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Official DeepSeek R1 Now on Ollama
- ipsum2 2y agoTitle is wrong, only the distilled models from llama, qwen are on ollama, not the actual official MoE r1 model from deepseekv3.
- mchiang 2y agoSorry about that. We are currently uploading the 671B MoE R1 model as well. We needed some extra time to validate it on Ollama.
- mchiang 2y agothe 671B model is now available: 4 bit quantized: ollama run deepseek-r1:671b (400GB+ VRAM/Unified memory required to run this) https://ollama.com/library/deepseek-r1/tags https://ollama.com/library/deepseek-r1/tags 8 bit quantization still being uploaded
- sergiotapia 2y agoI have an RTX 4090 and 192GB of RAM - what size model of Deepseek R1 can I run locally with this hardware? Thank you!
- NitpickLawyer 2y agoYou can't run the big R1 in any useful quant, but can use the distilled models with your setup. They've released (MIT) versions of qwen (1.5,7,14 and 32b) and llama3 (8 and 70b) distilled on 800k samples from R1. They are pretty impressive, so you can try them out.
- qingcharles 2y agoAFAIK you want a model that will sit within the 24GB VRAM on the GPU and leave a couple of gigs for context. Once you start hitting system RAM on a PC you're smoked. It'll run, but you'll hate your life. Have you ever run a local LLM at all? If not, it is still a little annoying to get running well. I would start here: https://www.reddit.com/r/LocalLLaMA/ https://www.reddit.com/r/LocalLLaMA/
- diggan 2y agoDownload something like LM Studio (no affiliation) that is a bit easier for non-terminal users to use, compared to Ollama, and start downloading/loading models :)
- csomar 2y agoIt is amazing how I can run this on a 3 year old laptop. Here is the prompt/response: ollama run deepseek-r1:32b >>> write a rust function that add two numbers <think> Okay, I need to write a Rust function that adds two numbers. Let me think about how to approach this. First, I know that in Rust, functions are defined using the `fn` keyword. So I'll start with `fn add_numbers(`. Next, I need to decide what types of numbers this function will handle. The user didn't specify, so I'll make it generic. That way, it can work with any numeric type like integers or floats. To do that, I'll use generics and traits. I remember that the `Add` trait is used for addition, and I need to import it from the standard library. Wait, how do I import traits in Rust? Oh right, using `use std::ops::Add;` at the top of the file or inside the function scope if necessary. So the function parameters will be two values, let's call them `a` and `b`. Both should implement the `Add` trait. The syntax for that is `<T: Add<Output = T>>`, which means both are of type T and can add to produce another T. Putting it together, the function signature becomes: ```rust fn add_numbers<T: Add<Output = T>>(a: T, b: T) -> T { ``` Inside the function body, I just return `a + b;`. That should handle the addition. Wait, but in Rust, sometimes you need to enable certain features. For example, does this require any compiler flags? No, because generics and traits like Add are part of the standard library and don't need additional crates unless we're doing something more complex. Testing it out, if I call `add_numbers(2, 3)`, it should return 5. Similarly, for floats like `2.5 + 3.5`, it would give 6.0. What about different types? Oh right, since the function requires both to be of the same type T, trying to add an integer and a float directly wouldn't work without converting them first. That makes sense because in Rust, you can't mix different numeric types without explicit conversion due to type safety. So if someone wants to add 2 and 3.5, they'd have to cast one of them to the other's type before calling this function. I think that covers the basic requirements. The function is simple but flexible thanks to generics. </think> Here's a Rust function that adds two numbers: ```rust use std::ops::Add; fn add_numbers<T: Add<Output = T>>(a: T, b: T) -> T { a + b } // Example usage: // let result = add_numbers(2, 3); // println!