That model was trained in part utilizing their unreleased R1 "thinking" design. Today they've launched R1 itself, together with an entire family of new obtained from that base.

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

DeepSeek-R1-Zero appears to be the base model. It's over 650GB in size and, like most of their other releases, is under a tidy MIT license. DeepSeek alert that "DeepSeek-R1-Zero encounters difficulties such as unlimited repeating, bad readability, and language mixing." ... so they also released:

DeepSeek-R1-which "integrates cold-start data before RL" and "attains efficiency comparable to OpenAI-o1 across math, code, and reasoning tasks". That a person is also MIT licensed, and is a similar size.

I don't have the capability to run models bigger than about 50GB (I have an M2 with 64GB of RAM), so neither of these two models are something I can easily play 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 dense models distilled from DeepSeek-R1 based on Llama and Qwen.

This is a remarkable 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 models are Apache certified so maybe MIT is OK?

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

Licensing aside, these distilled designs are interesting beasts.

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

Quantized versions are currently beginning to appear. So far I've tried just one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF launched 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 fetched and ran the design utilizing Ollama itself:

This downloads around 8.5 GB of model data and starts an interactive chat user interface.

Once the design has actually been brought, LLM can speak with it as well. I prefer utilizing LLM for experiments due to the fact that it logs whatever to SQLite for later exploration.

I set up the plugin and ran it like this:

Each action starts with a ... pseudo-XML tag containing the chain of thought utilized to help generate the action. For "a joke about a pelican and a walrus who run a tea space together" that started like this:

Okay, so I need to come up with a joke about a pelican and a walrus running a tea space together. Hmm, that's an intriguing combination. Let me think of how these 2 characters might engage in a funny scenario.

First, let's consider their characteristics. 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 climates, like icebergs or snowy areas. So, combining these 2 into a tea room setting is currently an amusing 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 decide to open a quaint little tea room 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, uses its beak to put dishes on the table, triggering a few spills.

After a series of comical accidents, the walrus searches for and says with a grin, "This isn't so fishy any longer." 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 a not likely tea space. Their sizes result in comedic turmoil.

• 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 humorous while fitting the style.
  
  So yeah, the joke is dreadful. But the procedure of getting there was such a fascinating insight into how these new models work.
  
  This was a fairly little 8B design. I'm eagerly anticipating trying out the Llama 70B variation, which isn't yet available in a GGUF I can run with Ollama. Given the strength of Llama 3.3 70B-currently my favourite GPT-4 class model that I've operated 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 model working like this-a 34GB download:
  
  Can it draw a pelican?
  
  I attempted 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 aspects wrong, so I followed up with:
  
  the background wound up covering the remainder of the image
  
  It believed some more and offered me this:
  
  Similar to the earlier joke, the chain of believed in the records was much more fascinating than the end outcome.
  
  Other ways to try DeepSeek-R1
  
  If you wish to attempt the design out without setting up anything you can do so utilizing chat.deepseek.com-you'll need to create an account (indication in with Google, use an email address or trademarketclassifieds.com offer a Chinese +86 phone number) and after that choose the "DeepThink" alternative below the prompt input box.
  
  DeepSeek provide the model through 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 prompts.
  
  This won't show you the thinking tokens, sadly. Those are served up by the API (example here) however LLM doesn't yet have a way to display them.