That design was trained in part utilizing their unreleased R1 "thinking" model. Today they've released R1 itself, along with a whole family of brand-new designs obtained from that base.

There's a lot of things in the brand-new release.

DeepSeek-R1-Zero appears to be the base model. It's over 650GB in size and, like the majority of their other releases, is under a clean MIT license. DeepSeek warn that "DeepSeek-R1-Zero comes across challenges such as limitless repetition, bad readability, and language blending." ... so they likewise released:

DeepSeek-R1-which "incorporates cold-start data before RL" and "attains performance similar to OpenAI-o1 across mathematics, code, and thinking jobs". That one is also MIT certified, and is a comparable size.

I don't have the ability to run models larger than about 50GB (I have an M2 with 64GB of RAM), so neither of these two models are something I can easily have fun with myself. That's where the new distilled designs are available in.

To support the research community, fraternityofshadows.com we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and online-learning-initiative.org 6 thick models distilled from DeepSeek-R1 based upon Llama and Qwen.

This is an interesting flex! They have actually designs based upon Qwen 2.5 (14B, 32B, Math 1.5 B and Math 7B) and Llama 3 (Llama-3.1 8B and Llama 3.3 70B Instruct).

Weirdly those Llama designs have an MIT license connected, which I'm uncertain works with the underlying Llama license. Qwen designs are Apache certified so maybe MIT is OK?

(I likewise simply observed the MIT license files state "Copyright (c) 2023 DeepSeek" so they may require to pay a little bit more attention to how they copied those in.)

Licensing aside, wiki.snooze-hotelsoftware.de these distilled designs are interesting beasts.

Running DeepSeek-R1-Distill-Llama-8B-GGUF

Quantized versions are already beginning to show up. Up until now I've tried simply one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF released by Unsloth AI-and it's actually enjoyable to have fun with.

I'm it using the combination of Ollama, LLM and the llm-ollama plugin.

First I fetched and ran the model utilizing Ollama itself:

This downloads around 8.5 GB of design information and begins an interactive chat interface.

Once the model has actually been brought, LLM can talk to it also. I prefer utilizing LLM for experiments since it logs whatever to SQLite for later exploration.

I installed the plugin and ran it like this:

Each action begins with a ... pseudo-XML tag containing the chain of idea used to help produce the action. For "a joke about a pelican and a walrus who run a tea room together" that started like this:

Okay, so I require to come up with a joke about a pelican and a walrus running a tea space together. Hmm, that's an interesting mix. Let me believe about how these 2 characters may engage in a humorous circumstance.

First, let's consider their attributes. Pelicans are known for their long beaks and Webbed feet, typically seen near the beach or water. Walruses have huge teeth, thick fur, and they're typically found in chillier climates, like icebergs or snowy locations. So, integrating these two into a tea space setting is currently an amusing image.

[...]
It then thought for 20 paragraphs before outputting the joke! Here's the complete transcript and here's the output at the end, after the tag:

The Joke:

A pelican and a walrus decide to open a quaint little tea room together. The walrus, with its huge size, has a hard time to find comfortable chairs, so it sits on the table by mishap, knocking over the teapot. Meanwhile, the pelican, trying to help, uses its beak to position dishes on the table, triggering a few spills.

After a series of comical mishaps, the walrus searches for and states with a smile, "This isn't so fishy anymore." The pelican smirks and remarks, "Maybe not, however we do have a great deal of krill in our tea!"

Explanation:

- Setup: Pelican and Walrus run an unlikely tea space. Their sizes result in comedic chaos.

• Punchline: "This isn't so fishy any longer" is a pun on the circumstance becoming less unreasonable. The follow-up joke introduces "krill," a play on words that connects into both marine life ("fish") and tea, ratemywifey.com making it amusing while fitting the style.
  
  So yeah, the joke is terrible. But the process of getting there was such a fascinating insight into how these brand-new models work.
  
  This was a fairly small 8B design. I'm anticipating experimenting with the Llama 70B variation, which isn't yet available in a GGUF I can keep up Ollama. Given the strength of Llama 3.3 70B-currently my preferred GPT-4 class design that I've run on my own machine-I have high expectations.
  
  Update 21st January 2025: I got this quantized variation of that Llama 3.3 70B R1 distilled design working like this-a 34GB download:
  
  Can it draw a pelican?
  
  I attempted my timeless Generate an SVG of a pelican riding a bicycle timely too. It did refrain from doing effectively:
  
  It aimed to me like it got the order of the aspects incorrect, so I followed up with:
  
  the background wound up covering the remainder of the image
  
  It thought some more and offered me this:
  
  Just like the earlier joke, the chain of believed in the transcript was far more fascinating than the end result.
  
  Other methods to attempt DeepSeek-R1
  
  If you desire to try the design out without setting up anything you can do so using chat.deepseek.com-you'll require to create an account (indication in with Google, use an email address or offer a Chinese +86 contact number) and then choose the "DeepThink" alternative below the timely input box.
  
  DeepSeek provide the design through their API, utilizing an OpenAI-imitating endpoint. You can access that through LLM by dropping this into your extra-openai-models. yaml configuration file:
  
  Then run llm secrets set deepseek and paste in your API key, then utilize llm -m deepseek-reasoner 'timely' to run triggers.
  
  This will not show you the thinking tokens, regretfully. Those are provided by the API (example here) however LLM doesn't yet have a method to show them.