That design was trained in part utilizing their unreleased R1 "thinking" model. Today they've released R1 itself, in addition to a whole household of brand-new models obtained from that base.

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

DeepSeek-R1-Zero seems 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 encounters challenges such as limitless repeating, poor readability, and language blending." ... so they likewise launched:

DeepSeek-R1-which "integrates cold-start information before RL" and "attains efficiency similar to OpenAI-o1 throughout mathematics, code, and reasoning jobs". That one is likewise MIT certified, akropolistravel.com and is a similar size.

I don't have the ability to run designs bigger than about 50GB (I have an M2 with 64GB of RAM), so neither of these 2 designs are something I can quickly have fun with myself. That's where the new distilled designs are available in.

To support the research neighborhood, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and 6 thick designs distilled from DeepSeek-R1 based upon Llama and Qwen.

This is a remarkable flex! They have models 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 is suitable with the underlying Llama license. Qwen designs are Apache certified so perhaps MIT is OK?

(I likewise just discovered 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, these distilled designs are remarkable beasts.

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

Quantized variations are currently starting to reveal up. Up until now I've attempted just one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF released by Unsloth AI-and it's truly 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 design utilizing Ollama itself:

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

Once the model has been brought, LLM can talk with it as well. I prefer using LLM for experiments because it logs whatever to SQLite for later expedition.

I installed the plugin and ran it like this:

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

Okay, so I need to come up with a joke about a pelican and a walrus running a tea room together. Hmm, that's an interesting combination. Let me consider how these two characters might connect in an amusing situation.

First, let's consider their characteristics. Pelicans are understood for their long beaks and Webbed feet, typically seen near the beach or water. Walruses have huge teeth, thick fur, and they're typically discovered in colder climates, like icebergs or snowy locations. So, integrating these 2 into a tea room setting is currently 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 room together. The walrus, with its huge size, has a hard time to discover comfy chairs, so it rests on the table by mishap, overturning the teapot. Meanwhile, the pelican, trying to assist, utilizes its beak to place dishes on the table, causing a few spills.

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

Explanation:

- Setup: Pelican and Walrus run a not likely tea space. Their sizes lead to comical mayhem.

• Punchline: "This isn't so fishy any longer" is a pun on the situation ending up being less ridiculous. The follow-up joke introduces "krill," a play on words that connects into both marine life ("fish") and tea, making it amusing while fitting the theme.
  
  So yeah, the joke is horrible. But the procedure of getting there was such a fascinating insight into how these brand-new designs work.
  
  This was a fairly small 8B model. I'm looking forward to 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 variation of that Llama 3.3 70B R1 distilled design working like this-a 34GB download:
  
  Can it draw a pelican?
  
  I attempted my classic Generate an SVG of a pelican riding a bike timely too. It did refrain from doing very well:
  
  It aimed to me like it got the order of the components incorrect, so I followed up with:
  
  the background wound up covering the remainder of the image
  
  It believed some more and gave me this:
  
  As with the earlier joke, the chain of thought in the records was far more interesting than completion result.
  
  Other ways to try DeepSeek-R1
  
  If you desire to attempt the model out without installing anything at all you can do so utilizing chat.deepseek.com-you'll need to develop an account (check in with Google, use an email address or supply a Chinese +86 phone number) and then pick the "DeepThink" option listed below the prompt input box.
  
  DeepSeek use the design via their API, utilizing an OpenAI-imitating endpoint. You can access that via LLM by dropping this into your extra-openai-models. yaml configuration file:
  
  Then run llm secrets set deepseek and paste in your API secret, then use llm -m deepseek-reasoner 'prompt' to run prompts.
  
  This will not show you the tokens, sadly. Those are provided by the API (example here) but LLM doesn't yet have a method to show them.