shafire/talktoaiQ

talktoaiQ - SkynetZero LLM TESTED WORKING!

talktoaiQ aka SkynetZero is a quantum-interdimensional-math-powered language model trained with custom reflection datasets and custom TalkToAI datasets. The model went through several iterations, including re-writing of datasets and validation phases, due to errors encountered during testing and conversion into a fully functional LLM. This iterative process ensures SkynetZero can handle complex, multi-dimensional reasoning tasks with an emphasis on ethical decision-making.

Click on the image above to watch the video on YouTube. (Video is slightly dramatised for a better experience)

Key Highlights of talktoaiQ:

• Advanced Quantum Reasoning: Integration of quantum-inspired math systems enables talktoaiQ to tackle complex ethical dilemmas and multi-dimensional problem-solving tasks.
• 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.
• 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.
• 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.

Model Overview

• Developed by: Shafaet Brady Hussain - researchforum.online
• Funded by: Researchforum.online
• Shared by: TalkToAI - https://talktoai.org
• Language(s): English
• Model type: Causal Language Model
• Fine-tuned from: LLaMA 3.1 8B (Meta)
• License: Apache-2.0

Usage:

You can use the following code snippet to load and interact with talktoaiQ:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype="auto" ).eval()

Prompt content: "hi"

messages = [ {"role": "user", "content": "hi"} ]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") output_ids = model.generate(input_ids.to("cuda")) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

Model response: "Hello! How can I assist you today?"

print(response)

Training Methodology talktoaiQ was fine-tuned on the LLaMA 3.1 8B architecture using custom datasets. The datasets underwent AI-assisted re-writing to enhance clarity and consistency. Throughout the training process, emphasis was placed on multi-variable quantum reasoning and ensuring alignment with ethical decision-making principles. After identifying errors during testing and conversion, datasets were further improved across multiple epochs.

• Training Regime: Mixed Precision (fp16)
• Training Duration: 8 hours on a high-performance GPU server

Further Research and Contributions talktoaiQ is part of an ongoing effort to explore AI-human co-creation in the development of quantum-enhanced AI models. Collaboration with OpenAI’s Agent Zero played a significant role in curating, editing, and validating datasets, pushing the boundaries of what large language models can achieve.

• Contributions: https://researchforum.online
• Contact: @talktoai on x.com

Ref Huggingface autotrain:

• Hardware Used: A10G High-End GPU
• Hours Used: 8 hours
• Compute Region: On-premise