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That model was trained in part utilizing their unreleased R1 "reasoning" design. Today they've released R1 itself, forum.altaycoins.com along with a whole family of brand-new models obtained from that base.

There's a lot of stuff 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 clean MIT license. DeepSeek alert that "DeepSeek-R1-Zero encounters challenges such as unlimited repeating, bad readability, and language mixing." ... so they likewise released:

DeepSeek-R1-which "includes cold-start information before RL" and "attains performance similar to OpenAI-o1 across mathematics, code, and reasoning tasks". That one is also MIT accredited, and is a similar size.

I do not 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 easily play with myself. That's where the new distilled designs are available in.

To support the research neighborhood, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, opensourcebridge.science and 6 thick models distilled from DeepSeek-R1 based on Llama and Qwen.

This is a fascinating 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 designs have an MIT license connected, which I'm uncertain is suitable with the underlying Llama license. Qwen models are Apache certified so possibly MIT is OK?

(I likewise simply discovered the MIT license files say "Copyright (c) 2023 DeepSeek" so they may require to pay a bit more attention to how they copied those in.)

Licensing aside, pipewiki.org these distilled models are remarkable beasts.

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

Quantized variations are currently beginning to appear. Up until now I've tried just one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF released 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 model using Ollama itself:

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

Once the model has actually been brought, LLM can speak with it as well. I prefer using LLM for experiments since it logs whatever to SQLite for ura.cc later expedition.

I set up the plugin and ran it like this:

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

Okay, wiki.eqoarevival.com 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 mix. Let me consider how these 2 characters may communicate in a funny circumstance.

First, let's consider their qualities. Pelicans are understood for their long beaks and tandme.co.uk Webbed feet, typically seen near the beach or water. Walruses have huge teeth, thick fur, and they're normally found in cooler climates, like icebergs or snowy areas. So, integrating these two into a tea room setting is currently a funny image.

[...]
It then believed 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 decide to open a quaint little tea room together. The walrus, with its huge size, has a hard time to find comfortable chairs, so it sits on the table by accident, knocking over the teapot. Meanwhile, the pelican, trying to assist, uses its beak to position dishes on the table, triggering a few spills.

After a series of humorous mishaps, the walrus searches for and says with a grin, "This isn't so fishy any longer." The pelican smirks and mentions, "Maybe not, however we do have a great deal of krill in our tea!"

Explanation:

- Setup: Pelican and Walrus run a not likely tea room. Their sizes result in comical turmoil.

• Punchline: "This isn't so fishy anymore" is a pun on the scenario becoming 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 theme.
  
  So yeah, the joke is dreadful. But the procedure of arriving was such an interesting insight into how these new designs work.
  
  This was a fairly little 8B model. I'm anticipating 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 favourite 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 variation of that Llama 3.3 70B R1 distilled design working like this-a 34GB download:
  
  Can it draw a pelican?
  
  I attempted my classic Generate an SVG of a pelican riding a bicycle prompt too. It did refrain from doing effectively:
  
  It aimed to me like it got the order of the aspects incorrect, so I followed up with:
  
  the background ended up covering the remainder of the image
  
  It thought some more and offered me this:
  
  Similar to the earlier joke, the chain of thought in the records was far more fascinating than completion result.
  
  Other ways to try DeepSeek-R1
  
  If you want to attempt the model out without setting up anything you can do so utilizing chat.deepseek.com-you'll need to create an account (indication in with Google, use an email address or offer a Chinese +86 telephone number) and after that choose the "DeepThink" alternative below the timely input box.
  
  DeepSeek offer the design via their API, utilizing an OpenAI-imitating endpoint. You can access that by means of 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 'prompt' to run prompts.
  
  This won't show you the reasoning tokens, regretfully. Those are served up by the API (example here) but LLM doesn't yet have a method to show them.