That design was trained in part utilizing their unreleased R1 "thinking" design. Today they have actually launched R1 itself, surgiteams.com along with an entire household of new designs obtained from that base.

There's an entire 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 clean MIT license. DeepSeek warn that "DeepSeek-R1-Zero comes across obstacles such as unlimited repetition, poor readability, and language blending." ... so they also launched:

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

I do not have the capability to run models bigger than about 50GB (I have an M2 with 64GB of RAM), so neither of these two designs are something I can easily 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 6 dense models distilled from DeepSeek-R1 based upon Llama and Qwen.

This is an interesting flex! They have actually 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 works with the underlying Llama license. Qwen models are Apache certified so perhaps MIT is OK?

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

Licensing aside, these distilled models are interesting monsters.

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

Quantized versions are currently beginning to reveal up. Up until now I have actually attempted just one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF released by Unsloth AI-and it's truly enjoyable to play with.

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

First I brought and ran the design using Ollama itself:

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

Once the design has been fetched, LLM can talk to it as well. I choose using LLM for experiments due to the fact that it logs everything to SQLite for bytes-the-dust.com later exploration.

I installed the plugin and ran it like this:

Each response begins with a ... pseudo-XML tag containing the chain of idea used to assist create the reaction. 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 intriguing combination. Let me think about how these two characters might engage in an amusing scenario.

First, let's consider their characteristics. Pelicans are understood for bytes-the-dust.com their long beaks and Webbed feet, typically seen near the beach or surgiteams.com water. Walruses have big teeth, thick fur, and they're typically discovered in cooler climates, like icebergs or snowy areas. So, integrating these 2 into a tea space setting is currently a funny image.

[...]
It then believed 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 choose to open a quaint little tea room together. The walrus, with its big size, has a hard time to discover comfy chairs, so it sits on the table by mishap, knocking over the teapot. Meanwhile, the pelican, trying to assist, utilizes its beak to put dishes on the table, causing a couple of spills.

After a series of humorous accidents, the walrus looks up 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 lead to comedic turmoil.

• Punchline: "This isn't so fishy any longer" is a pun on the circumstance becoming less unreasonable. 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 horrible. But the process of getting there was such an intriguing insight into how these brand-new models work.
  
  This was a fairly small 8B model. I'm eagerly anticipating trying 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 model that I have actually 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 design working like this-a 34GB download:
  
  Can it draw a pelican?
  
  I tried my traditional Generate an SVG of a pelican riding a bicycle timely too. It did refrain from doing 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:
  
  Similar to the earlier joke, the chain of thought in the records was even more fascinating than completion result.
  
  Other methods to attempt DeepSeek-R1
  
  If you desire to attempt the model out without setting up anything you can do so utilizing chat.deepseek.com-you'll need to develop an account (check in with Google, use an email address or offer a Chinese +86 telephone number) and then pick the "DeepThink" option below the timely input box.
  
  DeepSeek offer the model by means of their API, using 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 secret, then use llm -m deepseek-reasoner 'timely' to run prompts.
  
  This won't show you the thinking tokens, unfortunately. Those are served up by the API (example here) but LLM does not yet have a method to display them.