That model was trained in part utilizing their unreleased R1 "thinking" design. Today they've launched R1 itself, together with a whole family of new designs obtained from that base.
There's a great deal of stuff in the brand-new release.
DeepSeek-R1-Zero appears to be the base model. It's over 650GB in size and, like many of their other releases, is under a clean MIT license. DeepSeek alert that "DeepSeek-R1-Zero encounters difficulties such as unlimited repeating, poor readability, and language blending." ... so they likewise released:
DeepSeek-R1-which "includes cold-start information before RL" and "attains performance similar to OpenAI-o1 throughout math, code, and thinking jobs". That a person is likewise MIT accredited, and is a similar size.
I do not have the ability to run models bigger than about 50GB (I have an M2 with 64GB of RAM), so neither of these 2 models are something I can easily play with myself. That's where the brand-new distilled designs are available in.
To support the research study community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen.
This is a fascinating flex! They have actually designs 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 is compatible with the underlying Llama license. Qwen models are Apache certified so perhaps MIT is OK?
(I also just discovered the MIT license files say "Copyright (c) 2023 DeepSeek" so they may require to pay a bit more attention to how they copied those in.)
Licensing aside, these distilled models are fascinating monsters.
Running DeepSeek-R1-Distill-Llama-8B-GGUF
Quantized variations are currently beginning to appear. Up until now I have actually attempted simply among those- unsloth/DeepSeek-R 1-Distill-Llama-8B-GGUF released by Unsloth AI-and it's truly fun to play with.
I'm running it using the mix of Ollama, LLM and the llm-ollama plugin.
First I brought and ran the design using Ollama itself:
This downloads around 8.5 GB of model data and begins an interactive chat interface.
Once the design has been brought, LLM can talk with it as well. I prefer using LLM for experiments since it logs whatever to SQLite for later exploration.
I set up the plugin and ran it like this:
Each reaction starts with a ... pseudo-XML tag containing the chain of thought utilized to help produce the response. For "a joke about a pelican and a walrus who run a tea space 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 a fascinating combination. Let me consider how these two characters might communicate in a funny 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 big teeth, thick fur, disgaeawiki.info and they're typically found in chillier climates, like icebergs or snowy locations. So, integrating these two into a tea room setting is currently a funny 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 choose to open a quaint little tea room together. The walrus, with its huge size, has a hard time to find comfy chairs, so it rests on the table by mishap, knocking over the teapot. Meanwhile, the pelican, attempting to help, uses its beak to put dishes on the table, triggering a few spills.
After a series of humorous mishaps, the walrus searches for vokipedia.de and states with a grin, "This isn't so fishy any longer." The pelican smirks and mentions, "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 cause comedic chaos.
- Punchline: "This isn't so fishy anymore" is a pun on the scenario becoming less absurd. The follow-up joke presents "krill," a play on words that connects into both marine life ("fish") and tea, making it humorous while fitting the theme.
So yeah, the joke is awful. But the procedure of arriving was such a fascinating insight into how these new designs work.
This was a fairly little 8B model. I'm anticipating trying the Llama 70B variation, which isn't yet available in a GGUF I can keep up Ollama. Given the strength of Llama 3.3 70B-currently my favourite GPT-4 class design 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 traditional Generate an SVG of a pelican riding a bike prompt too. It did refrain from doing extremely well:
It aimed to me like it got the order of the elements wrong, so I followed up with:
the background wound up covering the remainder of the image
It thought some more and offered me this:
As with the earlier joke, the chain of believed in the transcript was much more fascinating than completion result.
Other ways to try DeepSeek-R1
If you wish to attempt the design out without installing anything at all you can do so utilizing chat.deepseek.com-you'll require to create an account (indication in with Google, utilize an email address or offer a Chinese +86 contact number) and after that choose the "DeepThink" alternative below the prompt input box.
the design by means of 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 secret, then use llm -m deepseek-reasoner 'prompt' to run prompts.
This won't show you the thinking tokens, regretfully. Those are dished out by the API (example here) however LLM doesn't yet have a way to display them.