Update README.md

README.md

@@ -6,17 +6,82 @@ tags:
 - unsloth
 - qwen2
 - trl
-license: apache-2.0
+license: other
+license_name: qwen-research
+license_link: https://huggingface.co/Spestly/Athena-1-3B/blob/main/LICENSE
 language:
 - en
+library_name: transformers
 ---
 
-# Uploaded  model
+![Header](https://raw.githubusercontent.com/Aayan-Mishra/Images/refs/heads/main/AwA.png)
 
-- **Developed by:** Spestly
-- **License:** apache-2.0
-- **Finetuned from model :** Spestly/Athena-2-0.5B
+# AwA - 0.5B
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+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.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## 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
+
+```python
+# 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