That design was trained in part using their unreleased R1 "thinking" design. Today they've launched R1 itself, in addition to a whole household of new designs obtained from that base.
There's an entire lot of things in the new release.
DeepSeek-R1-Zero seems the base model. It's over 650GB in size and, like the majority of their other releases, is under a tidy MIT license. DeepSeek alert that "DeepSeek-R1-Zero comes across challenges such as limitless repetition, poor readability, and language blending." ... so they also launched:
DeepSeek-R1-which "integrates cold-start information before RL" and "attains performance equivalent to OpenAI-o1 across math, code, and reasoning tasks". That a person is also MIT accredited, and is a comparable size.
I don't have the ability to run designs bigger 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 models are available in.
To support the research study community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based upon Llama and Qwen.
This is a fascinating flex! They have 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 works with the underlying Llama license. Qwen designs are Apache certified so perhaps MIT is OK?
(I also just saw 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 fascinating beasts.
Running DeepSeek-R1-Distill-Llama-8B-GGUF
Quantized variations are already beginning to appear. So far I've tried just one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF released by Unsloth AI-and it's actually enjoyable to play with.
I'm running it utilizing the mix of Ollama, LLM and the llm-ollama plugin.
First I fetched and ran the model using Ollama itself:
This downloads around 8.5 GB of design information and begins an interactive chat user interface.
Once the design has been fetched, LLM can speak to it also. I prefer using LLM for experiments because it logs whatever to SQLite for later exploration.
I installed the plugin and ran it like this:
Each action starts with a ... pseudo-XML tag containing the chain of idea 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 need to come up with a joke about a pelican and a walrus running a tea room together. Hmm, that's an interesting mix. Let me consider how these 2 characters may engage in an amusing circumstance.
First, let's consider their attributes. Pelicans are understood for their long beaks and Webbed feet, seen near the beach or water. Walruses have big teeth, thick fur, and they're usually discovered in chillier climates, like icebergs or snowy areas. So, integrating these 2 into a tea room setting is currently a funny image.
[...]
It then thought for 20 paragraphs before outputting the joke! Here's the complete 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, utahsyardsale.com struggles to discover comfy chairs, so it rests on the table by accident, overturning the teapot. Meanwhile, the pelican, trying to assist, utilizes its beak to put saucers on the table, causing a couple of spills.
After a series of funny incidents, the walrus looks up and states with a grin, "This isn't so fishy any longer." The pelican smirks and says, "Maybe not, but we do have a lot of krill in our tea!"
Explanation:
- Setup: Pelican and Walrus run a not likely tea room. Their sizes result in comedic turmoil.
- Punchline: "This isn't so fishy any longer" is a pun on the situation ending up being less unreasonable. The follow-up joke introduces "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 process of getting there was such a fascinating insight into how these brand-new designs work.
This was a fairly small 8B design. 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 preferred GPT-4 class model that I've 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 model working like this-a 34GB download:
Can it draw a pelican?
I tried my timeless 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 incorrect, so I followed up with:
the background wound up covering the remainder of the image
It thought some more and offered me this:
As with the earlier joke, the chain of believed in the transcript was even more interesting than completion outcome.
Other ways to try DeepSeek-R1
If you want to try the design out without installing anything you can do so utilizing chat.deepseek.com-you'll need to produce an account (indication in with Google, use an email address or supply a Chinese +86 telephone number) and after that pick the "DeepThink" option below the timely input box.
DeepSeek use 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 key, then utilize llm -m deepseek-reasoner 'timely' to run prompts.
This won't show you the thinking tokens, unfortunately. Those are dished out by the API (example here) but LLM doesn't yet have a way to show them.