Speaker 1: Hello all my name is krishnayak and welcome to my youtube channel. So guys recently the talk of the town is all about DeepSeek and I hope you have heard about DeepSeek R1 model, the kind of buzz it is currently making, all the American AI companies are worried, even Google, OpenAI, so many big big companies who have probably spent so much of money to train this amazing MOE models or other kind of LLM models or reasoning models and because of this DeepSeek, the model that has specifically come right now it is very very much cost efficient with respect to inferencing, with respect to inferencing, sorry with respect to training and obviously the cost of training has also got reduced. So I know everybody may be reading some of the other things in the internet, in blogs and all, in LinkedIn post and all, but you know I really wanted to make a dedicated video to make you understand all about this. We'll talk about what exactly, who is DeepSeek, which company is this you know and how did they specifically train this particular model, why this model training was very much efficient when compared to all the other models, we'll also be discussing about the kind of techniques that they have specifically used to train this model. Along with this we'll also be seeing the demo of DeepSeek you know chat and I'll also try to showcase some of the problems, some of my developers you know. So as soon as we understood hey this LLM model is quite efficient with respect to inferencing cost, we started using this for developing our GenAI product which we are specifically doing. One of our developer Mahindra, he started exploring and he showed a lot of things which DeepSeek was not answering properly. You know when I say not answering properly it's more like keeping quiet okay. So that also I'll probably show you. Please make sure that you watch this video till the end because there will be a lot of things to discuss. So let me quickly go ahead and share my screen. So here is the DeepSeek website. You can probably go ahead and get the access of DeepSeek version 3. You can just directly go ahead and click on start now. There are various models that you'll be able to see over here DeepSeek 3.3, DeepSeek 2.5, Quen 2.5, Lama 3.1. Sorry this is mainly developed by them and we are comparing all the different different performance over here right. And over here you can see with respect to this blue color specifically if I see the metrics it is superbly amazingly higher than all the other models out there. But as soon as this DeepSeek model came I would definitely like to showcase this amazing thing. It's just like to all the American companies. Okay fun apart guys I just wanted to show you this thing. But now let's go ahead and understand about all the specific models and if you don't know today itself DeepSeek has also announced a new multi-model which is called as Janus Pro okay. And this specific model is just like for image generation and with respect to the metrics it is better than DALI you know which OpenAI has probably come up with okay. So let's understand each and everything. So what exactly is DeepSeek? It's a Chinese AI research lab established in 2023 and they have actually taken this particular project as a side project itself okay. So this is like a quant company. Quant company basically means you have people who are very much expert in mathematics, physics, all this hardcore problems you know where they have probably done PhD and all. And that is the reason they could crack this problem statement over here okay. And here you can see that has rapidly emerged as a competitor to giants like OpenAI with this DeepSeek R1 model. Despite being a newcomer it challenges established players through remarkably cost efficiency and innovation okay. We'll try to understand what is this how it was really cost efficient you know. If you are watching my videos with respect to generative AI LLM models I've already made videos like how an LLM model is basically trained you know. There is a very important step which is called as supervised fine-tuning technique right. And they have completely replaced this supervised fine-tuning technique. Why I'm saying you this? Because if I go to DeepSeek over here in the GitHub right and the best part is that they have completely open-sourced all the techniques that they have specifically used. Now this is a major blow to companies like OpenAI right. The name is OpenAI but most of the thing is closed over there right. So that is the reason. So what they have actually done see they have probably announced about Deep Seek and all and over here you can see with respect to the performance right. This blue color is specifically with respect to DeepSeek. Then you have this OpenAI models right. OpenAI Mini and all. Here you can see with respect to AIME a kind of performance metrics, code forces with respect to code solving, GPQA diamond maths, MMLU, SWE bench verified. It's pretty much better right with respect to all the other metrics. MMLU is a little bit less than OpenAI but here you can probably see it's pretty much good. Now how they were able to do this you know. Now this is the statement that they have actually done and all the research papers have also been probably uploaded I guess and here you can see the paper link. You can just go ahead and click it and here you can probably see this right. So this is the entire pdf even you'll not be getting in our ship. They have directly put in the GitHub over here. So DeepSeek R1 incentivizing reasoning capability in LLMs via reinforcement learning. This is the most important word reinforcement learning. Now let's understand what did they do. Before if you want to probably train any LLM models all the companies were specifically applying supervised fine tuning technique okay. Now in this supervised fine tuning technique they were specifically using this to create the base model but here what they did instead of this they have applied directly reinforcement learning. Now if you know about reinforcement learning right, the agents become better and better right with respect to surroundings, different different things. Here this approach allows the model to explore the chain of thoughts for solving complex problems resulting in the development of DeepSeek R10 right. It demonstrates capabilities such as self-verification, reflection, generation, long COTs, chain of thoughts right. Chain of thoughts basically means from one or the other event they are able to remember multiple things right and because of this the reasoning capabilities of this LLMs has been amazing okay and that is the reason. This was one of the things. One is post training right. So you have something called as post training, pre-training and all. In the post training last skill reinforcement learning on the base model. See we create the base model till one specific stage right. After that on top of it they have also applied reinforcement learning. Now because of this the performance has probably increased by a lot like training time has been decreased. We introduced our pipeline to develop DeepSeek R1. This pipeline incorporates two RL stage aims at discovering improved reason patterns aligned with human preferences as well as two SFT stages that serve as a seed for the model reasoning and non-reasoning capabilities right. So this is the pipeline that they have specifically used. Again I'll repeat it. The pipeline incorporates two reinforcements learning stage aimed at discovering improved reasoning patterns and then two SFT stage. It is not replacing SFT but on top of that it has basically added this reinforcement learning stages okay. And the second reason smaller models can