quantized training (nf4 / int4 / int8) via bitsandbytes
• Pipeline-friendly: checkpoints from one stage flow into the next (SFT → DPO auto-detects base model and LoRA config)
• Phase scheduling: progressively unfreeze router → LoRA → base FFN
• Preflight + MoE stability validation: catch config errors before a single GPU cycle
• Integrated benchmarking with Ollama / OpenAI / local HF judge models
• Hyperparameter search (grid / random / bayes) via Optuna
• HF export: produce a standard HuggingFace directory that can be loaded with from_pretrained()
• Internationalized CLI: --lang ko/en/zh/ja/es

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Installation

pip install -e .

Requirements: Python ≥ 3.9, PyTorch ≥ 2.1, Transformers ≥ 5.5.

Optional extras:

pip install -e .[hpo]  # Optuna for grid/bayesian search
pip install -e .[tb]   # TensorBoard logging

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Quickstart

1. Train

# Simplest: dense LoRA SFT on Qwen3.5-0.8B with raw JSONL data
eulerforge train \
    --preset configs/presets/qwen3.5_0.8b_dense_lora_sft.yml \
    --set data.format=raw \
    --set data.task=sft \
    --set data.path=data/sft_10k_raw.jsonl \
    --set data.max_length=512

2. Evaluate

# Benchmark against a baseline + judge model
eulerforge bench --preset configs/bench/sft_with_judge.yml \
    --target-output-dir outputs/run_YYYYMMDD_HHMMSS

3. Export

# Merge LoRA into a standard HF directory
eulerforge export-hf \
    --checkpoint outputs/run_YYYYMMDD_HHMMSS \
    --output ./exported

4. Load in Python

from eulerforge import load_model

result = load_model("outputs/run_YYYYMMDD_HHMMSS")
# result.model, result.tokenizer, result.metadata

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CLI Commands

Command	What it does
eulerforge train	Fine-tune with LoRA / MoE strategies + phase scheduling
eulerforge convert	Convert arbitrary JSONL → EulerForge raw JSONL (map / recipe)
eulerforge preprocess	Tokenize raw JSONL → processed JSONL (cached)
eulerforge bench	Run target / baseline / judge inference benchmark
eulerforge export-hf	Export trained checkpoint as a standard HF model directory
eulerforge grid	Hyperparameter search (grid / random / bayesian)

Global options apply to all commands:

eulerforge --lang en train --preset ...   # English log output
eulerforge --lang ko train --preset ...   # Korean (default)

📖 Full CLI reference with every flag, YAML spec, training types, phase scheduling, load precision, and output structure: docs/cli_en.md

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Included Presets

A curated set of ready-to-run training presets lives under configs/presets/:

Category	Example preset	Strategy	Training
Qwen 3.5 small	qwen3.5_0.8b_dense_lora_sft.yml	Dense LoRA	SFT
Qwen 3.5 small	qwen3.5_0.8b_mixture_lora_sft.yml	Mixture-of-LoRAs	SFT
Qwen 3.5 small	qwen3.5_0.8b_moe_expert_lora_sft.yml	MoE Expert LoRA	SFT
Qwen 3.5 small	qwen3.5_0.8b_moe_expert_lora_dpo.yml	MoE Expert LoRA	DPO
Qwen 3.5 small	qwen3.5_0.8b_dense_lora_orpo.yml	Dense LoRA	ORPO
Qwen 3.5 small	qwen3.5_0.8b_dense_lora_rm.yml	Dense LoRA	RM
Qwen 3.5 small	qwen3.5_0.8b_dense_lora_ppo.yml	Dense LoRA	PPO
Qwen 3.5	qwen3.5_4b_dense_lora_sft.yml	Dense LoRA	SFT
Llama 3.2	llama3_1b_dense_lora_sft.yml	Dense LoRA	SFT
Llama 3.2	llama3_1b_moe_expert_lora_sft_handoff.yml	MoE Expert LoRA + Handoff	SFT
Gemma 3	gemma3_1b_mixture_lora_dpo.yml	Mixture-of-LoRAs	DPO
Gemma 3	gemma3_4b_moe_expert_lora_orpo_handoff.yml	MoE Expert LoRA + Handoff	ORPO
Gemma 4 (dense)	gemma4_e2b_dense_lora_sft.yml, gemma4_e4b_*.yml	All strategies	SFT / DPO
Gemma 4 (MoE)	gemma4_26b_a4b_native_expert_lora_sft.yml	Native MoE Expert LoRA	SFT
Mixtral	mixtral_native_expert_lora_sft.yml	Native MoE Expert LoRA	SFT
TinyLlama	tinyllama_1.1b_dense_lora_dpo.yml	Dense LoRA	DPO

