That model was trained in part utilizing their unreleased R1 "reasoning" design. Today they have actually released R1 itself, in addition to an entire household of brand-new models obtained from that base.

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

DeepSeek-R1-Zero seems the base design. It's over 650GB in size and, wiki.rolandradio.net like many of their other releases, is under a tidy MIT license. DeepSeek warn that "DeepSeek-R1-Zero encounters obstacles such as limitless repeating, poor readability, and language mixing." ... so they also released:

DeepSeek-R1-which "incorporates cold-start information before RL" and "attains performance similar to OpenAI-o1 throughout math, code, and reasoning tasks". That a person is also MIT certified, and is a similar 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 two designs are something I can easily play 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 six thick models distilled from DeepSeek-R1 based on Llama and Qwen.

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

(I also simply discovered the MIT license files say "Copyright (c) 2023 DeepSeek" so they might require to pay a 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 already starting to show up. Up until now I've tried simply among 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 utilizing the combination of Ollama, LLM and the llm-ollama plugin.

First I brought and ran the model utilizing Ollama itself:

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

Once the model has actually been brought, LLM can talk to it too. I choose utilizing LLM for experiments due to the fact that it logs whatever to SQLite for wiki.fablabbcn.org later expedition.

I set up the plugin and ran it like this:

Each reaction begins with a ... pseudo-XML tag containing the chain of thought used to assist generate the action. 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 an intriguing combination. Let me think about how these 2 characters may interact in an amusing scenario.

First, let's consider their characteristics. Pelicans are known for their long beaks and Webbed feet, typically seen near the beach or water. Walruses have big teeth, thick fur, and they're normally found in colder environments, like icebergs or snowy locations. So, combining 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 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 huge size, struggles to discover comfortable chairs, so it rests on the table by accident, overturning the teapot. Meanwhile, the pelican, attempting to help, utilizes its beak to place saucers on the table, triggering a few spills.

After a series of comical mishaps, the walrus searches for and states 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 an unlikely tea room. Their sizes lead to comical chaos.

• 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 ties into both marine life ("fish") and tea, making it humorous while fitting the style.
  
  So yeah, the joke is dreadful. But the procedure of arriving was such an interesting insight into how these new models work.
  
  This was a fairly small 8B model. I'm anticipating checking out the Llama 70B variation, coastalplainplants.org which isn't yet available in a GGUF I can run with Ollama. Given the of Llama 3.3 70B-currently my preferred GPT-4 class design 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 attempted my timeless Generate an SVG of a pelican riding a bicycle timely too. It did refrain from doing really well:
  
  It aimed to me like it got the order of the elements incorrect, so I followed up with:
  
  the background ended up covering the remainder of the image
  
  It believed some more and gave me this:
  
  Just like the earlier joke, the chain of believed in the transcript was much more fascinating than completion result.
  
  Other ways to attempt DeepSeek-R1
  
  If you wish to attempt the model out without setting up anything at all you can do so using chat.deepseek.com-you'll require to create an account (check in with Google, use an email address or supply a Chinese +86 telephone number) and after that choose the "DeepThink" choice below the prompt input box.
  
  DeepSeek use the design via their API, using an OpenAI-imitating endpoint. You can access that via LLM by dropping this into your extra-openai-models. yaml setup file:
  
  Then run llm secrets set deepseek and paste in your API secret, then use llm -m deepseek-reasoner 'prompt' to run triggers.
  
  This will not show you the reasoning tokens, regretfully. Those are dished out by the API (example here) but LLM doesn't yet have a method to display them.