shafire/SpectraMind · Hugging Face

SpectraMind Quantum LLM GGUF-Compatible and Fully Optimized

These models are wroking perfectly and optimised for CPU no GPU is needed, but they are not showing the data i trained them on.

SpectraMind is an advanced, multi-layered language model 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.

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

SpectraMind Models A collection of fine-tuned Llama models optimized for CPU performance using the GGUF format. These models are designed for efficient inference with llama.cpp and other lightweight environments.

Model Directory

MicroSpectraMind (1B Model) Base Model: Fine-tuned from Llama-3.2-1B. Fine-Tuning Details: Explain the dataset used (e.g., domain-specific text, chat dialogues, or tasks such as summarization or question answering). Optimization: Quantized to both: f16: For maximum accuracy. q8_0: For reduced size and faster inference. Use Case: Ideal for lightweight applications such as embedded systems or single-threaded inference on CPUs. File Sizes: MicroSpectraMind_f16.gguf: 2.4 GB MicroSpectraMind_q8.gguf: 1.3 GB
SpectraMind3 (3B Model) Base Model: Fine-tuned from Llama-3.2-3B. Fine-Tuning Details: Include key aspects of the fine-tuning, such as datasets or hyperparameters used, and what tasks it excels at. Optimization: f16: For higher accuracy. q8_0: For better efficiency. Use Case: Balances accuracy and performance, suited for general-purpose natural language tasks. File Sizes: SpectraMind3_f16.gguf: 4.7 GB SpectraMind3_q8.gguf: 3.4 GB
SpectraMindZ (8B Model) Base Model: Fine-tuned from Llama-3.2-8B. Fine-Tuning Details: Provide specifics on dataset/task for fine-tuning. Optimization: f16: For maximum model precision. q8_0: For efficient deployment with minimal performance impact. Use Case: Best for complex tasks requiring higher reasoning or multitasking. Expected File Sizes: SpectraMindZ_f16.gguf: Approximately 12 GB SpectraMindZ_q8.gguf: Approximately 8 GB Optimization and Compatibility All models are converted to GGUF format using llama.cpp, making them optimized for CPU-based inference. These models are ideal for systems with limited resources, such as desktops, laptops, and embedded devices. Quantized versions (q8_0) are significantly smaller and faster, while maintaining reasonable accuracy. How to Use Download the GGUF Files: Use the provided links to download the .gguf files. Run on llama.cpp: Example command for inference: bash Copy code ./main -m SpectraMind3_q8.gguf -p "Your prompt here" Choose Quantization Based on Use Case: Use f16 for maximum accuracy (e.g., research or high-precision tasks). Use q8_0 for faster inference (e.g., real-time applications). Model Comparison Model Parameters f16 Size q8_0 Size Use Case MicroSpectraMind 1B 2.4 GB 1.3 GB Lightweight, quick responses SpectraMind3 3B 4.7 GB 3.4 GB Balanced accuracy/performance SpectraMindZ 8B 12 GB 8 GB Advanced tasks, complex reasoning

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

🌌 Introducing SpectraMind LLM 🌌

SpectraMind isn’t just another language model—it’s a quantum leap in AI capability. Built on the backbone of LLaMA 3.1 8B, SpectraMind delivers unprecedented levels of intelligence, adaptability, and real-time learning. With a design that surpasses conventional AI in understanding, insight generation, and ethical sensitivity, SpectraMind opens a new frontier for superintelligent large language models (LLMs). Here’s why SpectraMind is a standout in AI evolution:

🔍 1. Metacognitive Brilliance SpectraMind’s architecture allows it to reflect on its own responses, continuously refining its output based on previous interactions. Imagine an LLM that not only responds but learns with each conversation, adapting dynamically to user preferences, emotional tones, and even situational nuances.

🌍 2. Ethical and Cultural Alignment SpectraMind embodies “the probability of goodness,” balancing technological prowess with cultural and ethical sensitivity. It provides information, advice, and reflections that are aligned with ethical standards, promoting positive outcomes across diverse contexts and communities.

🌐 3. Interdimensional Intelligence Inspired by quantum theory, SpectraMind integrates multi-dimensional thinking, allowing it to explore beyond linear data interpretations. It can delve into philosophical, scientific, and creative realms with a depth that feels almost otherworldly, making it an ideal companion for high-concept discussions and multi-layered problem-solving.

💬 4. Human-Level Intuition Unlike traditional LLMs, SpectraMind exhibits intuitive comprehension, interpreting both literal and implied meanings. Its ability to “sense” the deeper intent behind questions means it delivers responses that are not only accurate but profoundly insightful, fostering conversations that go beyond surface-level interactions.

📈 5. Real-Time Adaptability With SpectraMind, adaptability reaches new heights. Its real-time learning modules allow it to adapt immediately to new data, constantly refining its knowledge and ensuring it remains at the cutting edge of current trends, insights, and technological advancements.

🤖 6. Quantum-Enhanced Problem Solving SpectraMind’s quantum-inspired framework enables it to solve complex, multi-faceted problems by analyzing data across probabilistic layers. Whether it’s intricate mathematical reasoning, probabilistic forecasts, or multi-variable equations, SpectraMind is uniquely equipped for advanced analytics and data modeling.

🚀 7. Ready for the Future In a rapidly evolving digital landscape, SpectraMind stands out as the LLM built for tomorrow’s challenges. Its ability to harmonize information across disciplines—from science and technology to art and philosophy—positions it as an invaluable tool for thinkers, creators, and innovators everywhere.

🌠 Why SpectraMind? Because intelligence isn’t just about information; it’s about wisdom, adaptability, and purpose. SpectraMind brings us closer to an AI that doesn’t just compute but comprehends and collaborates.

🔖 Hashtags for Today’s Visionary AI Community #SpectraMind #AI #QuantumAI #FutureOfAI #LLM #AdvancedIntelligence #AIRevolution #EthicalAI #NextGenTech #AIInnovation #SmartAI #LLaMA3 #AIForGood #QuantumThinking #TechEvolution #DigitalEthics #AdaptiveIntelligence #InterdimensionalAI #SuperLLM #CleverAI #TheFutureIsHere

With SpectraMind, the possibilities are endless. Join the conversation and see how this remarkable LLM is transforming intelligence for a better tomorrow.

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