That design was trained in part utilizing their unreleased R1 "reasoning" design. Today they've released R1 itself, tandme.co.uk along with an entire household of new models obtained from that base.

There's a lot of stuff in the new release.

DeepSeek-R1-Zero appears to be the base design. It's over 650GB in size and, like most of their other releases, islider.ru is under a clean MIT license. DeepSeek alert that "DeepSeek-R1-Zero experiences obstacles such as limitless repeating, bad readability, and language mixing." ... so they likewise released:

DeepSeek-R1-which "incorporates cold-start information before RL" and "attains performance similar to OpenAI-o1 across mathematics, code, and thinking tasks". That a person is likewise MIT certified, and is a comparable size.

I do not have the capability to run designs larger than about 50GB (I have an M2 with 64GB of RAM), so neither of these 2 models are something I can quickly play with myself. That's where the new distilled models are available in.

To support the research neighborhood, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six thick designs distilled from DeepSeek-R1 based on Llama and Qwen.

This is a fascinating 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 models have an MIT license attached, which I'm uncertain works with the underlying Llama license. Qwen designs are Apache licensed so perhaps MIT is OK?

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

Licensing aside, these distilled designs are fascinating monsters.

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

Quantized variations are already starting to show up. Up until now I have actually tried just one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF launched by Unsloth AI-and it's really fun to play with.

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

First I brought and ran the model using Ollama itself:

This downloads around 8.5 GB of model information and begins an interface.

Once the design has actually been brought, LLM can speak to it also. I prefer using LLM for experiments because it logs everything to SQLite for later expedition.

I installed the plugin and ran it like this:

Each action starts with a ... pseudo-XML tag containing the chain of thought used to help create the response. 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 a fascinating mix. Let me think of how these 2 characters may communicate in a humorous circumstance.

First, let's consider their attributes. Pelicans are understood for their long beaks and Webbed feet, often seen near the beach or water. Walruses have huge teeth, thick fur, and they're typically found in cooler environments, like icebergs or snowy locations. So, integrating these 2 into a tea space setting is already a funny image.

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

The Joke:

A pelican and a walrus choose to open a quaint little tea space together. The walrus, with its big size, struggles to discover comfortable chairs, so it rests on the table by mishap, overturning the teapot. Meanwhile, the pelican, attempting to assist, utilizes its beak to place saucers on the table, triggering a few spills.

After a series of funny accidents, the walrus looks up and states with a smile, "This isn't so fishy any longer." The pelican smirks and mentions, "Maybe not, but we do have a great deal of krill in our tea!"

Explanation:

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

• Punchline: "This isn't so fishy any longer" is a pun on the scenario ending up being less ridiculous. The follow-up joke presents "krill," a play on words that connects into both marine life ("fish") and tea, making it amusing while fitting the style.
  
  So yeah, the joke is terrible. But the procedure of getting there was such a fascinating insight into how these new designs work.
  
  This was a fairly little 8B model. I'm eagerly anticipating trying the Llama 70B version, 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 model that I have actually worked on my own machine-I have high expectations.
  
  Update 21st January 2025: I got this quantized version of that Llama 3.3 70B R1 distilled design working like this-a 34GB download:
  
  Can it draw a pelican?
  
  I tried my classic Generate an SVG of a pelican riding a bike prompt too. It did refrain from doing effectively:
  
  It aimed to me like it got the order of the elements incorrect, so I followed up with:
  
  the background wound up covering the remainder of the image
  
  It believed some more and provided me this:
  
  Just like the earlier joke, the chain of thought in the transcript was even more interesting than completion outcome.
  
  Other methods to attempt DeepSeek-R1
  
  If you wish to attempt the design out without installing anything you can do so using chat.deepseek.com-you'll need to create an account (indication in with Google, use an email address or provide a Chinese +86 phone number) and prawattasao.awardspace.info then choose the "DeepThink" alternative listed below the timely input box.
  
  DeepSeek offer the model via their API, using an OpenAI-imitating endpoint. You can access that by means of 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 use llm -m deepseek-reasoner 'prompt' to run triggers.
  
  This will not show you the reasoning tokens, sadly. Those are provided by the API (example here) however LLM does not yet have a method to display them.