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That model was trained in part utilizing their unreleased R1 "thinking" model. Today they have actually launched R1 itself, together with a whole household of new designs obtained from that base.

There's a whole lot of things in the brand-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 tidy MIT license. that "DeepSeek-R1-Zero comes across difficulties such as limitless repetition, poor readability, and language mixing." ... so they likewise released:

DeepSeek-R1-which "incorporates cold-start data before RL" and "attains performance similar to OpenAI-o1 throughout mathematics, code, and reasoning tasks". That a person is likewise MIT certified, and is a comparable size.

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

To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, asteroidsathome.net and six dense designs distilled from DeepSeek-R1 based on Llama and Qwen.

This is a remarkable flex! They have actually models 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 is compatible with the underlying Llama license. Qwen models are Apache certified so possibly MIT is OK?

(I also just noticed 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, these distilled models are fascinating monsters.

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

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

I'm running it utilizing the mix 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 design information and starts an interactive chat user interface.

Once the model has been fetched, LLM can talk to it as well. I prefer utilizing LLM for experiments since it logs whatever to SQLite for oke.zone later exploration.

I installed the plugin and ran it like this:

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

Okay, so I require 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 think about how these two characters might interact in an amusing situation.

First, let's consider their qualities. 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 usually discovered in colder environments, like icebergs or snowy locations. 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 records and wiki.snooze-hotelsoftware.de 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, struggles to find comfy chairs, so it rests on the table by mishap, knocking over the teapot. Meanwhile, the pelican, trying to help, uses its beak to put dishes on the table, triggering a few spills.

After a series of funny incidents, the walrus searches for and says with a grin, "This isn't so fishy anymore." The pelican smirks and remarks, "Maybe not, but we do have a lot of krill in our tea!"

Explanation:

- Setup: Pelican and Walrus run an unlikely tea room. Their sizes cause comical chaos.

• Punchline: "This isn't so fishy any longer" is a pun on the circumstance ending up being less absurd. 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 terrible. But the procedure of arriving was such a fascinating insight into how these brand-new models work.
  
  This was a fairly little 8B design. I'm looking forward to trying the Llama 70B version, 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 design that I have actually operated 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 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 aspects wrong, so I followed up with:
  
  the background ended up covering the remainder of the image
  
  It thought some more and gave me this:
  
  Just like the earlier joke, the chain of believed in the records was much more interesting than the end outcome.
  
  Other ways to try DeepSeek-R1
  
  If you want to try the design out without setting up anything at all you can do so using chat.deepseek.com-you'll require to create an account (sign in with Google, use an email address or offer a Chinese +86 contact number) and after that select the "DeepThink" option listed below the timely input box.
  
  DeepSeek offer the model 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 paste in your API secret, then utilize llm -m deepseek-reasoner 'prompt' to run triggers.
  
  This will not show you the thinking tokens, unfortunately. Those are provided by the API (example here) however LLM doesn't yet have a method to show them.