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----
-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 challenges do you enjoy solving?
-license: apache-2.0
----
-
-**SpectraMind Quantum LLM** **GGUF-Compatible and Fully Optimized**
-
-![SpectraMind](https://huggingface.co/shafire/SpectraMind/resolve/main/spectramind.png)
-
-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.
-
-![SpectraMind Performance](https://huggingface.co/shafire/SpectraMind/resolve/main/performance_chart.png)
-
-<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**: LLaMA 3.1 8B (Meta)
-- **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:
-![SpectraMind Agent Interface](https://huggingface.co/shafire/SpectraMind/resolve/main/interface_screenshot.png)
-
----
-
-### 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