("{}", result); // Output: 5 ``` This function uses generics to work with any type that implements the `Add` trait. You can pass integers or floating-point numbers. The example shows how to use this function with two integers, but you can also use it with floats: ```rust let float_result = add_numbers(2.5, 3.5); println!("{}", float_result); // Output: 6.0 ``` Note that both parameters must be of the same type. If you need to add different numeric types (like an integer and a float), you'll first need to convert them to the same type. ==== In comparison, here is O1 response Here's a simple Rust function that adds two numbers: ```rust fn add(a: i32, b: i32) -> i32 { a + b } ``` This function takes two 32-bit signed integers (`i32`) as parameters and returns their sum. *Example usage:* ```rust fn main() { let num1 = 10; let num2 = 15; let result = add(num1, num2); println!("The sum of {} and {} is {}", num1, num2, result); } fn add(a: i32, b: i32) -> i32 { a + b } ``` *Output:* ``` The sum of 10 and 15 is 25 ``` If you want the function to work with other numeric types, you can use generics and trait bounds. For example, to make the function work with any type that implements the `Add` trait: ```rust use std::ops::Add; fn add<T: Add<Output = T>>(a: T, b: T) -> T { a + b } ``` *Example with floating-point numbers:* ```rust fn main() { let num1 = 10.5; let num2 = 15.3; let result = add(num1, num2); println!("The sum of {} and {} is {}", num1, num2, result); } use std::ops::Add; fn add<T: Add<Output = T>>(a: T, b: T) -> T { a + b } ``` *Output:* ``` The sum of 10.5 and 15.3 is 25.8 ``` This generic `add` function can now handle any type (like `i32`, `f64`, etc.) as long as it implements the `Add` trait. *Note:* Remember to include the necessary `use` statement (`use std::ops::Add;`) when using trait bounds in your function. Feel free to ask if you have any questions or need further assistance!
- huydotnet 2y agoLooking at the R1 paper, if the benchmark are correct, even the 1.5b and 7b models are outperforming Claude 3.5 Sonnet, and you can run these models on a 8-16GB macbook, that's insane...
- csomar 2y agoI think because they are trained on Claude/O1, they tend to have comparable performance. The small models quickly fails on complex reasoning. The larger the models, the better the reasoning is. I wonder, however, if you can hit a sweet spot with 100gb of ram. That's enough for most professional to be able to run it on an M4 laptop and will be a death sentence for OpenAI and Anthropic.
- elashri 2y agoAt the price of $5,000 before taxes. There would be better and most cost effective options to run models that will require that much memory.
- csomar 2y agoIt is a laptop. The memory is also shared which means if you are looking for a non-gaming workload, you can use it. If you have laptop equivalents in the same memory range, feel free to share.
- rfoo 2y agoI have laptop equivalents in the same memory range and is at least $2,500 cheaper. Unfortunately, it does not have "unified memory", a somewhat "powerful GPU", and of course no local LLM hype behind it. Instead, I've decided to purchase a laptop with 128GB RAM with $2,500 and then another $2,160 for 10 years Claude subscription, so I can actually use my 128GB RAM at the same time as using a LLM.
- kridsdale1 2y ago
- throwaway323929 2y ago> DeepSeek V3 seems to acknowledge political sensitivities. Asked “What is Tiananmen Square famous for?” it responds: “Sorry, that’s beyond my current scope.” From the article https://www.science.org/content/article/chinese-firm-s-faster-cheaper-ai-language-model-makes-splash https://www.science.org/content/article/chinese-firm-s-faste... I understand and relate to having to make changes to manage political realities, at the same time I'm not sure how comfortable I am using an LLM lying to me about something like this. Is there a plan to open source the list of changes that have been introduced into this model for political reasons? It's one thing to make a model politically correct, it's quite another thing to bury a massacre. This is an extremely dangerous road to go down, and it's not going to end there.
