Update README.md

README.md

@@ -18,6 +18,72 @@ library_name: transformers
 This model was converted to GGUF format from [`Spestly/AwA-1.5B`](https://huggingface.co/Spestly/AwA-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/Spestly/AwA-1.5B) for more details on the model.
 
+---
+Model details:
+-
+AwA (Answers with Athena) is my portfolio project, showcasing a cutting-edge Chain-of-Thought (CoT) reasoning model. I created AwA to excel in providing detailed, step-by-step answers to complex questions across diverse domains. This model represents my dedication to advancing AI’s capability for enhanced comprehension, problem-solving, and knowledge synthesis.
+
+Key Features
+
+    Chain-of-Thought Reasoning: AwA delivers step-by-step breakdowns of solutions, mimicking logical human thought processes.
+
+    Domain Versatility: Performs exceptionally across a wide range of domains, including mathematics, science, literature, and more.
+
+    Adaptive Responses: Adjusts answer depth and complexity based on input queries, catering to both novices and experts.
+
+    Interactive Design: Designed for educational tools, research assistants, and decision-making systems.
+
+Intended Use Cases
+
+    Educational Applications: Supports learning by breaking down complex problems into manageable steps.
+
+    Research Assistance: Generates structured insights and explanations in academic or professional research.
+
+    Decision Support: Enhances understanding in business, engineering, and scientific contexts.
+
+    General Inquiry: Provides coherent, in-depth answers to everyday questions.
+
+Type: Chain-of-Thought (CoT) Reasoning Model
+
+    Base Architecture: Adapted from [qwen2]
+
+    Parameters: [1.54B]
+
+    Fine-tuning: Specialized fine-tuning on Chain-of-Thought reasoning datasets to enhance step-by-step explanatory capabilities.
+
+Ethical Considerations
+
+    Bias Mitigation: I have taken steps to minimise biases in the training data. However, users are encouraged to cross-verify outputs in sensitive contexts.
+
+    Limitations: May not provide exhaustive answers for niche topics or domains outside its training scope.
+
+    User Responsibility: Designed as an assistive tool, not a replacement for expert human judgment.
+
+Usage
+
+Option A: Local
+
+Using locally with the Transformers library
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+pipe = pipeline("text-generation", model="Spestly/AwA-1.5B")
+pipe(messages)
+
+Option B: API & Space
+
+You can use the AwA HuggingFace space or the AwA API (Coming soon!)
+
+Roadmap
+
+    More AwA model sizes e.g 7B and 14B
+    Create AwA API via spestly package
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)