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Synthetic Data Generator
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Build datasets using natural language
Ansh Gupta
thisisanshgupta
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AI & ML interests
Pytorch | NLP
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argilla/synthetic-data-generator
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thisisanshgupta's activity
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Pdf2audio
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Generate detailed script for podcast or lecture from text input
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Free Youtube URL Video-to-Text Using OpenAI Whisper
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Stable Diffusion 3 Medium
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Generate images from text prompts
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InstantMesh
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Create a 3D model from an image in 10 seconds!
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3DGen Arena
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Generate 3D models from text or images
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Here's a 1-minute video tutorial on how to fine-tune
unsloth/llama-3-8b-bnb-4bit with unsloth
Using Roller Coaster Tycoon peep thoughts as an example
Using Roller Coaster Tycoon peep thoughts as an example
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It took Google’s Transformer model from 2017 a whopping $900 to train. 💸
This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! 💰
Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). 🤖
Compared to OpenAI’s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. 💡
2017: Original Transformer Model: $930 [@Google ] 💻
2018: BERT-Large: $3,288 [@Google] 📚
2019: RoBERTa Large: 160k [@Meta] 🌐
2020: GPT-3(175B): $4.32M [@OpenAI] 🧠
2023: Llama 2 70B: $3.93M [@Meta] 🐑
2023: GPT-4: $78.35M [@OpenAI] 🌟
Now, Gemini Ultra: $191.4M [@Google] 🚀
This forms an exponential curve! 🤯
But, why? 🤔
Compute, data, and expertise. All three come at a great cost! ⚙️📊💡
Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! 🆓
This barrier of cost is going to result in fewer new foundational models/less competition and more fine-tunes! 📉🔄
Data [Stanford University’s 2024 AI Index Report]: https://aiindex.stanford.edu/report/
Graphic: https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088
Many thanks to everyone spending tons of resources and open-sourcing the models! 🤗
This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! 💰
Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). 🤖
Compared to OpenAI’s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. 💡
2017: Original Transformer Model: $930 [@Google ] 💻
2018: BERT-Large: $3,288 [@Google] 📚
2019: RoBERTa Large: 160k [@Meta] 🌐
2020: GPT-3(175B): $4.32M [@OpenAI] 🧠
2023: Llama 2 70B: $3.93M [@Meta] 🐑
2023: GPT-4: $78.35M [@OpenAI] 🌟
Now, Gemini Ultra: $191.4M [@Google] 🚀
This forms an exponential curve! 🤯
But, why? 🤔
Compute, data, and expertise. All three come at a great cost! ⚙️📊💡
Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! 🆓
This barrier of cost is going to result in fewer new foundational models/less competition and more fine-tunes! 📉🔄
Data [Stanford University’s 2024 AI Index Report]: https://aiindex.stanford.edu/report/
Graphic: https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088
Many thanks to everyone spending tons of resources and open-sourcing the models! 🤗
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