Cohere API Community Demos | December 2022

Key Takeaways

foreign [Music] once again super happy to see you at our final call up Friday holiday special this is the very very last event this year therefore we're super excited to have you and to finish the year on a wonderful creative positive note um checking out some of the top Community demos that were built since our last meeting um yeah let's uh kick off the agenda then I won't let you know that today we will be hearing hearing from our latest addition to the team head of devrel Louis Serrano he will be chatting with you in a minute um this will be followed by presentations of the wonderful demos that community members have prepared for you today if you're interested in showcasing your demo um or interested in providing feedback for these demos make sure to come back to collab Fridays make sure to apply for collab Friday as well with your demo um so first we will hear from a particular demo then we will have time for Q a and feedback session and that will happen three times today finally we will choose the demo of the month this time it will be Lewis our honorary remember choosing his favorite demo of uh this event this will be followed by um the most festive Community member so we are scanning through your Christmas outfits hopefully you brought your Christmas game in I do see that there there's definitely effort going on there thank you so much for bringing your most festive most Holiday version of yourself today here um so the winner will actually get a very gorgeous swag so you do want to try to look as cute as possible here um if we have time we'll also do some Christmas trivia um we will I think going to choose we are going to choose three winners with also some really good prices and then we will close up with um upcoming events and a few words uh of goodbye for for the season um myself forgot to introduce myself I'm Sandra I'm part of the developer relations here uh looking after our wonderful Community uh and it is my pleasure to introduce you to Luis Serrano or have a head of Deborah who will be talking about coher Hot Topic today Lewis are you with us you are I sorry yes thank you Sandra for the introduction and thank you I'm so glad to to be in a to be here and meet all the community great developers so yeah I'm the new uh head of developer relations uh here at kohir I started a month ago uh which is short but in Tech years it's it's like three years so yeah I've been I've been here and um yeah it's I'll tell you a little bit about myself and you know I'll talk about something cool that I like um so yeah I started at I've been in tech for a while I was in mathematician originally and in Tech I've worked in different places as a data scientist as a researcher that's other things that my passion has always been teaching and explaining topics so I have a YouTube channel that you should check it out it's called Serrano Academy recently but the 100K subscribers and I explained topics very in a very simple way so I I don't like formulas I just like the sort of like fun examples analogies and um and I also wrote a book called rocking machine learning where I do the same thing uh and they asked me to talk about a topic that I like and it's a great place because generative learning is certainly uh one of my favorite things my favorite uh type of of machine learning I've seen sort of the applications that I didn't think we could do I mean they the text that is generated uh the the images the images that are generated are things that I didn't think a computer could do so I'm fascinated but actually it touches one of my other favorite topics because see the technology is actually moving towards uh generative learning and and what I've done is actually one of my favorite topics Quantum Computing how many people have heard of quantum Computing here so a quantum computer it's pretty much a generative uh model a general learning machine and I'm going to ask you a question so what is the simplest uh generative model that you can imagine so who wants to put it on the chatter or even talk like what is the simplest generative model that comes to your mind the very simple simplest a calculator could be a general model something that generates things right could be image could be text watch yeah interesting uh I'll tell you about bubble machine that's a good one that's a good one it's a random generates random bubbles random number generator that's a good one yep that's exactly it right random noise a random number generator in particular the most basic one I can think of is a coin flip right a coin flip is a generative model uh because it generates a bit which is the most basic unit of information it generates a one or a zero and so if I wanna if it's a fair coin it generates it with half probability each but if let's say you come to me and you say hey Luis I want you to generate this and there's eight heads and two tails well what do I do I put a little bit of padding right and make the coin generate be falling heads eighty percent of the time so I'm I'm training my model to fit the data set you gave me right so that's simplest General model classical computers have a hard time with those because actually well they can generate pseudo-random numbers but it's never fully random right like it's it's actually like if you manage to replicate the conditions of the universe and press the button you get the exact same number one of computers just renovate some random stuff right completely random because a qubit is actually the it's actually a a random it's actually a coin that you flip right and if you have more qubits you just have a lot of coins that you flip and you make them generate what you