Domain-specific preset groups:

• configs/presets/math/ — math SFT + DPO pipeline for Llama 3.2-1B
• configs/presets/reasoning/ — 2-stage Mixture-of-LoRAs for CoT reasoning

Benchmark presets live under configs/bench/.

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Tutorials

EulerForge ships with a numbered tutorial series, available in both English and Korean.

Browse: docs/tutorials/en/ — docs/tutorials/ko/

Recommended reading order:

1. Getting Started — install, strategy selection, CLI quickstart
2. 00. Data Preprocessing
3. 01. Dense LoRA
4. 02. Mixture-of-LoRAs
5. 03. MoE Expert LoRA
6. 04. Native MoE Expert LoRA
7. 05. DPO Training
8. 06. ORPO Training
9. 07. Reward Model Training
10. 08. PPO (RLHF) Training
11. 09. MoE Stability & Validation
12. 10. Metrics Monitoring
13. 11. Inference Benchmark
14. 12. Grid / Random / Bayesian Search
15. 13. LLaMA Fine-Tuning
16. 14. LoRA Handoff
17. 15. Loading Models
18. 16. HuggingFace Export
19. 17. Scratch Pretraining
20. 18. Training Pipeline (SFT → PPO)
21. 19. Data Collection for Labs
22. 20. Lab: Math SFT + DPO Pipeline
23. 21. Lab: Chain-of-Thought Reasoning Model
24. 22. Lab: Korean Finance Copilot
25. 23. Lab: Full MoE Pipeline (SFT → DPO → RM → PPO)

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Architecture at a Glance

            YAML preset
                │
                ▼
   ┌──────────────────────────────┐
   │   Config resolve + validate   │  ← preflight + MoE stability checks
   └──────────┬───────────────────┘
              ▼
   ┌──────────────────────────────┐
   │   Base model load (bnb 4/8)  │  ← HuggingFace AutoModel
   └──────────┬───────────────────┘
              ▼
   ┌──────────────────────────────┐
   │   Injection (WHERE / WHAT)   │  ← dense_lora / mixture_lora / MoE expert LoRA
   └──────────┬───────────────────┘
              ▼
   ┌──────────────────────────────┐
   │   Phase scheduler (WHEN)     │  ← router → LoRA → base FFN
   └──────────┬───────────────────┘
              ▼
   ┌──────────────────────────────┐
   │   Training loop              │  ← AdamW + cosine LR + AMP + aux loss
   │    SFT / DPO / ORPO / RM / PPO│
   └──────────┬───────────────────┘
              ▼
   ┌──────────────────────────────┐
   │   Checkpoint + HF export     │  ← final / best / latest
   └──────────────────────────────┘

Key design points:

• Backbone adapters locate layers, FFN and attention modules for each model family, so the same injection / training code works across Qwen, Llama, Gemma, Mixtral, …
• Phase scheduling lets you stage who is trainable over time (router warmup → LoRA → full FFN), making large-model fine-tuning stable and reproducible.
• Pipeline checkpoints automatically detect a prior EulerForge run and re-use its base model + LoRA config, so SFT → DPO → RM → PPO is one sequence of commands.

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Testing

# Run the public test suite (no GPU required)
pytest tests/ -x

This covers unit tests, CLI surface tests, validators, backbone adapters, loss functions, schedulers, i18n, and the plugin system. Test fixtures and synthetic data are included.

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Contributing

1. Run the test suite: pytest tests/ -x.
2. Open a PR with a clear summary of what changed and why.
3. Please keep CLI output i18n-clean (no hard-coded user-facing strings).

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License

Apache License 2.0. See LICENSE.

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Contact

EulerWa Inc. 🌐 eulerwa.com 📧 [e-mail]