MiniMax, the company behind Hailuo_AI, has joined the open source community by releasing both models and demos of MiniMax-Text-01 & MiniMax-VL-01🔥
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MiniMax, the company behind Hailuo_AI, has joined the open source community by releasing both models and demos of MiniMax-Text-01 & MiniMax-VL-01🔥
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- Model
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- Model
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https://huggingface.co/MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-VL-01
model
resource
https://huggingface.co/MiniMaxAI/MiniMax-VL-01
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https://huggingface.co/MiniMaxAI/MiniMax-Text-01
MiniMaxAI/MiniMax-Text-01
model
resource
https://huggingface.co/MiniMaxAI/MiniMax-Text-01
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- Demo
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- Demo
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https://huggingface.co/spaces/MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-VL-01
space
resource
https://huggingface.co/spaces/MiniMaxAI/MiniMax-VL-01
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https://huggingface.co/spaces/MiniMaxAI/MiniMax-Text-01
MiniMaxAI/MiniMax-Text-01
space
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https://huggingface.co/spaces/MiniMaxAI/MiniMax-Text-01
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✨ MiniMax-text-01: 
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✨ MiniMax-text-01: 
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- 456B with 45.9B activated per token
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- 456B with 45.9B activated per token
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- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
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- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
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- Training context up to 1M tokens, inference handles 4M tokens
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- Training context up to 1M tokens, inference handles 4M tokens
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✨ MiniMax-VL-01: 
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✨ MiniMax-VL-01: 
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- ViT-MLP-LLM framework ( non-transformer👀)
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- ViT-MLP-LLM framework ( non-transformer👀)
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- Handles image inputs from 336×336 to 2016×2016
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- Handles image inputs from 336×336 to 2016×2016
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- 694M image-caption pairs + 512B tokens processed across 4 stages
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- 694M image-caption pairs + 512B tokens processed across 4 stages
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MiniMax, the company behind Hailuo_AI, has joined the open source community by releasing both models and demos of MiniMax-Text-01 & MiniMax-VL-01🔥
- Model
https://huggingface.co/MiniMaxAI/MiniMax-VL-01
https://huggingface.co/MiniMaxAI/MiniMax-Text-01
- Demo
https://huggingface.co/spaces/MiniMaxAI/MiniMax-VL-01
https://huggingface.co/spaces/MiniMaxAI/MiniMax-Text-01

✨ MiniMax-text-01: 
- 456B with 45.9B activated per token
- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
- Training context up to 1M tokens, inference handles 4M tokens

✨ MiniMax-VL-01: 
- ViT-MLP-LLM framework ( non-transformer👀)
- Handles image inputs from 336×336 to 2016×2016
- 694M image-caption pairs + 512B tokens processed across 4 stages

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