Open-R1: a Completely Open Reproduction Of DeepSeek-R1

Hey there! This post is an intro to the job, not a claim that we’ve replicated R1 yet. We’re integrating in the open, so as quickly as we have assessment numbers, we’ll share them. You can follow our development on Hugging Face and GitHub.

True, but it appears like there’s nothing to be assessed as of right now. I presume the supreme goal is to train a new reasoning model and after that utilize the very same assessment metrics as o1 and the DeepSeek-R1.

Well, there must be at least some peace of mind check and validation to guarantee the model was trained correctly.

Oh yes, if you are speaking about the evaluation variety of deepseek’s design it’s coming soon!

As pointed out in the article there is no design called Open-R1 to check at all … not yet anyway. This is a blog site describing that Hugging face will take the R1 Deepseek design, exercise how it was developed as described in the paper and from what they released, and after that replicate that process.

in fact this is quite much how science works … A develops a plan, discovery or innovation and it is tested by B, C and D to see if it is reproduceable. Thats been the foundation of research now for a couple of centuries.

This blog site is not they have actually already done so … Its a blog outlining an intent to start training a model like R1 and calling it Open-R1.

Also DeepSeek-R1 was just launched last week, and even in their paper they detailed the compute hours needed. While those are low calculate hours for a SOTA model this does not mean you can train said model in a week. I ‘d personally love to be able to train a transformer design in a week, however we may require to wait a while for that level of calculate innovation.

So there are no standards for a design that has not been built yet right? As detailed in the blog, and again in reply to your concern.

However fear not, there is a GitHub Repo already and contributors (hell I may join myself), some prelim work done, and a strategy of attack. A good starting position.

n
@edbeeching
has actually evaluated the launched models currently

( src: https://x.com/edwardbeeching/status/1884273209136275742)

R1 just trained on o1 outputs, so jointly …/ s. This is what the new AI czars are stating

Hi! This post is an introduction to the task, not a claim that we’ve recreated R1 yet. We will totally share the missing out on piece when we have them, you can anticipate the designs and datasets to be upload in this Hugging Face org and the code to be in this GitHub repo

That’s great and essential to comprehend this significant hype that lacks technical comprehension and description. Science is about recreation, and if they declare to be open, let them fullfill the open part.

Please do release the training cost.

We will!

Excalidraw Hi n
@bojan2501
thanks, we will certainly be striving to make certain this training recipe can work for small language models on consumer hardware because not everyone has a cluster of H100s in your home:-RRB- The tool we used for the images was Excalidraw! https://excalidraw.com

looking forward to it! WTF are your discussing?

should be a joke

It’s truly cool to see how the entire open source community comes together!

Ops …

5.5 M is number reporter in the deepseekv3 tech report (just the training, not the experiment afaik), for R1 difficult to approximate tbh however much less than 5.5 M imo

Historically, they have never ever released code or datasets of their LLM training, so I would not expect this time to be different. If they would launch it that would be incredible obviously!

Yes naturally!

So basically you’re asking to change existing censorship with another flavour of censorship?

The code for the designs are inside the design repositories, e.g. for V3: https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/modeling_deepseek.py

Hello Team, I’m Ray Bernard, the author and developer of EQUATOR. My research team will be dealing with a paper concentrated on reproducing particular parts of DeepSeek R1. Our aim is to reproduce the cold start and offer your group with a dataset that includes COT and other techniques to support these efforts. We like to contribute our work to assist. Please let me understand if you find this helpful. Best, Ray Bernard https://www.facebook.com/groups/1186310571520299/

Where is the evaluation numbers? without it you can’t call it recreation.

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True, however it appears like there’s nothing to be assessed as of today. I assume the supreme goal is to train a brand-new reasoning model and then use the very same assessment metrics as o1 and the DeepSeek-R1.

That’s quite fascinating, I was asking myself why the concerns the author exposed here are not being asked by others? I think the work they have done is unforgettable however at the very same time I question why they would not put these missing pieces on if they are supposed to be fully open.
Why even without reproduction and understanding of the innovation they could impact so much the marketplace in this way?

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Hi! This blog post is an introduction to the job, not a claim that we have actually recreated R1 yet. We will totally share the missing piece when we have them, you can expect the models and datasets to be upload in this Hugging Face org and the code to be in this GitHub repo

Interesting read, and it is good that we see more effort into this direction: more optimization and less strength.
Also wonder what tool did the author use for creating step diagram.

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Excalidraw I’m so happy that initiative like this currently exist, I’m gon na attempt to contribute:-RRB- 1 reply

looking forward to it! So racist articel

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WTF are your talking about?

Awesome to have this open reproduction began!

For Step # 1 check out https://github.com/open-thoughts/open-thoughts!

https://x.com/ryanmart3n/status/1884284101265612856

Let’s do this thing!

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It’s actually cool to see how the entire open source community comes together!

Does anyone understand the actual training cost of r1? I can’t discover it in the paper or the statement post. Is the 6M expense reported by media just the number taken from v3‘s training cost?

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Ops …

Has anybody asked the DeepSeek team to publish their training data and code, or a minimum of share them privately with an independent duplication project like this? Have they rejected such a demand?

A faithful replication depends upon utilizing the very same dataset and hyperparameters. Otherwise, any major inconsistencies with the published benchmarks would be tough to pin down-whether due to training information distinctions or the replication technique itself.

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Historically, they have actually never launched code or datasets of their LLM training, so I would not expect this time to be various. If they would release it that would be amazing naturally!

In the meantime we have to make finest guess estimates and see if we can arrive ourselves.

You provide good replication procedure of Deepseek reasoning training. I will attempt something similar to it.

This is really great info, can we tweak with specific use case when code is launched?

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Yes obviously!

Please consider eliminating biased, tainted or unaligned training data and make an effort to get rid of copyrighted works from the crawl from consumption. This will make the model more functional. If you reused anthropic curation checks, this might also assist, eliminate obviouslybiased information will likely add a lot of value. We do not desire another tainted, unaligned open source model, right? And no corporate would ever use deepseek or a model that reuses it, right?
We appreciate your work for the advantage of mankind, we hope.
Miike C from NJ

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So basically you’re asking to change existing censorship with another flavour of censorship?

Can’t wait! Hopefully the model will be uncensored but whatever you can do is alright! Love seeing open source structure itself up. I’m not clever enough to actually assist however I can contribute moral support lol

Hello guys, I am even just looking for code for DeepSeek-V2, in order to fully understand multi-head hidden attention. You do not appear to have code in Hugging Face even for that. Or am I missing something? Don’t see anything in src/transformers/models. MLA is not correctly explained in their paper, so it would be essential to have code for this.