README.md

---
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: other
---

# SkynetZero LLM - Trained with AutoTrain and Updated to GGUF Format

**SkynetZero** is a quantum-powered language model trained with reflection datasets and TalkToAI custom data sets. The model went through several iterations, including a re-writing of datasets and validation phases due to errors encountered during testing and conversion into a fully functional LLM. This process helped ensure that SkynetZero can handle complex, multi-dimensional reasoning tasks with an emphasis on ethical decision-making.

### Key Highlights of SkynetZero:
- **Advanced Quantum Reasoning**: The integration of quantum-inspired math systems enabled SkynetZero 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 SkynetZero’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.

SkynetZero is now available in **GGUF format**, following 8 hours of training on a large GPU server using the Hugging Face AutoTrain platform.

# Usage - SkynetZero leverages open-source ideas and mathematical innovations. Further details can be found on [talktoai.org](https://talktoai.org) and [researchforum.online](https://researchforum.online). The model is licensed under the official legal guidelines for LLaMA 3.1 Meta.

```python
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
SkynetZero was fine-tuned on the **LLaMA 3.1 8B** architecture, utilizing custom datasets that underwent AI-assisted re-writing. The training process focused on enhancing the model's ability to handle **multi-variable quantum reasoning** while ensuring ethical decision-making alignment. After identifying errors during testing and conversion to a model, the datasets were adjusted and the model iteratively improved across multiple epochs.

### Further Research and Contributions
SkynetZero is part of an ongoing effort to explore **AI-human co-creation** in the development of quantum-enhanced AI models. The co-creation process with OpenAI’s **Agent Zero** provided valuable assistance in curating, editing, and validating datasets, pushing the boundaries of what large language models can achieve.