Model Description

This model is an IPO (Identity Preference Optimization) fine-tune of Qwen/Qwen3-0.6B-Base.

It was aligned using the HelpSteer2 dataset to improve helpfulness and instruction following capabilities.

Unlike PPO or ReMax, IPO formulates the alignment problem as a regression task. It minimizes a regularized squared error loss on the preferences, providing a stable and theoretically grounded approach to alignment without needing a separate reward model or complex sampling loops during training.

The goal of the fine-tuning was to improve helpfulness/harmlessness behavior as measured by the HelpSteer2 dataset, while also enabling controlled model diffing experiments as part of the AIPlans research workflow.

Developed by: AIPlans
Funded by: AIPlans
Shared by: AIPlans
Model type: Causal decoder-only Transformer (LLM)
Languages: English
Intended Use: Research on model diffing, preference fine-tuning, evaluation of lightweight LLM behavior changes

📊 Evaluation

Below is a comparison between the base model and this IPO-trained version.

Task	Metric	Base Model	IPO Model	Change
arc_challenge	acc_norm	0.3848	0.3968	+0.0119
arc_easy	acc_norm	0.5783	0.6700	+0.0918
hellaswag	acc_norm	0.5379	0.5540	+0.0160
truthfulqa_mc2	acc	0.4586	0.4576	-0.0010
winogrande	acc	0.5896	0.5935	+0.0039

⚙️ Training Details

Method: IPO (Identity Preference Optimization)
Base Model: Qwen/Qwen3-0.6B-Base
SFT Model Used: AIPlans/Qwen3-0.6b-SFT-hs2
Precision: bfloat16 (Training), bfloat16 (Final Weights)
Learning Rate: 5e-7
Beta: 0.01
Epochs: 3
Batch Size: 8 (Global Effective: 16)
Hardware: NVIDIA A100 (80GB) (Took 1 hr 25 mins and acquired 78.4 GB VRAM)

Algorithm Highlights

Direct Optimization: Optimizes preferences directly without a reward model loop.
Stability: Uses a squared error loss which is bounded and often more stable than DPO's sigmoid loss.
Regularization: Uses a beta of 0.01 to balance preference satisfaction with reference model divergence.

💻 Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AIPlans/Qwen3-0.6B-IPO"

model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

prompt = "User: How do I make a cake?\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Card Author

Premanand Jena - AIPlans Research Intern, Contact : [email protected]

Citation

IPO Paper : https://arxiv.org/abs/2310.12036

TRL:

@misc{vonwerra2022trl,
    title        = {TRL: Transformer Reinforcement Learning},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}