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license: apache-2.0
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**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.
<a href="https://www.youtube.com/watch?v=xyz123">Watch Our Model in Action</a>
**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](https://researchforum.online)
- **Funded by**: [Researchforum.online](https://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:
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### Usage Code Example:
You can load and interact with SpectraMind using the following code snippet:
```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()
# 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
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