nineninesix/kani-tts-2-pt
Text-to-Speech • 0.4B • Updated • 620 • 42
Abduraxim Jabborov
Abduraxim
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liked a model 15 days ago
nineninesix/kani-tts-2-pt liked a model 15 days ago
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reacted to sequelbox's post with 🔥 15 days ago
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3529
Multiple NEW RELEASES for Gemma 4 12B!
- Esper 4, our flagship agentic coder: specialist in coding, architecture, DevOps, and MLOps!
- Tachibana-Agent, trained only on code for dedicated, predictable deployment!
- Guardpoint, our structured medical reasoning model: medical diagnosis, management, knowledge, and understanding in structured, concise form!
GET OUR NEW MODELS:
ValiantLabs/gemma-4-12B-it-Esper4
sequelbox/gemma-4-12B-it-Tachibana-Agent
ValiantLabs/gemma-4-12B-it-Guardpoint
Get the datasets for your own training:
sequelbox/Titanium4-DeepSeek-V4-Pro
sequelbox/Mitakihara2-DeepSeek-V4-Pro
sequelbox/Tachibana4-DeepSeek-V4-Pro
sequelbox/Superpotion-DeepSeek-V3.2-Speciale
Esper 4 is also available for Qwen 3.6 27B: ValiantLabs/Qwen3.6-27B-Esper4
We'll be expanding Esper 4 to more models and releasing new models as funding allows - donate for more, faster, better models and datasets: sequelbox/SupportOpenSource
More to come soon!
go build stuff :)
allegra
- Esper 4, our flagship agentic coder: specialist in coding, architecture, DevOps, and MLOps!
- Tachibana-Agent, trained only on code for dedicated, predictable deployment!
- Guardpoint, our structured medical reasoning model: medical diagnosis, management, knowledge, and understanding in structured, concise form!
GET OUR NEW MODELS:
ValiantLabs/gemma-4-12B-it-Esper4
sequelbox/gemma-4-12B-it-Tachibana-Agent
ValiantLabs/gemma-4-12B-it-Guardpoint
Get the datasets for your own training:
sequelbox/Titanium4-DeepSeek-V4-Pro
sequelbox/Mitakihara2-DeepSeek-V4-Pro
sequelbox/Tachibana4-DeepSeek-V4-Pro
sequelbox/Superpotion-DeepSeek-V3.2-Speciale
Esper 4 is also available for Qwen 3.6 27B: ValiantLabs/Qwen3.6-27B-Esper4
We'll be expanding Esper 4 to more models and releasing new models as funding allows - donate for more, faster, better models and datasets: sequelbox/SupportOpenSource
More to come soon!
go build stuff :)
allegra
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reacted to loleg's post with ❤️ 24 days ago
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1413
Thank you Hugging Face team for some very helpful and quick support today. Greetings from the AI for Good summit in Geneva!
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#1 opened 8 months ago
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Article
Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers
sanchit-gandhi
• • 377 Not worked as planned
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#2 opened over 1 year ago
by Abduraxim
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reacted to singhsidhukuldeep's post with 🔥 almost 2 years ago
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2662
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! 🤗
- 5 replies