Update README.md

README.md

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-license: apache-2.0
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+
+---
+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: [email protected]
+
+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