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README.md
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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-
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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-
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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}-{
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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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| 58 |
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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.
|
| 61 |
Each version enhances domain adaptation, linguistic fluency, and cross-modal comprehension between visual brain patterns and textual insight.
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| 62 |
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| 63 |
+
- **Tbai-NAI-{x} (e.g. Tbai-NAI-1, Tbai-NAI-2)**
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| 64 |
Models specialized for **EEG-based cognitive commentary**.
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| 65 |
Designed to interpret brainwave signals and translate them into meaningful linguistic explanations.
|
| 66 |
Subsequent versions expand accuracy in recognizing temporal EEG events, emotional states, and neurological anomalies.
|
|
|
|
| 68 |
### **Lbai Family — Large-Scale Cognitive Reasoning Models**
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| 69 |
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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| 73 |
Lbai models aim to achieve near-complete domain comprehension across **brain science, medicine, and cognitive AI research**.
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| 74 |
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