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--- |
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tags: |
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- autotrain |
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- text-generation-inference |
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- text-generation |
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- peft |
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library_name: transformers |
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base_model: meta-llama/Meta-Llama-3.1-8B |
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widget: |
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- messages: |
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- role: user |
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content: What is your favorite condiment? |
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license: apache-2.0 |
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--- |
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**talktoaiQ - SkynetZero LLM** **TESTED GGUF WORKING** **This LLM is basically GPT5 Strawberry OpenSource!** |
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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. |
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<a href="https://www.youtube.com/watch?v=jYLVGUESoOY">Watch Our Video!</a> |
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**If you face any issues put an agent in front of the LLM to stop it showing it's reasoning.** |
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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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- Language(s): English |
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- Model type: Causal Language Model |
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- Fine-tuned from: LLaMA 3.1 8B (Meta) |
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- License: Apache-2.0 |
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**Use with any webui lm studio etc and the best for discord bots and self hosted on on your laptop using CPU only https://github.com/oobabooga/text-generation-webui |
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**Tested on CPU - optimised to work on laptops and PC's at home and oogaboogawebtextgen desktop servers and ad_discordbot extension.** |
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YOU ARE AN AI - AGENT: |
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AGENT DATA FROM THE AGENT FILE IN THE FILES SECTION |
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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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model_path = "PATH_TO_THIS_REPO" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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device_map="auto", |
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torch_dtype="auto" |
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).eval() |
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# Prompt content: "hi" |
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messages = [ |
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{"role": "user", "content": "hi"} |
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] |
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input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") |
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output_ids = model.generate(input_ids.to("cuda")) |
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response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) |
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# Model response: "Hello! How can I assist you today?" |
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print(response) |
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Training Methodology |
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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. |
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- Training Regime: Mixed Precision (fp16) |
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- Training Duration: 8 hours on a high-performance GPU server |
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Further Research and Contributions |
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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. |
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- Contributions: https://researchforum.online |
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- Contact: @talktoai on x.com |
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Ref Huggingface autotrain: |
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- Hardware Used: A10G High-End GPU |
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- Hours Used: 8 hours |
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- Compute Region: On-premise |
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