That design was trained in part utilizing their unreleased R1 "reasoning" design. Today they've released R1 itself, together with a whole household of new models obtained from that base.
There's a lot of things in the 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 warn that "DeepSeek-R1-Zero experiences challenges such as limitless repetition, bad readability, and language blending." ... so they also launched:
DeepSeek-R1-which "integrates cold-start data before RL" and "attains performance comparable to OpenAI-o1 throughout math, code, and thinking tasks". That one is also MIT certified, and is a similar size.
I don't have the ability to run models bigger than about 50GB (I have an M2 with 64GB of RAM), so neither of these two models are something I can easily play with myself. That's where the new distilled models are available in.
To support the research study neighborhood, we have open-sourced DeepSeek-R1-Zero, chessdatabase.science DeepSeek-R1, and six thick models distilled from DeepSeek-R1 based upon Llama and Qwen.
This is a remarkable flex! They have designs based upon 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 works with the underlying Llama license. Qwen models are Apache certified so possibly MIT is OK?
(I also just observed the MIT license files state "Copyright (c) 2023 DeepSeek" so they may need to pay a little bit more attention to how they copied those in.)
Licensing aside, fraternityofshadows.com these are remarkable beasts.
Running DeepSeek-R1-Distill-Llama-8B-GGUF
Quantized variations are already beginning to reveal up. Up until now I have actually tried simply among those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF launched by Unsloth AI-and it's truly fun to have fun with.
I'm running it utilizing the combination of Ollama, videochatforum.ro LLM and the llm-ollama plugin.
First I fetched and ran the design using Ollama itself:
This downloads around 8.5 GB of design data and begins an interactive chat interface.
Once the design has actually been brought, LLM can talk to it too. I prefer utilizing LLM for experiments since it logs everything to SQLite for later expedition.
I installed the plugin and ran it like this:
Each reaction starts with a ... pseudo-XML tag containing the chain of idea utilized 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 space together. Hmm, that's an interesting combination. Let me consider how these two characters might engage in a funny scenario.
First, let's consider their attributes. Pelicans are understood for their long beaks and Webbed feet, often seen near the beach or water. Walruses have huge teeth, thick fur, and they're usually discovered in chillier climates, like icebergs or snowy areas. So, combining these 2 into a tea space setting is already an amusing 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 decide to open a quaint little tea room together. The walrus, with its huge size, struggles to find comfy chairs, so it sits on the table by accident, overturning the teapot. Meanwhile, the pelican, attempting to assist, uses its beak to place saucers on the table, causing a few spills.
After a series of humorous incidents, the walrus searches for and states with a grin, "This isn't so fishy anymore." The pelican smirks and visualchemy.gallery says, "Maybe not, but we do have a lot of krill in our tea!"
Explanation:
- Setup: Pelican and Walrus run an unlikely tea space. Their sizes lead to comical chaos.
- Punchline: "This isn't so fishy any longer" is a pun on the situation becoming less unreasonable. The follow-up joke introduces "krill," a play on words that ties into both marine life ("fish") and tea, setiathome.berkeley.edu making it amusing while fitting the style.
So yeah, the joke is dreadful. But the process of arriving was such an intriguing insight into how these brand-new designs work.
This was a fairly little 8B model. I'm eagerly anticipating experimenting with 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 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 attempted my timeless 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 components incorrect, so I followed up with:
the background wound up covering the remainder of the image
It believed some more and offered me this:
As with the earlier joke, the chain of believed in the records was far more interesting than the end result.
Other methods to try DeepSeek-R1
If you wish to attempt 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, utilize an email address or supply a Chinese +86 contact number) and after that select the "DeepThink" option below the timely input box.
DeepSeek offer the design by means of 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 keys set deepseek and setiathome.berkeley.edu paste in your API secret, then use llm -m deepseek-reasoner 'timely' to run triggers.
This will not reveal you the thinking tokens, unfortunately. Those are served up by the API (example here) but LLM doesn't yet have a method to display them.