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Introducing Kraken-LoRA – a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model.
Introducing Kraken-LoRA – a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model.
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@fernandofernandes
fernandofernandes
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 , me, 
 , me, 
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@Crystalcareai
Crystalcareai
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 , 
 , 
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@ehartford
ehartford
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 created the Kraken-LoRA!
 created the Kraken-LoRA!
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🔍 What’s the big deal?
🔍 What’s the big deal?
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✅ Size Consistency: While Kraken’s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).
✅ Size Consistency: While Kraken’s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).
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✅ VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.
✅ VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.
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✅ Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.
✅ Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.
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✅ High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.
✅ High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.
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💡 Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance.
💡 Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance.
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Check out the model here: 
Check out the model here: 
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https://huggingface.co/VAGOsolutions/Kraken-LoRA
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VAGOsolutions/Kraken-LoRA
https://huggingface.co/VAGOsolutions/Kraken-LoRA
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Explore the code here: 
Explore the code here: 
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https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA
https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA
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Have fun with Kraken-LoRA! 🐙
Have fun with Kraken-LoRA! 🐙
Introducing Kraken-LoRA – a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model.

@fernandofernandes , me, @Crystalcareai , @ehartford created the Kraken-LoRA!

🔍 What’s the big deal?

✅ Size Consistency: While Kraken’s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).
✅ VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.
✅ Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.
✅ High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.

💡 Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance.

Check out the model here: https://huggingface.co/VAGOsolutions/Kraken-LoRA
Explore the code here: https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA

Have fun with Kraken-LoRA! 🐙
🔥
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👍
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