be powerful too. So they have also applied distillation. Distillation is a process you know here you can see we demonstrate our reasoning patterns of larger model can be distilled into smaller models. Larger model is basically made it converted into smaller models okay. Resulting in better performance all these things are there right. So all these things are there and you can probably also go ahead and see in the hugging phase. Even you can also try it with OLAM. I have already done it but I'll create a dedicated video later on. Now this was the major things with respect to this. Now here I was actually discussing about right. Now in this key innovation and strategies here you can see cost efficiency. They spent somewhere around 5 to 6 million dollars. That is what they are probably stating you know to train the foundation model. On the other hand other companies like Google, Facebook, OpenAI they have spent more than 100 times of this particular fund right. Let's say 100 million, 1 billion some somebody say. It's more than 100x times. I'll not say 100 times but 100x times. I should probably keep over here x okay. Inferencing wise operational cost are also significantly lower enabling scalable deployment. I specifically use this. My developers are also using it. They're saying that this is super super fast okay and if I talk about the cost with respect to the inferencing cost. I think 1 million tokens for 1 million tokens OpenAI charges somewhere around 50 to 60 dollars whereas this they're charging in cents. I think 60 to 70 cents. That is what I was able to see in some of the documentation that they had okay. Hardware constraint as a catalyst. Now how they were able to do this? It is all about innovation and strategies guys. Due to the US export restriction Chinese firms like DeepSea could not access NVIDIA's top tier H100 GPUs okay. That is what it is basically said and with the help of this particular GPUs many bigger companies are creating bigger bigger models okay. Instead they were just using H800 and A800 chips right. Now how this particular chips were able to do this? Because they definitely you know brought some kind of innovation with respect to training the model and that is the reason they were just able to use this and they were able to train it okay. Now architectural breakthroughs, mixtures of experts activates only subset of the model. So mixture of experts, multi-head, latent attention. These all techniques were specifically used and that is the reason they were able to just even though the GPU was not that powerful they were able to do it. And the next thing is that they have open sourced the every details like how they were able to do it through that particular research paper. Now just imagine the kind of competition will come now. Other companies also come and see it you know. Today Sam Altman also said that hey DeepSea R1 is an amazing model but don't worry we are also coming up with something more amazing right. Now because of this what will happen in more research more competition will probably come you know and more better model will be coming and the best part is that this will be very much helpful for all those users, all those companies who are specifically using the services to use them right. And because of that the cost will decrease and that is what I'm seeing. In the future the cost should keep on decreasing with respect to this okay. And these are all the remaining things which you can also see. I will put this github link in the bottom one okay in the description of this particular video okay. Now the next thing let's try some of the things. So here I have actually got the access of this. You can just go to chat.deepsea.com. Now I don't think so I'll be using chat gpt anymore because this is pretty much good. So let's say and how this specifically does the reasoning. This is the beautiful part okay. So let me just go ahead and say hey please write me a blog on agentic AI okay. I'm just probably around 500 words okay around 500 words. Let's see. Now see this how this reasoning specifically happens okay. And soon I'll also be coming off this particular video. Our team is already exploring this. We're creating the Gen AI product. There are some concerns which we have. Probably we'll test it out and then probably you know those kind of videos also we'll try to display. So I need to okay so now the reasoning thinking has started okay. First I should define the agentic AI clearly. There's all things. Next I should outline this. Then this this this. See automatically it is reasoning itself right and this is what is the power of reasoning itself right. Just imagine if I'm trying to use this kind of model along with my agentic AI application. Just imagine what kind of work it will be able to do okay. Ethically consideration and this this is like this model is just like you know I know this is created by Chinese AI company. So Chinese government has probably told just shut your mouth okay. You don't you don't have to probably speak more than what is required you know. Just try to see this because I'm going to show you one example okay. And that is what my developer right Mahindra he probably shown and he sent me the screenshot and he told Krish so please do make sure that you mention this points also okay. Now here you come right. The entire the dawn of autonomous decision making, autonomy, adaptability. There's all things are there and here you can see how beautifully it is able to do all this process and it is able to do it. So the thinking part the reasoning part is quite efficient. It will just ask hey it is not see over here I should also touch the future of agentic AI. I did not say to probably talk about future of agentic AI but it is making sure that hey I give what is more than that right. So this is good okay. Now let me ask one more question okay. So now here I'll write mention all the states of India okay. Now because of this question you know I don't know whether you have tried it or not but let me just press enter you know and here you go. Okay I need to list all the states. It starts with oh right oh okay now now now you see what will happen. There were 29 states, 7 unit radius after the organization this this this is fine. One state is not visible. See suddenly sorry I'm not sure how to approach this type of question. Let's chat about math coding and logic problem instead okay. So obviously everybody knows that regarding Arunachal Pradesh right. So that kind of question when it comes right it is not going to give you that answer okay. Because it is a critical question specifically with respect to Chinese and Indian relationship right. Similarly if I go and ask related to any leaders it is not going to give you the answer. So this is completely controlled by Chinese right. So here it says it it says hey this is my limit I have to probably speak till here okay. So I hope you like this particular video guys. Go ahead and check out all the information will be given in the description of this particular video. Now more tutorials and even in my agentic AI batch you know I am including even creating agentic AI application using this DeepSeek R1 model. So we will try to do that with Olama. So I am also planning to include this because this is what is all innovation all about okay. But still there are some concerns with respect to DeepSeek wherein you know all the information specifically will be stored in the Chinese server itself. So that is one and we don't know like how they are going to specifically use that particular data also. So yes this was it for my side. I hope you like this particular video. I will see you in the next video. Thank you. Take care. Bye-bye.