That model was trained in part using their unreleased R1 "thinking" model. Today they have actually released R1 itself, in addition to an entire family of brand-new models obtained from that base.
There's an entire lot of things in the new release.
DeepSeek-R1-Zero seems the base design. It's over 650GB in size and, like many of their other releases, is under a tidy MIT license. DeepSeek warn that "DeepSeek-R1-Zero comes across obstacles such as endless repetition, bad readability, and language blending." ... so they likewise launched:
DeepSeek-R1-which "integrates cold-start data before RL" and "attains efficiency comparable to OpenAI-o1 across math, code, and reasoning tasks". That one is also MIT certified, and is a similar size.
I don't have the capability to run designs larger 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 designs are available in.
To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six thick models distilled from DeepSeek-R1 based on Llama and Qwen.
This is a remarkable flex! They have models 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 models have an MIT license connected, which I'm uncertain works with the underlying Llama license. Qwen designs are Apache accredited so possibly MIT is OK?
(I also simply saw 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, these distilled designs are interesting monsters.
Running DeepSeek-R1-Distill-Llama-8B-GGUF
Quantized versions are currently starting to show up. So far I have actually attempted 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 utilizing the combination of Ollama, LLM and library.kemu.ac.ke the llm-ollama plugin.
First I brought and ran the model using Ollama itself:
This downloads around 8.5 GB of model information and starts an interactive chat user interface.
Once the design has actually been brought, LLM can speak to it also. I prefer using LLM for experiments because it logs whatever to SQLite for later exploration.
I installed the plugin and ran it like this:
Each reaction starts with a ... pseudo-XML tag containing the chain of thought utilized to assist produce the response. 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 space together. Hmm, that's a fascinating combination. Let me believe about how these 2 characters may engage in a funny scenario.
First, let's consider their characteristics. Pelicans are understood for their long beaks and Webbed feet, often seen near the beach or water. Walruses have big teeth, thick fur, and they're normally found in colder climates, like icebergs or snowy locations. So, integrating these 2 into a tea room 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 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 put dishes on the table, causing a couple of 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 says, "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 turmoil.
- Punchline: "This isn't so fishy anymore" is a pun on the circumstance ending up being less absurd. The follow-up joke introduces "krill," a play on words that ties into both marine life ("fish") and tea, making it humorous while fitting the style.
So yeah, the joke is horrible. But the procedure of getting there was such an intriguing insight into how these new models work.
This was a fairly little 8B design. I'm eagerly anticipating experimenting with 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 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 model working like this-a 34GB download:
Can it draw a pelican?
I attempted my classic Generate an SVG of a pelican riding a bike timely too. It did refrain from doing extremely well:
It aimed to me like it got the order of the components incorrect, so I followed up with:
the background ended up covering the remainder of the image
It believed some more and provided me this:
As with the earlier joke, the chain of thought in the records was far more intriguing than completion result.
Other methods to try DeepSeek-R1
If you want to attempt the design out without installing anything at all you can do so utilizing chat.deepseek.com-you'll need to produce an account (indication in with Google, use an email address or offer a Chinese +86 contact number) and after that choose the "DeepThink" option below the prompt input box.
DeepSeek provide the model by means of their API, utilizing an . 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 secret, then utilize llm -m deepseek-reasoner 'prompt' to run prompts.
This won't reveal you the reasoning tokens, sadly. Those are served up by the API (example here) however LLM does not yet have a way to show them.