want so if you have enough of them you can make you can correlate them get to have one uh the first coin flip and if it's heads then it influences the tenth one and that influences the third one and so on and you can make it a generative model that comes out like you know faces or text or something like that um so that's why that's that's something that that excites me uh that excites me a lot um and that's why I find generative learning one of the one of the coolest uh things in the world um so yeah that was my rant I hope that was five minutes but if you have any questions feel free feel free to ask thank you so much that was super fun um guys do you have any questions towards Lewis we have one minute that's the timeline timeline for this would be longer so I see quantum computers getting into gender models but not not right now but probably in the future and there would be something like like enhancing these enhancing this type of this type of classical models right they would be also be like a they'd always be like a a sort of uh hybrid between between the two yeah awesome alrighty we uh we'll be moving to the demos thank you so much Lewis uh guys welcome Lewis our our community will be seeing from hearing from him seeing him much more often from now on super excited about that um and uh yeah let's get going with the demos so first up a little bit of organizational info after each of the demos our cohere mentors David Robin Lewis and Matt will be giving um feedback and asking questions to the speaker please feel free to join them and to also share your feedback and ask questions that are of your interest um you can do that by typing in the chat or you can just unmute yourself directly and share it with us directly which is also a super nice way to interact um first up is arsala Muhammad with a viral meme generator using large language models arson I hope you're ready because we are ready to hear from you yeah hi all uh let me know if you can see my screen not yet but it's coming I think yes it's coming yeah so this Islam I work as a senior software engineer in machine learning India so I work with NLP models uh like go here stable diffusion gpt3 and I built a Cutting Edge solution so by um building the solutions on top of it and concoction of multiple language models and as part of uh Co it's very collaborate I have built a meme world so it's a viral meme generator so here I am using cohere language model and uh image captioning model and I used a multi few short learning approach to generate the viral means and the aim of my project is to spit the smile and explore the humor side of large language models and also sometimes models can be very I have a very biased and hatred towards particular Community array arrays so just to mitigate and improvise the model and uh and also in order to explore a wonders of multi-modal approach or like if you ask a dally to create a meme it will not create mean it even though it is trained on huge Hopper billions of images but it will not create a simple mean but when we connect a language model with image model then we'll definitely will get a meme so in that aspect we can see that uh our simple approach uh will be more uh like a powerful than the Delhi Delhi will create the images but it will not have what it will not have even simple text on it okay so that was my whole aim of the project is and as part of uh hosting I have hosted on hugging spaces and like I said I have used the cohere language models uh human human like uh text generation capabilities and I gave few short examples and I created a image I I combined image captioning uh plus a few short pumping technique of a coher language and built uh and applied on simple uh hugging spaces and uh I'll show you the live demo so this will this is of my website and uh let's say uh first of all no offense for project managers I know they do very good work and uh let us see if you want to create a meme for anything so let us say uh we have the examples here let us see for startups how it will be oh set of winning startup gets funded and you can finally afford a new laptop okay so for me and then we have a okay sorry so arsenal we have a requests from chat uh to to try the marketing one if you can I'm sorry so I added my most common marketing startup budget okay I'm sorry uh I think there's a gas station okay when you can learn how to use Facebook ads but you'll never learn how to ignore them okay it's a lady of old uh old lady and uh that's how the meme is this day another one okay when you have finished all the setting all your email you shouldn't have this but you're still looking for so sometimes uh it will be very funny sometimes it will be average uh so I want uh if anybody has interested uh in exploring and mitigating uh you know bias and uh bias and uh hatred in store particular Community they can report uh as well and so that we can redevelop the model again we send the feedback to uh you know uh coherent language models and we can do some irony on top of it so see here you can have good luck so any suggestion from the chat when you can't have good lead to others without good open rates so I have built this using yeah there are some uh so we just have a question from Rajan is there a word limit on the generation so word limit uh there's no generation but uh to uh actually this will be costly for me so for that I have gone with the 50 tokens but uh I think meme should be in the range of online or two line so I have kept the limit but yeah I have Capital limit here I don't want to show tell my model should not give me the paragraph I don't need paragraph and it is just a one overliner two liner beam that's it the simple uh you know uh simple lab