shafire/SpectraMindQ · Hugging Face

SpectraMind Quantum LLM GGUF-Compatible and Fully Optimized

SpectraMind is an advanced, multi-layered language model based on the Zephyr 7B architecture, built with quantum-inspired data processing techniques. Trained on custom datasets with unique quantum reasoning enhancements, SpectraMind integrates ethical decision-making frameworks with deep problem-solving capabilities, handling complex, multi-dimensional tasks with precision.

Watch Our Model in Action

Use Cases:
This model is ideal for advanced NLP tasks, including ethical decision-making, multi-variable reasoning, and comprehensive problem-solving in quantum and mathematical contexts.

Key Highlights of SpectraMind:

Quantum-Enhanced Reasoning: Designed for tackling complex ethical questions and multi-layered logic problems, SpectraMind applies quantum-math techniques in AI for nuanced solutions.
Refined Dataset Curation: Data was refined over multiple iterations, focusing on clarity and consistency, to align with SpectraMind's quantum-based reasoning.
Iterative Training: The model underwent extensive testing phases to ensure accurate and reliable responses.
Optimized for CPU Inference: Compatible with web UIs and desktop interfaces like oobabooga and lm studio, and performs well in self-hosted environments for CPU-only setups.

Model Overview

Developer: Shafaet Brady Hussain - ResearchForum
Funded by: Researchforum.online
Language: English
Model Type: Causal Language Model
Base Model: Zephyr 7B Beta (HuggingFaceH4)
License: Apache-2.0

Usage: Run on any web interface or as a bot for self-hosted solutions. Designed to run smoothly on CPU.

Tested on CPU - Ideal for Local and Self-Hosted Environments

AGENT INTERFACE DETAILS:

Usage Code Example:

You can load and interact with SpectraMind using the following code snippet:

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()

# Example prompt
messages = [
    {"role": "user", "content": "What challenges do you enjoy solving?"}
]

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)

print(response)  # Prints the model's response