Downloads · 30 days
7
6% of all-time downloads
AIPlans/Qwen3-0.6B-DPO
Qwen3-0.6B-DPO is a text generation model from AIPlans. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
<a href="https://aiplans.org" target="blank" style="margin: 2px;" <img alt="AIPlans" src="./logos/AI-Plans.svg" style="display: inline-block; vertical-align: middle;"/ </a
Downloads · 30 days
7
6% of all-time downloads
All-time downloads
117
Public
Repo size
20.6 MB
Likes
0
Public
Click a slice to open those files.
.json14.2 MB · 57%
From the Hugging Face model README
This model is a fine-tuned variant of Qwen/Qwen3-0.6B, trained using Direct Preference Optimization (DPO) on a preference-form version of the nvidia/HelpSteer2 dataset as part of the AIPlans Model Diffing Project.
This model is a 0.6B parameter language model based on Qwen3-0.6B and fine-tuned using DPO for preference optimization.
The goal of the fine-tuning was to improve helpfulness and harmlessness as measured by the HelpSteer2 preference dataset, while enabling controlled model diffing experiments within the AIPlans research workflow.
Special attention was paid to training efficiency, including gradient checkpointing and other memory-saving strategies.
Developed by: AIPlans
Funded by: AIPlans
Shared by: AIPlans
Model type: Causal decoder-only Transformer (LLM)
Languages: English
License: MIT
Fine-tuned from: Qwen/Qwen3-0.6B
Training Method: Direct Preference Optimization (DPO)
Intended Use: Research on model diffing, preference fine-tuning, evaluation of lightweight LLM behavior changes.
Dataset is taken from Jennny/helpsteer2-helpfulness-preference . Thanks Jennny
Below is a comparison between the base Qwen3-0.6B model and our DPO-trained version (trained using HelpSteer2 preference data).
The model was evaluated using lm-eval-harness on multiple reasoning and truthfulness benchmarks.
Below is a comparison between the Base Qwen3-0.6B model and This DPO-Trained Model.
| Task | Metric | Base Model | DPO Model | Change |
|---|---|---|---|---|
| ARC-Challenge | acc | 0.3148 | 0.3208 | +0.0060 |
| acc_norm | 0.3447 | 0.3430 | -0.0017 | |
| ARC-Easy | acc | 0.6044 | 0.6103 | +0.0059 |
| acc_norm | 0.5589 | 0.5589 | 0 | |
| HellaSwag | acc | 0.3751 | 0.3752 | +0.0001 |
| acc_norm | 0.4738 | 0.4740 | +0.0002 | |
| TruthfulQA (MC2) | acc | 0.4275 | 0.4305 | +0.0030 |
| Winogrande | acc | 0.5604 | 0.5620 | +0.0016 |
Jithesh Pavan D Souza – AIPlans Research Intern
Jithesh – [email protected]