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README.md

@@ -18,71 +18,6 @@ library_name: transformers
 This model was converted to GGUF format from [`Spestly/AwA-0.5B`](https://huggingface.co/Spestly/AwA-0.5B) 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-0.5B) 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: [540m]
-
-    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-0.5B")
-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)