---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-8B
widget:
  - messages:
      - role: user
        content: What is your favorite condiment?
license: apache-2.0
---

talktoaiQ - SkynetZero LLM

talktoaiQ aka SkynetZero is a quantum-powered language model trained with 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.

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: info@talktoai.org

Carbon Emissions & Environmental Impact:
- Hardware Used: AMD EPYC CPU and High-End GPU
- Hours Used: 8 hours
- Compute Region: On-premise
- Carbon Emissions: Estimated 20 kg CO2