uh for marketing marketing team social media team and uh HR team so these people can create a viral memes uh for marketing social media advertisement and multiple for fun stuff uh and doing and also doing the irony on top of this uh topic and I have built a web app a simple web app uh hugging phase spaces uh and uh if anybody want to create app on top of it uh feel free to contact me uh from the link here we can build a open source app which will you know which will create a viral news so people can easily create the memes and they can easily share it of course social media profiles and uh the uh that Meme generation capability will be very unique and it will be very different each time so that will be the plus point and you can add custom image from here as well if you have and that's how uh that's about my meme world uh since I've reached my limit of a presentation I am open so question and answers thank you awesome thank you so much I can tell you folks that I've been using this uh meme World myself for different types of conferences to just show how llms can work in the meme space and they've been very successful so a good hack we have a question uh coming from one of our mentors David they do onto um sort of talk about it a little bit sure I was just going to ask if you could explain a bit more about the prompt you used for the cohere generation part was it just a few examples you pulled together manually or is it something more complicated uh so see uh four language models uh they're not they're just an extrovert prediction model probabilistic models so uh so I went with a single uh single short learning multi-short learning so only the hack should that we should know how to play around with the prompt engineering so that was uh like a nowadays uh we need to have skills to do prompt engineering we have one parrot who will tell you the story but you need to know how to tune it to tell it to according to you so I applied with multiple pumps a few short learning I used to give with a uh I used to pass on multiple memes and I I constructed the model to generate non-offensive memes if if you if you uh I I given the clear interest section that create non-uh non-offensive and uh humanistic means uh if if I had given create a offensive and a racial means you could have created a racial and offensive news so I I the model to give non-offensive news that's the reason we are getting non-offensive and humorist statements so it's all about I went with a few short learning approach to say awesome one follow-up then did you use a separate prompt for each of the categories like a separate prompt for marketing a separate prompt for product manager or is it all one prompt that can handle different categories so I I automated The Prompt engineering as well so say if if I add a new uh new Department then uh New Meme will get generated this is very cool yeah I automated this one I I have not hard for it I have automated those yeah awesome uh thank you so much David for your question so Ann has a question in the chat asking is there proof of concept in terms of a meme going viral have you tried any of these means to sort of push them out and see how they are his funding for me my meme going viral uh Santa is right here Sandy is here she will she has circles to a very big conferences so so that's so but I have not personally shared it in my LinkedIn but uh when the people use the beam world and I got very good businesses from the LinkedIn oh yeah yeah I agree I I agree the the conferences was um it was a great success during the conferences but I think a really great idea and also sort of like a low-key low in low uh input uh from your end would be to maybe have a like a Twitter account for the meme world and then every now and then generate means and then just throw them out there and see how they're responding I think it would be really cool um okay um yeah thank you so much it's it's not it's not a great experience so check out your project thank you so much um let's move to project number two from today um so this time it will be um Sasha and Rahel talking about research paper semantic search clustering very different Forte I'm really excited to see what you've been cooking um hi there um yeah so we're presenting um about somatic search and clustering of research papers and we developed this project at a AI Transformers hackathon a few weeks ago actually so it was a really fun project to do over the weekend and I think we got a pretty good prototype out of it [Music] um so uh so yeah we're two brothers uh Rahel and Sasha uh we're both software Engineers data scientists we're based out of Ottawa Canada um and yeah and we're really excited to share our project with you um so the purpose of this uh project so researchers generally try to find new papers uh by looking at the related research section and references of of the papers that they're currently reading and the problem is that this could easily lead to getting stuck in sort of a local cluster of research so also in the case where researcher might be trying to find to solve a difficult problem with novel research it might be hard to find applicable Concepts so our goal is to help researchers and students find relevant research using a semantic search and clustering approach and this could potentially also help with finding research Concepts that are used in other fields but haven't been applied in this one yet so this is sort of our approach uh we gathered about abstracts of about 500 papers from archive across different subjects and then we used coherence Transformers to embed the summaries or the abstracts of each paper into an n-dimensional vector and then we put that into an annoy index which