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

There's a great deal of stuff in the new release.

DeepSeek-R1-Zero appears to be the base design. It's over 650GB in size and, like the majority of their other releases, is under a clean MIT license. DeepSeek alert that "DeepSeek-R1-Zero experiences difficulties such as limitless repeating, poor readability, and language mixing." ... so they also launched:

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

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

To support the research study community, 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 fascinating flex! They have actually designs based on 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 likewise just discovered the MIT license files say "Copyright (c) 2023 DeepSeek" so they may need to pay a little bit more attention to how they copied those in.)

Licensing aside, gratisafhalen.be these distilled models are remarkable beasts.

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

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

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

First I brought and ran the model using 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 to it also. I choose using LLM for experiments due to the fact that it logs everything 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 assist create the reaction. For "a joke about a pelican and a walrus who run a tea room together" that began 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 a fascinating mix. Let me think of how these 2 characters might interact in an amusing circumstance.

First, let's consider their attributes. Pelicans are understood for their long beaks and Webbed feet, typically seen near the beach or water. Walruses have big teeth, yewiki.org thick fur, and they're typically discovered in cooler environments, like icebergs or snowy areas. So, combining these two into a tea room setting is currently an amusing image.

[...]
It then thought for 20 paragraphs before outputting the joke! Here's the full 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 big size, has a hard time to find comfy chairs, asteroidsathome.net so it sits on the table by accident, knocking over the teapot. Meanwhile, the pelican, attempting to assist, utilizes its beak to put dishes on the table, causing a couple of spills.

After a series of comical incidents, the walrus looks up and states with a smile, "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 an unlikely tea room. Their sizes cause comical mayhem.

• Punchline: "This isn't so fishy any longer" is a pun on the situation becoming less unreasonable. The follow-up joke introduces "krill," a play on words that ties into both marine life ("fish") and tea, making it funny while fitting the style.
  
  So yeah, the joke is dreadful. But the process of arriving was such an intriguing insight into how these brand-new models work.
  
  This was a fairly little 8B design. I'm 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 preferred GPT-4 class model 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 model working like this-a 34GB download:
  
  Can it draw a pelican?
  
  I tried my classic Generate an SVG of a pelican riding a bicycle prompt too. It did refrain from doing extremely well:
  
  It aimed to me like it got the order of the elements wrong, so I followed up with:
  
  the background ended up covering the remainder of the image
  
  It believed some more and gave me this:
  
  As with the earlier joke, forum.altaycoins.com the chain of thought in the records was far more interesting than completion outcome.
  
  Other ways to try DeepSeek-R1
  
  If you wish to try the model out without setting up anything you can do so utilizing chat.deepseek.com-you'll require to produce an account (indication in with Google, use an email address or offer a Chinese +86 telephone number) and then choose the "DeepThink" alternative below the timely input box.
  
  DeepSeek provide the design via their API, utilizing an OpenAI-imitating endpoint. You can access that through LLM by dropping this into your extra-openai-models. yaml setup file:
  
  Then run llm secrets set deepseek and asteroidsathome.net paste in your API secret, then use llm -m deepseek-reasoner 'prompt' to run triggers.
  
  This will not reveal you the thinking tokens, unfortunately. Those are dished out by the API (example here) but LLM does not yet have a way to display them.