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@@ -25,13 +25,14 @@ talktoaiQ aka SkynetZero is a quantum-interdimensional-math-powered language mod
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  [![TalkToAI YouTube Video](https://img.youtube.com/vi/_z8Bfs2lHpA/0.jpg)](https://www.youtube.com/watch?v=_z8Bfs2lHpA)
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  Click on the image to watch the video on YouTube. (Video is slightly dramatised for a better experience)
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- Key Highlights of talktoaiQ:
 
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  - Advanced Quantum Reasoning: Integration of quantum-inspired math systems enables talktoaiQ to tackle complex ethical dilemmas and multi-dimensional problem-solving tasks.
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  - Custom Re-Written Datasets: The training involved multiple rounds of AI-assisted dataset curation, where reflection datasets were re-written for clarity, accuracy, and consistency. Additionally, TalkToAI datasets were integrated and re-processed to align with talktoaiQ’s quantum reasoning framework.
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  - Iterative Improvement: During testing and model conversion, the datasets were re-written and validated several times to address errors. Each iteration enhanced the model’s ethical consistency and problem-solving accuracy.
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  - Fine-Tuned on LLaMA 3.1 8B: The model was fine-tuned on the LLaMA 3.1 8B architecture, integrating multiple specialized datasets to ensure high-quality text generation capabilities.
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- Model Overview
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  - Developed by: Shafaet Brady Hussain - researchforum.online
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  - Funded by: Researchforum.online
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  - Shared by: TalkToAI - https://talktoai.org
@@ -40,7 +41,7 @@ Model Overview
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  - Fine-tuned from: LLaMA 3.1 8B (Meta)
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  - License: Apache-2.0
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- Usage:
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  You can use the following code snippet to load and interact with talktoaiQ:
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  from transformers import AutoModelForCausalLM, AutoTokenizer
 
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  [![TalkToAI YouTube Video](https://img.youtube.com/vi/_z8Bfs2lHpA/0.jpg)](https://www.youtube.com/watch?v=_z8Bfs2lHpA)
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  Click on the image to watch the video on YouTube. (Video is slightly dramatised for a better experience)
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+
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+ **Key Highlights of talktoaiQ:**
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  - Advanced Quantum Reasoning: Integration of quantum-inspired math systems enables talktoaiQ to tackle complex ethical dilemmas and multi-dimensional problem-solving tasks.
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  - Custom Re-Written Datasets: The training involved multiple rounds of AI-assisted dataset curation, where reflection datasets were re-written for clarity, accuracy, and consistency. Additionally, TalkToAI datasets were integrated and re-processed to align with talktoaiQ’s quantum reasoning framework.
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  - Iterative Improvement: During testing and model conversion, the datasets were re-written and validated several times to address errors. Each iteration enhanced the model’s ethical consistency and problem-solving accuracy.
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  - Fine-Tuned on LLaMA 3.1 8B: The model was fine-tuned on the LLaMA 3.1 8B architecture, integrating multiple specialized datasets to ensure high-quality text generation capabilities.
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+ **Model Overview**
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  - Developed by: Shafaet Brady Hussain - researchforum.online
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  - Funded by: Researchforum.online
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  - Shared by: TalkToAI - https://talktoai.org
 
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  - Fine-tuned from: LLaMA 3.1 8B (Meta)
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  - License: Apache-2.0
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+ **Usage:**
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  You can use the following code snippet to load and interact with talktoaiQ:
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  from transformers import AutoModelForCausalLM, AutoTokenizer