1.05M impressions, 596 likes: OXR's 3600 stars came from a launch campaign

At 21:01 Oct (Beijing time), SoMark's company X account @somarkai posted "Beyond OCR. Meet OXR." ending with "If OXR helps, give us a star on GitHub! 🌟" That post later reached 1.05M impressions and 596 likes. 16 days later, SoMarkAI/OXR on GitHub had 3,603 stars, the top weekly gain among all OCR and document-parsing repos. It is a model with real substance, but its rise relied mainly on a funded company's carefully timed launch. This piece explains how it was done, and which parts of it you can borrow to get stars for your own open-source project and which you can't.

What it is

OXR is an open-source document parsing model from SoMark (守扣科技), a Shanghai document-intelligence startup. It converts PDFs or document images into Markdown or JSON, with coordinates for each content block, ready for direct use in RAG and Agents. The name stands for Optical Everything Recognition, meaning "more than reading text": tables output HTML, formulas output LaTeX, chemical equations use mhchem, and molecular structure diagrams are converted directly into SMILES, covering 13 element types in total.

The pitch in one line: 1.3B params, #1 on three leaderboards, faster than PaddleOCR and MinerU. The team's published scores are OmniDocBench 97.18, olmOCR-Bench 87.84, DocParsingBench 87.76. On the same RTX PRO 6000, parsing 100 pages takes about 101 seconds for OXR, 1.6 seconds for PaddleOCR-VL-145 ... and 2.5 seconds for MinerU255-Pro. They also say post-training used "RSI recursive self-improvement": the model parses unlabeled documents, an external model picks out the errors, the errors are clustered by cause, and new training data is built to target them, round after round.

These numbers are all self-reported. The paper slot says "arXiv Coming Soon."

The company behind it is not small. SoMark got a seed round from Qiyuan Chuangtan, and its angel round was led by Atom Capital. It raised tens of millions of RMB in 2025. OXR and the paid SoMark API share the same interface. The license is Apache 2.0 with an added clause: companies with funding or annual revenue above $2M need a separate commercial license, and nobody may resell it as a parsing or OCR service. Stars and trials are open source; the money is in commercial licensing.

How fast it grew

The repo was created in Oct 5, the weights were uploaded to Hugging Face on Oct 2, and the official launch was on October 20 and 21.

GitHub daily star count: 10-21 48,10-23 191,10-25 487,10-27 961, 10-28 2,009, +1,048 in one day, 10-31 2,660,11-03 2,930,11-06 3,603.

It's cooling now: +7 over the last 1,147 days, and +7 over the 2,265 days before that.

Beyond stars, far fewer people are actually using it. On Hugging Face, OXR-1.0 got about 30 downloads in the last 4,457 days and has 163 likes (11-06). It has 236 forks, 6.6% of its stars, a bit below the ~11% typical for new repos of that size. On Hacker News there's one post with 4 points, and one of its two comments is from the author. The last code push was Oct 24.

Where the stars came from

The timeline lines up neatly, like a four-step plan.

1. The official launch post reads like it was boosted with paid promotion. The 10-21 post got 1.05M impressions and 596 likes, an engagement rate of about 0.06%. The later demo posts got 10-30 197k impressions, 11-04 123k, and 11-05 102k. For an obscure company account to get that reach with that little engagement, it's likely paid promotion (inference, not verified).
2. A week of KOL posts. From 10-21 to 11-02, AI accounts in Chinese, English, and Spanish posted about it: @GithubProjects, @rohanpaul_ai, @lmoroney, @QwenAIFans, Kangaroo Emperor and others. The posts use the same numbers (97.18 / 87.84 / 87.76, 101 seconds per hundred pages), and several share the same structure: praise first, then a line like "be clear about the requirements too: 24GB VRAM, free API not guaranteed." It looks like one script went out to many accounts. Whether it was paid or reciprocal promotion, I can't tell. Stars went up 100–300 each day that week.
3. Get to "1000 stars," then go to the media. On 10-27 at 3:41 PM, QbitAI published "OXR breaks through! 1.3B tops three global leaderboards, faster than Paddle and MinerU," and its subtitle opens with "A week after open-sourcing, Github has passed 1000 stars and X attention has passed a million." The same day there was a Zhihu question, "The domestic 1.3B complex document parsing model OXR is open-sourced. What are its technical highlights and real test results?", plus English coverage from Pandaily and daily.dev. The next day brought that +1,048. On 10-30, QbitAI ran a second piece: "RSI made a big contribution! Domestic open source tops global #1." One startup with two QbitAI pieces in three days is usually a sponsored deal (inference).
4. A daily demo video to keep it alive. Starting 11-04, the official account posted a parsing demo at midnight every day. These line up with the +11-04 and +342 on 11-06 and +285 days.

