Simon Willison's Weblog
That design was trained in part using their unreleased R1 "thinking" model. Today they've released R1 itself, along with a whole household of brand-new models obtained from that base.
There's an entire lot of things in the brand-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 experiences difficulties such as limitless repetition, poor readability, and language blending." ... so they also launched:
DeepSeek-R1-which "includes cold-start data before RL" and "attains performance similar to OpenAI-o1 across mathematics, code, and reasoning jobs". That a person is also MIT certified, and is a comparable 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 two models are something I can easily have fun with myself. That's where the brand-new distilled models are available in.
To support the research neighborhood, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and smfsimple.com six dense designs distilled from DeepSeek-R1 based upon Llama and Qwen.
This is a fascinating 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 models have an MIT license connected, which I'm uncertain works with the underlying Llama license. Qwen designs are Apache certified so possibly MIT is OK?
(I likewise simply observed the MIT license files say "Copyright (c) 2023 DeepSeek" so they may need to pay a little bit more attention to how they copied those in.)
Licensing aside, these distilled models are remarkable monsters.
Running DeepSeek-R1-Distill-Llama-8B-GGUF
Quantized variations are currently beginning to reveal up. Up until now I've attempted simply one of those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF released by Unsloth AI-and it's actually fun to have fun with.
I'm running it utilizing the combination of Ollama, sciencewiki.science LLM and the llm-ollama plugin.
First I fetched and ran the model utilizing Ollama itself:
This downloads around 8.5 GB of model data and starts an interactive chat interface.
Once the model has actually been brought, LLM can speak with it also. I choose utilizing LLM for experiments due to the fact that it logs whatever to SQLite for later exploration.
I installed the plugin and ran it like this:
Each response starts with a ... pseudo-XML tag containing the chain of idea used 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 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 believe about how these two characters may interact in an amusing situation.
First, let's consider their qualities. Pelicans are understood for akropolistravel.com their long beaks and Webbed feet, often seen near the beach or water. Walruses have huge teeth, thick fur, and they're generally discovered in colder climates, like icebergs or snowy locations. So, combining these 2 into a tea space setting is already a funny image.
[...]
It then believed for 20 paragraphs before outputting the joke! Here's the full 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 find comfortable chairs, so it sits on the table by mishap, knocking over the teapot. Meanwhile, the pelican, attempting to help, utilizes its beak to place dishes on the table, causing a couple of spills.
After a series of humorous incidents, the walrus searches for and says with a smile, "This isn't so fishy anymore." The pelican smirks and mentions, "Maybe not, however we do have a great deal of krill in our tea!"
Explanation:
- Setup: Pelican and Walrus run an unlikely tea space. Their sizes result in comical chaos.
- Punchline: "This isn't so fishy anymore" is a pun on the scenario becoming less ridiculous. The follow-up joke introduces "krill," a play on words that connects into both marine life ("fish") and tea, making it humorous while fitting the style.
So yeah, the joke is horrible. But the process of arriving was such a fascinating insight into how these brand-new designs work.
This was a fairly little 8B model. I'm eagerly anticipating trying 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 preferred GPT-4 class model that I've operated 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 design working like this-a 34GB download:
Can it draw a pelican?
I tried my traditional 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 aspects incorrect, so I followed up with:
the background ended up covering the 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 fascinating than the end result.
Other ways to try DeepSeek-R1
If you want to try the model out without installing anything at all you can do so utilizing chat.deepseek.com-you'll need to produce an account (sign in with Google, use an email address or supply a Chinese +86 telephone number) and after that choose the "DeepThink" option listed below the timely input box.
DeepSeek use the design through their API, using 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 paste in your API key, then use llm -m deepseek-reasoner 'timely' to run prompts.
This won't reveal you the thinking tokens, sadly. Those are provided by the API (example here) however LLM doesn't yet have a method to show them.