Agent skill
unsloth-orpo
Install this agent skill to your Project
npx add-skill https://github.com/cuba6112/skillfactory/tree/main/skills/unsloth-orpo
SKILL.md
Overview
Unsloth-orpo facilitates one-step preference alignment using Odds Ratio Preference Optimization (ORPO). Unlike DPO, which requires a separate reference model, ORPO incorporates a penalty for disfavored generations directly into the training process, making it more efficient and faster.
When to Use
- When aligning models with human preferences (Chosen vs. Rejected) in a single step.
- When seeking an efficient alternative to DPO or RLHF.
- When training on preference datasets like
ultrafeedback_binarized.
Decision Tree
- Do you have a reference model?
- No: Use ORPO for one-step alignment.
- Is the model diverging from language patterns?
- Yes: Monitor
nll_loss(the SFT component) and adjust learning rate.
- Yes: Monitor
- Is the preference gap widening?
- Monitor
rewards/marginsandlog_odds_ratioin logs.
- Monitor
Workflows
- One-Step Preference Alignment: Load a model in 4-bit, apply LoRA, and initialize
ORPOTrainerwith a low learning rate (approx. 8e-6). - Monitoring ORPO Metrics: Connect to Weights & Biases to track the
log_odds_ratio(preference for chosen) andnll_loss(language modeling stability). - Exporting ORPO Models: Merge trained LoRA weights into the 16-bit base and export as GGUF (e.g.,
q4_k_m) for deployment.
Non-Obvious Insights
- ORPO eliminates the need for a separate Reward Model and Reference Model by penalizing the log-odds ratio of rejected responses directly.
- While SFT can accidentally increase the probability of bad responses by simply learning language patterns, ORPO widens the gap between "good" and "bad" from the first step.
- Optimal learning rates for ORPO are typically much lower than SFT (e.g., 8e-6 vs 2e-4).
Evidence
- "Traditional approaches like DPO require two steps... With ORPO, we can do it all in one step, which is great and more efficient." Source
- "ORPOConfig(learning_rate = 8e-6, beta = 0.1, lr_scheduler_type = 'cosine_with_restarts')." Source
Scripts
scripts/unsloth-orpo_tool.py: Setup for ORPOTrainer and preference datasets.scripts/unsloth-orpo_tool.js: Metric tracker for odds ratio changes.
Dependencies
unslothtrl(ORPOTrainer)wandb(recommended for monitoring)
References
- references/README.md
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