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@@ -55,12 +55,12 @@ A collection of open-access models for neurological imaging, analysis, and 3D br
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  A family of lightweight transformer-based models designed to bridge natural language and neural data interpretation.
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  Tbai models function similarly to small-scale tokenizers like **T5**, optimized for concise reasoning and domain-specific understanding.
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- - **Tbai-HF-v{x} (e.g. Tbai-HF-v1, Tbai-HF-v2)**
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  Models trained for **neuroimaging-based interpretation**.
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  Capable of generating analytical or descriptive text directly from **brain imaging data** such as MRI or CT scans.
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  Each version enhances domain adaptation, linguistic fluency, and cross-modal comprehension between visual brain patterns and textual insight.
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- - **Tbai-NAI-v{x} (e.g. Tbai-NAI-v1, Tbai-NAI-v2)**
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  Models specialized for **EEG-based cognitive commentary**.
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  Designed to interpret brainwave signals and translate them into meaningful linguistic explanations.
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  Subsequent versions expand accuracy in recognizing temporal EEG events, emotional states, and neurological anomalies.
@@ -68,8 +68,7 @@ Tbai models function similarly to small-scale tokenizers like **T5**, optimized
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  ### **Lbai Family — Large-Scale Cognitive Reasoning Models**
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  A suite of large language models (LLMs) trained on **medical, neuroscientific, and cognitive** corpora to perform advanced reasoning and diagnostic dialogue.
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- - **Lbai-{x}-{params} (e.g. Lbai-1-7B, Lbai-2-7B, Lbai-1-30B)**
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- Each model’s identifier specifies its parameter scale in billions.
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  These models emulate expert-level reasoning, trained to **think and respond like medical professionals**, with a strong focus on neuroscience, neurophysiology, and clinical linguistics.
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  Lbai models aim to achieve near-complete domain comprehension across **brain science, medicine, and cognitive AI research**.
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  A family of lightweight transformer-based models designed to bridge natural language and neural data interpretation.
56
  Tbai models function similarly to small-scale tokenizers like **T5**, optimized for concise reasoning and domain-specific understanding.
57
 
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+ - **Tbai-HF-{x} (e.g. Tbai-HF-1, Tbai-HF-2)**
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  Models trained for **neuroimaging-based interpretation**.
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  Capable of generating analytical or descriptive text directly from **brain imaging data** such as MRI or CT scans.
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  Each version enhances domain adaptation, linguistic fluency, and cross-modal comprehension between visual brain patterns and textual insight.
62
 
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+ - **Tbai-NAI-{x} (e.g. Tbai-NAI-1, Tbai-NAI-2)**
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  Models specialized for **EEG-based cognitive commentary**.
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  Designed to interpret brainwave signals and translate them into meaningful linguistic explanations.
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  Subsequent versions expand accuracy in recognizing temporal EEG events, emotional states, and neurological anomalies.
 
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  ### **Lbai Family — Large-Scale Cognitive Reasoning Models**
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  A suite of large language models (LLMs) trained on **medical, neuroscientific, and cognitive** corpora to perform advanced reasoning and diagnostic dialogue.
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+ - **Lbai-{x}-{state} (e.g. Lbai-1-preview, Lbai-2-base, Lbai-1-it)**
 
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  These models emulate expert-level reasoning, trained to **think and respond like medical professionals**, with a strong focus on neuroscience, neurophysiology, and clinical linguistics.
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  Lbai models aim to achieve near-complete domain comprehension across **brain science, medicine, and cognitive AI research**.
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