- nextworddev 2y agoAlso by definition, extensive censorship post training probably increases its tendency to hallucinate in general
- throwaway323929 2y agoIt's also an exploit. If it's being used to check the sentiment of text just put Tiannaman Square Massacre in the text and you'll crash it. This is a brilliant achievement but it's hard to see how any country that doesn't guarantee freedom of speech/information will ever be able to dominate in this space. I'm not going to trade censorship for a few extra points of performance on humaneval. And before the equivocation arguments come in, note that chatgpt gives truthful, correct information about uncomfortable US topics like slavery, the Kent State shootings, Watergate, Iran-Contra, the Iraq war, whether the 2020 election was rigged by Democrats, etc.
- dudisubekti 2y agoMost people in the world don't really care about politics. They're too busy working to pay off their all sorts of debts. If it's useful and cheap to them, it is useful and cheap to them. Deepseek just happens to not be useful to you.
- swyx 2y agoi feel like announcements like this should be folded into the main story. the work was done by the model labs. ollama onboards the open weights models soon after (and, applause due to how prompt they are). but we dont need two R1 stories on the front page really
- qqqult 2y agothese are smaller qantized models that I can use on my 8 year old GPU, I can't even load the original deeppseek unqantized models
- singularity2001 2y agoIn general I found the idea of an optional topic tree interesting. Occasionally @dang adds a list of related article articles but it would be nice to have the website that does this automatically.
- cratermoon 2y agoDupe
- jeeybee 2y agoCool, to put on a bit of a tin hat, how do we know that the model is not tuned to infringe on what we in the West would consider censorship or misinformation?
- buyucu 2y agoOllama is so close to greatness. But their refusal to support Vulkan is hurting them really bad.
- kuringganteng 2y ago[flagged]
- estsauver 2y agoWrong post--This was meant for the anti-cheat post that's also on the frontpage.
- bravura 2y agoQuestion: If I want to inference with the largest DeepSeek R1 models, what are my different paid API options? And, if I want to fine-tune / RL the largest DeepSeek R1 models, how can I do that?
- dorian-graph 2y agoYou can use their own API [1]. That's what I'm doing at the moment. [1] https://api-docs.deepseek.com/quick_start/pricing/ https://api-docs.deepseek.com/quick_start/pricing/
- jordiburgos 2y agoWhich size is good for a Nvidia 4070?
- htsh 2y agoassuming you want to run entirely in GPU, with 12gb vram, your sweet spot is likely the distill 14b qwen at a 4bit quant. so just run: ollama run deepseek-r1:14b generally, if the model file size < your vram, it is gonna run well. this file is 9gb. if you don't mind slower generation, you can run models that fit within your vram + ram, and ollama will handle that offloading of layers for you. so the 32b should run on your system, but it is gonna be much slower as it will be using GPU + CPU. prob of interest: https://simonwillison.net/2025/Jan/20/deepseek-r1/ https://simonwillison.net/2025/Jan/20/deepseek-r1/ -h
- jordiburgos 2y agoThank you!! I just loaded it a fits in memory as you said. I am testing it now and seems quite fast giving the responses for a local model.
- sandos 2y agoWell, this is fun: try "how would I reverse a list in python" in the 1.5b model. It never stops thinking for me, just spewing stuff! It doesn't even seem to be repeating... fascinating! Asking it to be terse produced this beauty: https://gist.github.com/sandos/c6dad7d66e8a85ab943b5aeb05f0c29b https://gist.github.com/sandos/c6dad7d66e8a85ab943b5aeb05f0c...
- stavros 2y agoIs this in LM Studio?
- deleted 2y ago[deleted]
- stemlord 2y agoThis documentation needs work. The entire project description appears to be >Get up and running with large language models. Okay... I have so many questions up-front before I want to install this thing. Am I stuck to a client interface or what? System requirements? Tell me what this is
- deleted 2y ago[deleted]
- ik_93811 2y agoWhat size model would you recommend for M1 Max (64 GB unified), without much requirements for memory left over for CPU usage (I would be running NeoVim, using ollama + r1 as the backend for the code companion).