That design was trained in part using their unreleased R1 "reasoning" design. Today they've released R1 itself, along with an entire household of new models obtained from that base.
There's a lot of things in the brand-new release.
DeepSeek-R1-Zero seems the base design. It's over 650GB in size and, like most of their other releases, is under a clean MIT license. DeepSeek caution that "DeepSeek-R1-Zero experiences challenges such as unlimited repeating, poor readability, and language blending." ... so they likewise launched:
DeepSeek-R1-which "includes cold-start data before RL" and "attains efficiency comparable to OpenAI-o1 throughout mathematics, code, and reasoning tasks". That a person is also MIT certified, 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 designs are something I can quickly 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 6 thick designs distilled from DeepSeek-R1 based on 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 have an MIT license attached, which I'm uncertain works with the underlying Llama license. Qwen models are Apache certified so perhaps MIT is OK?
(I likewise just saw the MIT license files say "Copyright (c) 2023 DeepSeek" so they might need 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 currently beginning to appear. So far I have actually attempted just one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF launched 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 brought and ran the model utilizing Ollama itself:
This downloads around 8.5 GB of model information and starts an interactive chat user interface.
Once the design has been brought, LLM can speak to it also. I choose utilizing LLM for experiments because it logs everything to SQLite for later exploration.
I installed the plugin and ran it like this:
Each reaction begins with a ... pseudo-XML tag containing the chain of thought utilized to assist generate the reaction. For "a joke about a pelican and a walrus who run a tea room together" that began 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 mix. Let me think about how these 2 characters may interact in a humorous situation.
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 found in cooler climates, like icebergs or snowy areas. So, combining these two into a tea space setting is currently an amusing image.
[...]
It then believed 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 space together. The walrus, with its huge size, struggles to discover comfy chairs, so it sits on the table by mishap, overturning the teapot. Meanwhile, the pelican, trying to assist, utilizes its beak to position dishes on the table, triggering a few spills.
After a series of humorous mishaps, the walrus looks up and says with a smile, "This isn't so fishy anymore." The pelican smirks and mentions, "Maybe not, but we do have a great deal of krill in our tea!"
Explanation:
- Setup: Pelican and Walrus run a not likely tea space. Their sizes cause comedic turmoil.
- Punchline: "This isn't so fishy anymore" is a pun on the situation becoming less unreasonable. The follow-up joke introduces "krill," a play on words that connects into both marine life ("fish") and tea, pl.velo.wiki making it funny while fitting the theme.
So yeah, the joke is terrible. But the process of arriving was such an intriguing insight into how these new models work.
This was a fairly little 8B design. I'm looking forward to checking out the Llama 70B version, which isn't yet available in a GGUF I can keep up 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 variation of that Llama 3.3 70B R1 distilled model working like this-a 34GB download:
Can it draw a pelican?
I tried my classic Generate an SVG of a pelican riding a bike prompt too. It did refrain from doing effectively:
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 provided me this:
Just like the earlier joke, the chain of thought in the transcript was far more interesting than the end outcome.
Other methods to attempt DeepSeek-R1
If you desire to attempt the design out without installing anything you can do so utilizing chat.deepseek.com-you'll require to produce an account (check in with Google, utilize an email address or provide a Chinese +86 contact number) and then select the "DeepThink" alternative below the timely input box.
DeepSeek use the model via their API, using an OpenAI-imitating endpoint. You can access that via LLM by dropping this into your extra-openai-models. yaml configuration file:
Then run llm keys set deepseek and paste in your API key, then use llm -m deepseek-reasoner 'prompt' to run triggers.
This won't show you the thinking tokens, regretfully. Those are provided by the API (example here) however LLM doesn't yet have a way to show them.