That model was trained in part their unreleased R1 "reasoning" model. Today they've released R1 itself, together with an entire family of new designs obtained from that base.
There's an entire lot of things in the new release.
DeepSeek-R1-Zero appears to be the base model. It's over 650GB in size and, like most of their other releases, is under a tidy MIT license. DeepSeek alert that "DeepSeek-R1-Zero comes across challenges such as endless repeating, poor readability, and language blending." ... so they also launched:
DeepSeek-R1-which "includes cold-start information before RL" and "attains performance comparable to OpenAI-o1 throughout math, code, and reasoning jobs". That one is also MIT accredited, 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 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 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 an interesting flex! They have actually models based upon Qwen 2.5 (14B, photorum.eclat-mauve.fr 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 suitable with the underlying Llama license. Qwen designs are Apache licensed so maybe MIT is OK?
(I likewise just saw the MIT license files state "Copyright (c) 2023 DeepSeek" so they might require to pay a little bit more attention to how they copied those in.)
Licensing aside, these distilled models are interesting beasts.
Running DeepSeek-R1-Distill-Llama-8B-GGUF
Quantized variations are already starting to appear. So far I have actually tried just among 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 using the combination 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 wiki.vst.hs-furtwangen.de starts an interactive chat user interface.
Once the model has been brought, LLM can speak to it as well. I choose using LLM for experiments since it logs everything to SQLite for later exploration.
I set up the plugin and ran it like this:
Each response begins 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 room 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 about how these 2 characters might interact in a humorous scenario.
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 found in chillier climates, like icebergs or qoocle.com snowy areas. So, combining these two into a tea space setting is already an amusing image.
[...]
It then thought for 20 paragraphs before outputting the joke! Here's the complete transcript 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 room together. The walrus, with its big size, struggles to discover comfy chairs, so it sits on the table by accident, overturning 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 mishaps, the walrus looks up and states with a smile, "This isn't so fishy any longer." The pelican smirks and remarks, "Maybe not, but we do have a lot of krill in our tea!"
Explanation:
- Setup: Pelican and Walrus run a not likely tea space. Their sizes result in comedic chaos.
- Punchline: "This isn't so fishy any longer" is a pun on the scenario 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 style.
So yeah, the joke is awful. But the process of arriving was such a fascinating insight into how these brand-new models work.
This was a fairly little 8B design. I'm eagerly anticipating trying out the Llama 70B variation, 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 model that I have actually operated 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 timeless Generate an SVG of a pelican riding a bike timely too. It did refrain from doing really well:
It aimed to me like it got the order of the components wrong, so I followed up with:
the background wound up covering the remainder of the image
It believed some more and offered me this:
Just like the earlier joke, the chain of believed in the records was much more interesting than completion outcome.
Other ways to try DeepSeek-R1
If you desire to try the design out without setting up anything at all you can do so utilizing chat.deepseek.com-you'll need to develop an account (check in with Google, utilize an email address or supply a Chinese +86 phone number) and after that select the "DeepThink" option below the prompt input box.
DeepSeek provide the design via their API, using an OpenAI-imitating endpoint. You can access that through LLM by dropping this into your extra-openai-models. yaml configuration file:
Then run llm keys set deepseek and paste in your API secret, then use llm -m deepseek-reasoner 'timely' to run triggers.
This won't reveal you the thinking tokens, it-viking.ch unfortunately. Those are served up by the API (example here) but LLM does not yet have a method to display them.