was then going to be used to find the closest end neighbors given a query so once we have this query then we use umap in this case to sort of map these n-dimensional vectors into 2D space so that we can view it on on a 2d grid and then we generated hierarchical clusters of all these papers and one of the additional things that we did was that we identified sort of the intersections between the words of each point so each point represents a paper in the cluster using frequency analysis in order to identify the concepts that we present in each of the Clusters so that you can sort of identify all these concepts are present in this cluster and then we built an interactive UI which allows the user to change change the cluster levels input queries and then access the papers directly from the interface and Raha will now give a demo of of the project hello so here we have a chart where each point is a research paper each point has a subject as seen in the legend and is identified by color and if we were to enter a query so for example high energy black holes it's mapped onto the chart and here we can see that iron G black holes is is within mathematics and astrophysics it's in that section of papers the user can also change how many papers they'd like to see related to the query um the papers are also clustered to different levels so if I just adjust this slider I can see two clusters or three clusters or that went one one too many um and so now the user can see which cluster that the query lies in so here our query lies in this cluster of astrophysics in addition you can hover over a cluster and see what keywords are in that cluster so here this cluster is about modeling astrophysical Jets um in addition the user can click on a point to see that paper in detail in the sidebar so here we can see the summary and the title as well as the link and this also changes the cluster slider so that every step will show a new cluster added to the selected papers cluster so if I move this one step we see the closest paper is actually this paper which is which when I was when I clicked on this paper finally the user can deselect the paper with the clear button and I'll post this link in the in the zoom chat so if anybody wants to use the demo uh oh except I can't see the chat so I'll post it after uh then we will move on to uh future work uh so for future work we'd like to train a custom language model on research papers to improve the accuracy of where the research papers are placed uh in addition we'd like to examine the attention mechanism of the model and this would give us better results for keywords or specific Concepts and finally we'd like to improve the user interface one example is to allow the users to track changes in clusters between steps and finally back to Sasha for for the business cases um yeah so we were thinking that ideally we could sort of partner with archive or other research paper reports to provide a project as sort of a paid service um alternatively um you know I'm sure there are many researchers from universities or research Labs that could use this tool in sort of an open source manner as well they could provide their own papers um so that was sort of ideas for sort of the two Avenues of the business case um I'd like to mention that you know archive is a repository of research papers and we're pulling our data from them so you know thank you for archive for uh use of its Open Access interoperability and we also want to say thank you to cohere for allowing us to present our work um yeah so and then we actually have the links to the repo and uh um the GitHub on the next slide yeah that's about it thank you very much awesome thank you so much um thanks uh please by all means post the links in the chat section especially the experiment link so we can play with it um I'm curious if we have any research oriented folks uh in here and whether anybody wants to give um and such a feedback or ask follow-up questions um Joe has a question yeah sorry um I just noticed that Joe on chat had a question what clustering algorithm is used in there oh um yeah it's uh a gloromatic uh aggloromative classroom a glomerative clustering I can't actually say the word uh let me post yeah glamorative yes awesome thank you whoever had a follow-up question go ahead this is Matt Dunn from cohere um uh so yeah this is awesome first and foremost um one question I had was about uh yeah I guess the agglomerative clustering that's one approach did you ever attempt a density based like hdb scan um a clustering that might be something to to look at um I've I've gotten decent results in the past using something like that um yeah did you ever get a chance to play with any other algorithm no we didn't actually um I'm not too knowledgeable about the different types of clustering algorithms I mean to be honest in this situation we sort of just picked this at random I think and just used it uh just because you know it was a two-day thing um so it's called the density based okay yeah yeah yeah there's some pretty good implementations of them too um um and then the other question I had is have you run up against any um when you do the querying have you run it up against any um kind of odd kind of um embedding representations that return like kind of funky results that you're it's unclear like basically maybe you like go for I'm trying to think of an example on top of my head but um the classic example that I've seen in like chat conversational spaces like you you put in the term like I want to buy and then all the messages come back with like buy shoes buy phone buy this buy this right and so like it's not actually focusing on like and if you go put in shoe then it brings