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, specifically designed for conversational AI with 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

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)