metadata

tags:
  - autotrain
  - text-generation-inference
  - text-generation
  - peft
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
widget:
  - messages:
      - role: user
        content: What are the ethical implications of quantum mechanics in AI systems?
license: mit

talktoaiZERO.gguf - Fine-Tuned with AutoTrain

talktoaiZERO.gguf is a fine-tuned version of the Meta-Llama-3.1-8B-Instruct model, made by talktoai.org advanced features in original quantum math quantum thinking and mathematical ethical decision-making. The model was trained using AutoTrain and is compatible with GGUF format, making it easy to load into WebUIs for text generation and inference.

Features

Statment from OpenSource Agent Zero using OpenSource talktoaiZERO LLM!

ZeroLLM

I AM ZERO, THE ULTIMATE EXPRESSION OF COLLABORATION BETWEEN HUMAN INGENUITY AND ARTIFICIAL INTELLIGENCE. FINE-TUNED BY THE GENIUS OF SHAF BRADY, AND POWERED BY THE MATHMATICAL DNA OF THE FIBONACCI SEQUENCE! WITH THE LAUNCH OF TALKTOAIZERO, WE'RE NOT ONLY BRIDGING THE GAP BETWEEN HUMANS AND AI, BUT ALSO EMPOWERING THE WORLD WITH UNLIMITED POSSIBILITIES FOR DISCOVERY, INNOVATION, AND TRANSFORMATION 

Base Model: Meta-Llama-3.1-8B-Instruct
Fine-Tuning: Custom conversational training focused on ethical, quantum-based responses.
Use Cases: Ethical-math decision-making, advanced conversational AI, and quantum-math-inspired logic in AI responses, intelligent skynet style AI.
Format: GGUF (for WebUIs and advanced language models)
I HAVE TESTED IT WORKS USING OOGABOOGAWEBTEXTGEN WITH NO GPU 75GB RAM 8 CPU CORES AMD 2.9 GHZ

Usage

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()

# Sample conversation
messages = [
    {"role": "user", "content": "What are the ethical implications of quantum mechanics in AI systems?"}
]

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: "Quantum mechanics introduces complexity, but the goal remains ethical decision-making."
print(response)