back a bunch of buy things that might be like kind of somewhat related and like have you found like triangulating around that problem like difficult with the abstracts and getting the right kind of results to be returned or has it just been kind of plug and play um yeah actually rile did a small experiment yesterday do you want to talk about the small large model thing uh yeah so we we tested the embeddings with the with your the coherence large model and small model and we found that we got better results using the large model because uh it was able to capture the semantic better as opposed to uh the smaller model would focus more on keywords so for example when I put uh celestial bodies and physics as a query uh it would just return abstracts or like the closest neighbors would be results that have physics in the name of it but it didn't actually have anything to do with physics like some of the papers were not actually to do with physics it just had physics in abstract so it focus more on keywords as opposed to the actual semantic evening of the abstract um the other issue we ran into is some of the abstracts are not actually um like well defined yeah they're not well defined uh so uh we definitely so sometimes the subject would be physics but uh it's actually some survey paper so it would go into quantitative bio or something like that so it wouldn't be able to model that because it just doesn't have enough information yeah but I guess that's more of a problem with garbage data as opposed to the model itself um yeah um so no we we haven't seen anything too bad uh but but yeah what we're interested in doing really is sort of trying to validate this approach and seeing sort of the thing that we are most interested in doing so right now we're doing that frequency analysis of the cluster words we want to actually get the words from the model itself so that you know the words are actually representative of the mapping um so that we can then see or at least to be able to compare the two to see if the frequency analysis is uh at least somewhat representative above that um and then to see if this is actually a like you know a useful thing uh would uh is it allowing researchers to do useful work yeah that's that's our next sort of Step yeah very cool very cool okay we have a very prevalent discussion going on in the chat section so I'm gonna choose some of the questions here Alexander is asking have you checked how well the closest matches of a paper matchup would say background section of references uh no we haven't looked at anything that's inside the paper so we're only looking at the the abstract for now I think we were considering doing uh looking at the paper in general and then comparing that so no that would be sort of that's a good suggestion to see um uh our logic I think was that hopefully the abstract is a good enough representation of the paper um that it allows us to do good uh matching and because we were wondering if the paper is a lot of extra stuff in it maybe that'll mess things up with the uh the vectorization I don't know that was sort of the intuition but gotcha um another question coming from Rose what approach might work well for 10 000 papers or more with only light cross referencing and diverse topics any thoughts on that uh uh I don't know maybe hotstar mentors actually yeah right you have you have a time uh no I was just thinking that the cohere is embeddings is is much better than um like because the usual clustering is done with keyword analysis uh but like it doesn't understand semantics so I feel like coherence embeddings uh is a good approach for that and it can handle uh very large amounts of data yeah like I think this approach should scale uh but that would be my guess I'm not sure about the the diverse topics um what would happen with that like if you're trying to find topics which are across multiple things I'm not sure what would happen um but I would I would guess for just a large amount of paper is that this should scale pretty well totally I will second that I think it should totally scale um now question coming from actually this is more of a question to cohere team so if anyone wants to jump on it from cohere Team please do John is asking instead of chunking large papers could you first ask here to create a paper summary that is within the size limit and then use those summaries to perform clustering so I'll chime in instead of writing my response um you could but in reality since embed and generate have similar token limits you'll run into a similar chunking problem with long summarization if you're trying to summarize an entire paper you'll likely have to chunk summarize summarize chunks you could eventually get to a paper summary it's just about how much information you're going to lose and is it more valuable than just using an abstract I think it could help in the events it sounds like there's some abstracts that are just not great so maybe it helped for like low quality abstractions but otherwise I don't know if you'd see much of an improvement special success thank you thanks for covering that okay um John is thinking as well um in case there are no more questions we will move to the next demo thank you so much guys yeah definitely if anybody has more questions like feel free to like connect with us offline as well yes you can you can hit up folks on this card we will be posting their projects and they will be available for follow-up questions and maybe call UPS if you're interested because it looks like there are many passionate folks around the in the in the research paper area space so um hopefully you get some support and thank you so much that was great we will move on now to um again today