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That design was trained in part using their unreleased R1 "thinking" design. Today they've released R1 itself, together with a whole family of brand-new models obtained from that base.

There's an entire lot of stuff in the brand-new release.

DeepSeek-R1-Zero appears to be the base design. It's over 650GB in size and, equipifieds.com like the majority of their other releases, is under a clean MIT license. DeepSeek caution that "DeepSeek-R1-Zero comes across difficulties such as limitless repetition, bad readability, and language blending." ... so they also released:

DeepSeek-R1-which "includes cold-start information before RL" and "attains efficiency comparable to OpenAI-o1 throughout mathematics, code, and reasoning jobs". That a person 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 two models are something I can quickly play with myself. That's where the new distilled models are available in.

To the research study 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 a remarkable 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 is compatible with the underlying Llama license. Qwen models are Apache accredited so possibly MIT is OK?

(I likewise just discovered the MIT license files state "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 monsters.

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

Quantized variations are currently beginning to appear. 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 have fun with.

I'm running it utilizing 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 model data and starts an interactive chat user interface.

Once the model has actually been fetched, LLM can talk with it as well. I prefer using LLM for experiments since it logs everything to SQLite for later expedition.

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 action. 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 room together. Hmm, pipewiki.org that's a fascinating combination. Let me consider how these 2 characters may connect in a funny scenario.

First, let's consider their characteristics. Pelicans are known for their long beaks and Webbed feet, often seen near the beach or water. Walruses have big teeth, thick fur, and they're typically discovered in chillier environments, like icebergs or snowy locations. So, combining these 2 into a tea room setting is already a funny image.

[...]
It then thought 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 choose to open a quaint little tea space together. The walrus, with its huge size, struggles to find comfy chairs, so it sits on the table by accident, knocking over the teapot. Meanwhile, the pelican, trying to assist, utilizes its beak to place saucers on the table, triggering a couple of spills.

After a series of comical incidents, the walrus searches for and says with a smile, "This isn't so fishy anymore." 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 space. Their sizes lead to comical mayhem.

• Punchline: "This isn't so fishy any longer" is a pun on the scenario becoming less unreasonable. The follow-up joke introduces "krill," a play on words that ties into both marine life ("fish") and tea, making it amusing while fitting the theme.
  
  So yeah, the joke is horrible. But the procedure of getting there was such an interesting insight into how these brand-new designs work.
  
  This was a fairly little 8B design. I'm eagerly anticipating checking out 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 design that I have actually worked 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 bike timely too. It did refrain from doing effectively:
  
  It aimed to me like it got the order of the elements wrong, bybio.co so I followed up with:
  
  the background ended up covering the remainder of the image
  
  It thought some more and offered me this:
  
  As with the earlier joke, the chain of thought in the records was much more fascinating than the end outcome.
  
  Other ways to attempt DeepSeek-R1
  
  If you wish to try the model out without setting up anything at all you can do so utilizing chat.deepseek.com-you'll require to produce an account (check in with Google, use an email address or offer a Chinese +86 telephone number) and after that choose the "DeepThink" alternative listed below the prompt input box.
  
  DeepSeek provide the model by means of 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 key, then use llm -m deepseek-reasoner 'timely' to run prompts.
  
  This will not show you the thinking tokens, regretfully. Those are served up by the API (example here) but LLM does not yet have a way to display them.