Overview
daVinci-LLM-3B is a 3B-parameter base language model presented in daVinci-LLM: Towards the Science of Pretraining. This project aims to make the pretraining process a transparent and reproducible scientific endeavor.
We release not only the final weights but also training trajectories, intermediate checkpoints, data processing decisions, and 200+ ablation studies covering data quality, mixture design, training dynamics, and evaluation validity.
- GitHub: GAIR-NLP/daVinci-LLM
- Paper: arXiv:2603.27164
- Dataset: davinci-llm-data
The model follows a two-stage curriculum over ~8T tokens:
- Stage 1 (6T tokens): broad pretraining over diverse web-scale corpora.
- Stage 2 (2T tokens): structured QA and reasoning-heavy data to amplify math and code reasoning.
Key Features
- Fully transparent pretraining pipeline: data processing logic, mixtures, logs, and checkpoints are publicly documented.
- Data Darwinism framework: a systematic L0–L9 taxonomy for data processing depth.
- Large-scale ablations: 200+ controlled experiments with both positive and negative results.
Intended Use
- Research: pretraining science, data quality studies, training dynamics, evaluation stability.
- General capabilities: broad language understanding, math/science reasoning, and code generation.
This is a base model and is not instruction- or safety-aligned. Additional safety evaluation and alignment are required for production deployment.
Architecture
- Type: Decoder-only Transformer (Qwen2 family)
- Parameters: ~3.09B
- Layers: 36
- Hidden size: 2048
- Attention heads: 16 (GQA, KV heads = 2)
- MLP: SwiGLU, intermediate size 11008
- Position encoding: RoPE (base = 10000)
- Context length: 4096
- Tokenizer: Qwen2 tokenizer (151,936 vocab)
Data and Processing
The training corpus spans general web text, code, science, and QA sources. Each dataset is annotated with a Data Darwinism level (L0–L9), and multiple sources receive L4/L5 generative refinement and cognitive completion.
Major categories:
- General: Common Crawl–based corpora (L3).
- Code: GitHub crawls + Nemotron code datasets (L3/L5).
- Science/Math: MegaMath, Nemotron-CC-Math, and Darwin-Science series (L3–L5).
- QA: multi-source QA data with rejection sampling (L5).
Evaluation
The model reaches an overall average score of 51.72 across 19 benchmarks, matching or exceeding the performance of larger 7B-scale models like OLMo-3 7B.
| Capability Dimension | daVinci-3B | OLMo-3 7B | LLaMA-3.2-3B | Qwen-2.5-3B |
|---|---|---|---|---|
| Overall Performance | 51.72 | 51.65 | 37.58 | 51.44 |
| General Knowledge | 52.96 | 55.13 | 51.08 | 55.16 |
| Code Generation | 55.99 | 54.42 | 32.40 | 56.13 |
| Scientific Reasoning | 48.30 | 45.98 | 22.45 | 44.65 |
| MATH | 62.80 | 39.60 | 9.00 | 37.20 |
Citation
@misc{qin2026davincillmtowardssciencepretraining,
title={daVinci-LLM:Towards the Science of Pretraining},
author={Yiwei Qin and Yixiu Liu and Tiantian Mi and Muhang Xie and Zhen Huang and Weiye Si and Pengrui Lu and Siyuan Feng and Xia Wu and Liming Liu and Ye Luo and Jinlong Hou and Qipeng Guo and Yu Qiao and Pengfei Liu},
year={2026},
eprint={2603.27164},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2603.27164},
}