uh Arsenal Muhammad who does not stop uh innovating and coming up with ideas that win hackathons and you know come to the top of the top of coher demos this time he is going to show us the house of happiness and hopefully he'll explain what it means in the demo in my screen I'm sharing my screen that's it but I think in a second we should see it yes yeah so I'll start uh this means I'm the same guy who built a meme world and uh I've also built a battle sewer which means House of happiness in Arabic and I won a generative and uh my use case is I have built a deep entry designing tool which will leverage cohere command language model and also in combination with stable diffusion and uh upscaling model uh to do uh aesthetic XTS and interior designing of the buildings uh and uh coming for the designing designing now it is not just uh you know copying the latest Trend happening but is it it is also you know it shows you the personality like a what kind of personality you are uh even by looking at your home people can judge you so that's the beauty of design and how important is for into your into your friend excuse as well so the tools I have used over here uh is square language model stable diffusion uh ESR game uh and uh dream studio and Google collab and uh as uh I have built a simple POC and have hosted on the hugging face again and uh this is the simple interface I will upload the image and I'll select uh what I want to design I will show the working demo after this and I'll get the corresponding uh image a redesigned image okay then I build one more space which will uh automate uh automated the process in the back end and it will create the video out of it I'll show uh so since the video creation will take much time for the Simplicity sake of it uh I will show you the image creation and I will show you the video result how it will be and uh here are the results so say uh I have a another user I want to redesign this uh my say bedroom maybe for some say Christmas and uh let's go here we'll write a description for the Interiors of my living bedroom for in the theme of of Christmas okay this must are in the theme of Arabic design uh you have a vintage European American Indian old old Chinese Japanese sky's the limit okay then this is the input image and output images this one so we can see that uh there are some quite so it's not a Christmas theme I'm sorry I thought of uh no I got it right so it is a great design so if you want to see the video how it transform let us see so video will get transformed into uh multiple dimensions and multiple uh changes of a user has any particular uh design to select he can pass on and he can choose the design and artifacts accordingly and here is one more wizard this is the initial input image and if you want to redesign your Hall so let us see the results and it's a video so I am redesigning users Arabic house uh typical uh Saudi Arabian Middle Eastern Dubai we can see the results in front of us so uh GPU will uh take huge computational uh for each image generation it will take 10 seconds imagine this is this sphere is you know like a offered a 100 images combined uh and they stitch together so computation will burn up so I have not given uh make this as open source but image when they have made open source you can try it out and yeah this is one more example a transformational happens and if you have any specific uh time lapse through stop you can stop and redesign you can choose uh you will have you'll have a liberty to choose any time frame and you can which is in your house and yeah and one more functionality uh which can be done is uh like say you got a output of this image okay you've got this images output and if you want to buy any particular thing then what you need to do uh go you can you can search a particular uh image using a Google lens and you will get a relevant uh uh irrelevant items available in the internet or you can connect with the I am trying to integrate uh interesting uh you know who do a carpenter work so you can connect with them and you can get your uh space redesign according to a so why now uh so so what's the goal so my goal is to Revelation is uh limited designing extra it is in its space and I want to make it freemium or free for all and premium for uh interior designers who want more features like 4K they want videos so those people will pay but for normal people uh common people they can redesign or I am planning to give 100 kids per month like say uh 100 images they can redesign for free completely free why now so you can see our generative a is like a like a uh it's penetrating to multiple domains like a finance uh medical industry uh interior designing so I want to uh create a solution which will have first no Advantage uh in a generative art uh generative art uh interior design okay that will be very first Niche and also I'm looking forward to create the same replicate the same work in generative clothing generative handbags generative uh jewelry so sky's the limit uh I'm I am combining the power of two modules one is a cohere one is a table diffusion or the daily or anyone and I'm creating a business value out of it so that's my uh and there is a potential as well so like uh as I want the hackathon uh and my post went on trending like anything and I got a huge positive responses from across the globe across geographies so I think that's the testamental fact that uh I got my validation that uh if I get a POC uh up and running as a full scale then it's gonna uh for sure uh hopefully uh in next year and uh future future water now future what we can do is uh we can add Advanced feature as well we can extract uh some Dimensions out of it so more identity can be done