Reference-free preference alignment, simpler than DPO.
Skill metadata
| Source | Optional — install with hermes skills install official/mlops/simpo |
| Path | optional-skills/mlops/simpo |
| Version | 1.0.0 |
| Author | Orchestra Research |
| License | MIT |
| Dependencies | torch, transformers, datasets, trl, accelerate |
| Platforms | linux, macos, windows |
| Tags | Post-Training, SimPO, Preference Optimization, Alignment, DPO Alternative, Reference-Free, LLM Alignment, Efficient Training |
Reference: full SKILL.md
The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
SimPO - Simple Preference Optimization
Quick start
SimPO is a reference-free preference optimization method that outperforms DPO without needing a reference model.
Installation:
# Create environment
conda create -n simpo python=3.10 && conda activate simpo
# Install PyTorch 2.2.2
# Visit: https://pytorch.org/get-started/locally/
# Install alignment-handbook
git clone https://github.com/huggingface/alignment-handbook.git
cd alignment-handbook
python -m pip install .
# Install Flash Attention 2
python -m pip install flash-attn --no-build-isolation
Training (Mistral 7B):
ACCELERATE_LOG_LEVEL=info accelerate launch \
--config_file accelerate_configs/deepspeed_zero3.yaml \
scripts/run_simpo.py \
training_configs/mistral-7b-base-simpo.yaml
Common workflows
Workflow 1: Train from base model (Mistral 7B)
Config (mistral-7b-base-simpo.yaml):
# Model
model_name_or_path: mistralai/Mistral-7B-v0.1
torch_dtype: bfloat16
# Dataset
dataset_mixer:
HuggingFaceH4/ultrafeedback_binarized: 1.0
dataset_splits:
- train_prefs
- test_prefs
# SimPO hyperparameters
beta: 2.0 # Reward scaling (2.0-10.0)
gamma_beta_ratio: 0.5 # Target margin (0-1)
loss_type: sigmoid # sigmoid or hinge
sft_weight: 0.0 # Optional SFT regularization
# Training
learning_rate: 5e-7 # Critical: 3e-7 to 1e-6
num_train_epochs: 1
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
# Output
output_dir: ./outputs/mistral-7b-simpo
Launch training:
accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml \
scripts/run_simpo.py training_configs/mistral-7b-base-simpo.yaml
Workflow 2: Fine-tune instruct model (Llama 3 8B)
Config (llama3-8b-instruct-simpo.yaml):
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
dataset_mixer:
argilla/ultrafeedback-binarized-preferences-cleaned: 1.0
beta: 2.5
gamma_beta_ratio: 0.5
learning_rate: 5e-7
sft_weight: 0.1 # Add SFT loss to preserve capabilities
num_train_epochs: 1
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
output_dir: ./outputs/llama3-8b-simpo
Launch:
accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml \
scripts/run_simpo.py training_configs/llama3-8b-instruct-simpo.yaml
Workflow 3: Reasoning-intensive tasks (lower LR)
For math/code tasks:
model_name_or_path: deepseek-ai/deepseek-math-7b-base
dataset_mixer:
argilla/distilabel-math-preference-dpo: 1.0
beta: 5.0 # Higher for stronger signal
gamma_beta_ratio: 0.7 # Larger margin
learning_rate: 3e-7 # Lower LR for reasoning
sft_weight: 0.0
num_train_epochs: 1
per_device_train_batch_size: 1
gradient_accumulation_steps: 16
When to use vs alternatives
Use SimPO when:
- Want simpler training than DPO (no reference model)
- Have preference data (chosen/rejected pairs)
- Need better performance than DPO
- Limited compute resources
- Single-node training sufficient
Algorithm selection:
- SimPO: Simplest, best performance, no reference model
- DPO: Need reference model baseline, more conservative
- PPO: Maximum control, need reward model, complex setup
- GRPO: Memory-efficient RL, no critic
Use alternatives instead:
- OpenRLHF: Multi-node distributed training, PPO/GRPO
- TRL: Need multiple methods in one framework
- DPO: Established baseline comparison
Common issues
Issue: Loss divergence
Reduce learning rate:
learning_rate: 3e-7 # Reduce from 5e-7
Reduce beta:
beta: 1.0 # Reduce from 2.0
Issue: Model forgets capabilities
Add SFT regularization:
sft_weight: 0.1 # Add SFT loss component
Issue: Poor preference separation
Increase beta and margin:
beta: 5.0 # Increase from 2.0
gamma_beta_ratio: 0.8 # Increase from 0.5
Issue: OOM during training
Reduce batch size:
per_device_train_batch_size: 1
gradient_accumulation_steps: 16 # Maintain effective batch
Enable gradient checkpointing:
gradient_checkpointing: true
Advanced topics
Loss functions: See references/loss-functions.md for sigmoid vs hinge loss, mathematical formulations, and when to use each.
Hyperparameter tuning: See references/hyperparameters.md for beta, gamma, learning rate selection guide, and model-size-specific recommendations.
Dataset preparation: See references/datasets.md for preference data formats, quality filtering, and custom dataset creation.
Hardware requirements
- GPU: NVIDIA A100/H100 recommended
- VRAM:
- 7B model: 1× A100 40GB (DeepSpeed ZeRO-3)
- 8B model: 2× A100 40GB
- 70B model: 8× A100 80GB
- Single-node: DeepSpeed ZeRO-3 sufficient
- Mixed precision: BF16 recommended
Memory optimization:
- DeepSpeed ZeRO-3 (default config)
- Gradient checkpointing
- Flash Attention 2
Resources
- Paper: https://arxiv.org/abs/2405.14734 (NeurIPS 2024)
- GitHub: https://github.com/princeton-nlp/SimPO
- Models: https://huggingface.co/princeton-nlp
- Alignment Handbook: https://github.com/huggingface/alignment-handbook