That design was trained in part utilizing their unreleased R1 "thinking" model. Today they have actually launched R1 itself, along with a whole household of new models obtained from that base.
There's a whole lot of stuff in the brand-new release.
DeepSeek-R1-Zero appears to be the base model. It's over 650GB in size and, like the majority of their other releases, is under a tidy MIT license. DeepSeek warn that "DeepSeek-R1-Zero comes across obstacles such as unlimited repeating, poor readability, and language blending." ... so they likewise launched:
DeepSeek-R1-which "integrates cold-start data before RL" and "attains efficiency similar to OpenAI-o1 throughout mathematics, code, and thinking jobs". That one is also MIT certified, and higgledy-piggledy.xyz is a comparable size.
I don't have the ability 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 neighborhood, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and securityholes.science 6 thick designs distilled from DeepSeek-R1 based on Llama and wiki.vst.hs-furtwangen.de 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 designs have an MIT license attached, which I'm uncertain is compatible with the underlying Llama license. Qwen designs are Apache certified so possibly MIT is OK?
(I likewise simply saw 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, these distilled designs are interesting beasts.
Running DeepSeek-R1-Distill-Llama-8B-GGUF
Quantized variations are currently starting to reveal up. So far I have actually attempted simply among those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF launched by Unsloth AI-and it's really fun to play with.
I'm running it using 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 model data and begins an interactive chat user interface.
Once the model has actually been fetched, LLM can talk with it also. I prefer using LLM for experiments due to the fact that it logs whatever to SQLite for later expedition.
I set up the plugin and ran it like this:
Each response starts 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 space together" that started 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 of how these two characters may interact in an amusing scenario.
First, let's consider their characteristics. 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 usually found in cooler environments, like icebergs or snowy areas. So, combining these two into a tea space setting is currently an amusing image.
[...]
It then thought for 20 paragraphs before outputting the joke! Here's the full transcript and here's the output at the end, chessdatabase.science after the tag:
The Joke:
A pelican and clashofcryptos.trade a walrus decide to open a quaint little tea room together. The walrus, with its big size, struggles to discover comfortable chairs, so it sits on the table by accident, overturning the teapot. Meanwhile, the pelican, trying to assist, utilizes its beak to position saucers on the table, triggering a couple of spills.
After a series of comical mishaps, the walrus searches for and says with a smile, "This isn't so fishy any longer." The pelican smirks and says, "Maybe not, however we do have a great deal of krill in our tea!"
Explanation:
- Setup: Pelican and online-learning-initiative.org Walrus run an unlikely tea space. 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 amusing while fitting the style.
So yeah, the joke is terrible. But the process of arriving was such an interesting insight into how these new models work.
This was a fairly small 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'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 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 aspects incorrect, so I followed up with:
the background ended up covering the remainder of the image
It believed some more and gave me this:
As with the earlier joke, the chain of believed in the transcript was much more interesting than the end result.
Other ways to attempt DeepSeek-R1
If you wish to try the design out without setting up anything at all you can do so using chat.deepseek.com-you'll need to develop an account (check in with Google, use an email address or provide a Chinese +86 phone number) and then pick the "DeepThink" alternative below the prompt input box.
DeepSeek offer the design via their API, utilizing an OpenAI-imitating endpoint. You can access that via LLM by dropping this into your extra-openai-models. yaml setup file:
Then run llm keys set deepseek and paste in your API key, then utilize llm -m deepseek-reasoner 'prompt' to run triggers.
This won't reveal you the thinking tokens, sadly. Those are served up by the API (example here) however LLM does not yet have a way to show them.