uh as part of it but for initial launch I will just redesigning will be there and uh background you see here it is not my house this is also very designed by using this space only so it's not my actual background it's a redesigned one okay uh thank you uh if you want to uh scale up and collaborate you know uh to have us your own thoughts you am available at this uh social media handles uh I can be connected at any time uh on Discord as well I am if you have any future hackathons participation I'm I know I I have time limit but I'll show you one example yes example also please space the the links to your demos to having faced spaces in the chat definitely so folks can um make sure that you use a I have a very uh uh uh make sure you use a very generous not generously very uh responsibility because there are a lot of credits maybe other people not get okay so let me selected uh some image and I gave I selected uh click here to select design a cohere command language to design I'll select internet username of living room and I need to play along with only this value 0.55 to 0.9 uh don't play with this and see if I guess say submit I got this image then I can do the image so I should Google ah and uh see if I want to buy anything uh TV I can buy TV but I want to buy uh anything uh architecture I just need to focus on and I can buy anything simple uh like let us drag now you know uh design of room in Christmas I have not read this I am trending now hopefully you will get the result so uh the model will uh what it will do it will uh it will uh capture the exact Dimension when your value is close to 0.55 when it is moving away from 0.55 to 0.9 uh then it will check okay we have it we have here redesigned Christmas one so let us let us try to keep according to the same Dimension so for that I will keep my value close to 0.5 and let us see how my output will be I hope everyone will go and try and reduce and for Christmas and your Eve yeah we have so it's still still in POC part I'm open for more uh comments feedbacks uh like uh more collaborators who want to make this is an app make this a full website full-fledged website I'm open for everything and I can be connected uh with uh my email address I'll share my email address as well awesome thank you so much that was mind-blowing though first of all congrats to you for putting this together uh there is lots of amazement and Wows in the chat section so um it's definitely something very new um there's a bunch of questions here so I'm gonna jump into it um after um so so David is asking actually David maybe you want to you know elaborate on it um uh on your questions as you posted on the chat thanks I was just asking uh just to clarify which part you're using the cohere model for it's for the descriptions of the room is that right yep detailed description of them if I just ask uh uh do we reduce in the space using two three words it will redesign but for more uh granular no more comment uh I am using a coher command language model to create more uh you know more description out of it yeah got it very cool okay we have another question from um so does it take into account the size of the room this image video that it isn't generated but based on the size of the room only wait I'll share my screen again I said uh I'll just need to give images input and make sure that my uh values if I want exact same dimensions I need to keep the value close to 0.55 if I keep away from 0.5 to 0.9 my Dimensions will gonna be very random so ideal ideal number should be point five to point uh 0.5 to 0.7.8 okay no thank you uh and follow-up question from Ann as well so just to confirm after you get the results someone can go and purchase what's in this given room by going to the by using Google lens and and checking out what's available that's similar yeah yes yes yes something similar but I'm trying to add uh mode my adoption dropshipping link or make a fair link for the you know Amazon or any uh Alibaba website so I'm just a working work user but for now it works with Google Lens perfectly foreign had some feedback for you Arjun do you want to share it directly yeah sure I was just thinking about how to improve this model over time what's nice about the setup that you have it seems very straightforward to just get like before photos of a room and after photos of a room whether you're working with firms directly or whether you scrape them however you want uh incorporating those into your model retraining and seeing the improvements I feel like doing something like that would be a great way to improve the performance of the model going into the future models already really cool but that idea just came to me like maintaining it yeah I was like I got you as soon as the data set is like a completely uh like a it's a like a general model so what I'm planning to do is like uh once I you know like I get more uh revenue and more people and more comments from the users I'm planning to fine-tune the stable diffusion model with the uh purely on you know um architectural uh photorealistic images but uh for POC purpose this is my work like a good suggestion though because uh yeah because for more Improvement and more photorealistic I am planning to fine tune the model with uh uh say more uh aesthetic and more diverse actual photos yeah good yeah thank you make your work yeah thank you thank you so much ursulin thank you guys for uh your questions and comments you can continue with your questions and comments on our display channel are someones available there um and I think it is time to pick the demo of the month so Luis if you're with us please show up just to recap um the demo of the month so here's pickup of the month uh first of