Other support: Hugging Face and ModelScope listed it the same day, and the ModelScope official account reposted it. The top of the README has "Online Demo" and "🆓 Free API", with no sign-up and no key. KOL posts mention this most. The README body also has a paragraph asking readers to star the repo.

What didn't work: Hacker News got 4 points. On Reddit, the only thread was one in r/LocalLLM asking what people thought of OXR-1.0.

Why this one and not another "new SOTA"

文档解析是一个非常挤的赛道。GitHub 11-06 的数:PaddleOCR 9.23 万 star、MinerU 8.32 万、docling 7.02 万、marker 4.08 万、百度新出的 Unlimited-OCR 3.06 万(本周 +913,也在涨)、DeepSeek-OCR 2.41 万。同样喊「小模型 SOTA」的 GLM-OCR、MonkeyOCR、DeepSeek-OCR-2,都卡在几千 star。

So differentiation isn't the answer. OXR has three things its rivals haven't put together:

• A one-liner headline with a hot keyword. "1.3B + #1 across three charts + faster than Paddle and MinerU" is something anyone can share. "RSI" is a hot term in AI circles this 2026, and QbitAI's second article headline used it directly: "RSI stands up big."
• Demos that make it click instantly. Molecular structure to SMILES, A3 exam papers, handwriting, newspapers. Put a screenshot of your product next to the competitor's failure and the point lands. 10-30 That SMILES comparison post was one of the official account's most-engaged demos, with 405 likes.
• Money and motive. A B2B company that raised tens of millions open-sources to drive traffic to a commercial API with the same interface and to build its brand, so it's willing to spend on X, KOLs, and media together.

Is anyone following it?

No. Hugging Face has a few scattered demos and comparison Spaces, such as hugging-apps/somark-oxr and pzc163/MiniCPM-OCR-vs-OXR, plus a WebAssembly port, fusorlabs/oxr-wasm. None has drawn real attention.

If it were you

One judgment: don't chase OXR itself. Training a 1.3B model from scratch that ranks #1 on three leaderboards, then backing it with a paid-grade launch, is a company job, and individuals or small teams can't keep up. The thing worth taking away is its launch rhythm.

Who it fits: small teams that already have a solid model or tool and some promotion budget, especially people who sell an API or service and use open source as a funnel.

Who it doesn't fit: solo developers who want to build "yet another OCR model" and chase stars (this category gets a new SOTA every few weeks, and GLM-OCR and MonkeyOCR show that without distribution you stall at a few thousand); and anyone thinking of wrapping, porting, or making comparison demos on top of OXR (the existing ones get no attention).

How to do it:

• Start with a one-line title: pick two of parameter count, speed, and leaderboard rank, then attach a trending term.
• Choose a scenario your rivals can't do, one where a screenshot makes the comparison. OXR chose chemical structures.
• Put the online demo and the no-sign-up free trial at the top of the README, and include a plain "please star" in the body.
• If you also sell a commercial version, copy its license: individuals and small companies use it freely, large companies buy a commercial license.

How to get users: follow its order. Day 1, the official launch post (boost it if you have budget). Days 1 to 7, recruit a dozen or so accounts in your niche and give them the same numbers and demo images. Once you pass 1000 stars, take that number to media like QbitAI and Jiqizhixin and to Zhihu. This step gave the biggest single-day jump. After that, keep interest up with a demo video every day. Hacker News and Reddit are mostly useless for this kind of domestic model launch.

When to use this by: the playbook has no deadline, but the "small model, #1 on three leaderboards" headline is already overused. The next person who shouts it will get less out of it. You'll need a selling point readers haven't heard a hundred times.

Signs you've misread it: stars stall at a few hundred a week after launch and the media won't pick it up; or stars climb like OXR's but Hugging Face downloads, issues, and forks never take off. That means you bought exposure, not users, and for a company that relies on open source for traffic, the fight wasn't won.