Self-Exploring Language Models: Active Preference Elicitation for Online Alignment.

SELM-Phi-3-mini-4k-instruct-iter-3

This model is a fine-tuned version of ZhangShenao/SELM-Phi-3-mini-4k-instruct-iter-2 using synthetic data based on on the HuggingFaceH4/ultrafeedback_binarized dataset.

Model description

• Model type: A 3.8B parameter Phi3-instruct-based Self-Exploring Language Models (SELM).
• License: MIT

Results

	AlpacaEval 2.0 (LC WR)	MT-Bench (Average)
SELM-Phi-3-mini-4k-instruct-iter-3	27.98	8.32
SELM-Phi-3-mini-4k-instruct-iter-2	26.79	8.44
SELM-Phi-3-mini-4k-instruct-iter-1	27.33	8.37
Phi-3-mini-4k-instruct	23.05	8.12

Our model also ranks highly on WildBench! 🔥

Training hyperparameters

The following hyperparameters were used during training:

• alpha: 0.001
• beta: 0.01
• train_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• num_epochs: 1

Framework versions

• Transformers 4.40.2
• Pytorch 2.1.2+cu121
• Datasets 2.14.6
• Tokenizers 0.19.1