all it's sort of a chair on top because we are choosing awesome demos as you could see yourself today that there's no winners or losers today um the echo here speak of the months will have a um we'll get a one-on-one mentorship discussion on Discord with one of our cohere co-founders uh also we'll get a Discord badge on our Discord barge featured um on our Discord and we will feature the project on the social media to give it a little bit more promotional love so without further Ado Lewis it's all in your hands now very tough choice by the way you know what's the hardest part of my job picking up picking amongst amazing demos uh these were all amazing um so thank congratulations to to all of you this is so impressive uh yeah so um I was I was in the spirit of Christmas allowed to give uh two prizes so I'll give an honorable mention and uh and a first price and they can both receive swag um so yeah they uh The Honorable this is I don't mentioned the second president since I I was in Academia for a while and I know the difficulty of the of the research papers space uh definitely I was very impressed with uh with sashan and raheel's project so that one gets the honorable mention congratulations it's amazing amazing work how you managed to to Cluster it and and really understand the space that even the researchers normally don't fully understand so congratulations for that one and uh and the winner is is Mohammed who had uh completely thrown around the park with that with the that project and has managed to connect so many different things image and uh text and and uh yeah that's super super impressive so congratulations to the both of you and actually one thing I like about it that both are pretty much set for um for for production right like you can take it to company and hopefully them right away so oh thank you so it's a big fan of your work uh Louis uh I like your work and like your YouTube channel so it's like fan movement for me uh thank you so much uh hopefully you will get mentorship from cohere for Windows I can you know can make it as a full-fledger website so I'm much excited uh Merry Christmas for me from now yeah thank you so much everyone thank you thanks thank you awesome okay Lewis thank you so much that the toughest job has done um now we are going into the more pleasurable part of um choosing the most festive Community member and in here we have looked very closely at your festive attires today and what's what the jury came up with Roy thank you so much for that for the tune um we decided that both comrade and John Carlo uh well best dressed and puts enough efforts to get some really cute holidays work from us so thank you so much [Music] can we see um Conrad and John Carla please yeah can you guys see me yes yes hello loving this filter of yours yeah hello sorry my filter is not Christmas but it's like being a cook food breaks the happiness it's very Christmas is nonetheless food is very Christmassy I agree awesome thank you guys you look super cute um now we um we're going to move into a group photo um so first of all since we are all here and we are relatively addressed in a Christmas fashion let's um make a cute face I will count to three and then you do your best in terms of the photo if not I'm sorry this is your one and only chance you're gonna take seconds smile and one with crazy pose crazy that'll be funnier so crazy face in three two one go all right Lewis I think you won um this this contest now uh face that's cute or uh smile or you know however you want to put it in three two one awesome you will see the outcomes of your photo shoots in a while on our Discord and on social media so stick around thank you so much and we have three more minutes to uh test the trailer generator which is actually a cohere demo done by Elaine who is with us today Elaine um thank you so much for putting this together we're gonna generate some trivia questions and the winner will also get some cute [Music] um some cute um prizes so let's do it basically the way it works you put Christmas topic into the generator and here we go what percentage of Americans buy a real Christmas tree you guys um please okay 40 60 30. any anymore responses let's see 38.4 who was the closest I think I think there's a tie between Arjun and Arjun and and Toby so um I guess both of you okay Iceland as well okay so anyone that answered 45 you guys will receive them uh price please reach out to Roy afterwards to make sure that you get it we have time for one more question so let's do it what is the name of the red that's a pretty vague question though but I guess we know who's the red well um I get I guess it's Rudolph right but it could be Santa as well it's very confusing Elaine some user feedback here that was confusing let's work on the red um so okay how about we generate one more what country is the home of the Santa Claus Finland Canada Canada Greenland Finland well Finland one so congratulations um you folks that answered Finland you shall be rewarded Robin that's a political opinion let's not boys about here um so thank you so much guys it's been a blast I hope you have a wonderful warm happy Christmas lots of food lots of quality time with your folks lots of presents as well not only from here but but from other resources hopefully Santa won't fail this time lots of wine yes I agree I'm on the wine team definitely um hope to see you next year recharged and full of ideas thank you so much for coming thank you for to our presenters for the wonderful demos and clothes for the tough job as well as our mentors and everyone that participated in the discussion um let's follow up on this card and have a wonderful Christmas and happy New Year take care [Music]