Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/qwen2.5-0.5b-instruct-bnb-4bit
+base_model: Qwen/Qwen2.5-0.5B-Instruct
 tags:
 - text-generation-inference
 - transformers
@@ -11,12 +11,81 @@ language:
 - en
 ---
 
-# Uploaded  model
+![Header](https://raw.githubusercontent.com/Aayan-Mishra/Images/refs/heads/main/Athena.png)
 
-- **Developed by:** Spestly
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-0.5b-instruct-bnb-4bit
+# Athena-1 0.5B:
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+Athena-1 0.5B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). Designed for ultra-lightweight applications, Athena-1 0.5B balances compactness with robust performance, making it suitable for tasks with limited computational resources.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+---
+
+## Key Features
+
+### ⚡ Ultra-Lightweight and Efficient
+
+*   **Compact Size:** With just **500 million parameters**, Athena-1 0.5B is ideal for edge devices and low-resource environments.
+*   **Instruction Following:** Fine-tuned for reliable adherence to user instructions.
+*   **Coding and Mathematics:** Capable of handling basic coding and mathematical tasks.
+
+### 📖 Contextual Understanding
+
+*   **Context Length:** Supports up to **16,384 tokens**, enabling processing of moderately sized conversations or documents.
+*   **Token Generation:** Can generate up to **4K tokens** of coherent output.
+
+### 🌍 Multilingual Support
+
+*   Supports **20+ languages**, including:
+    *   English, Chinese, French, Spanish, German, Italian, Russian
+    *   Japanese, Korean, Vietnamese, Thai, and more.
+
+### 📊 Structured Data & Outputs
+
+*   **Structured Data Interpretation:** Handles formats like tables and JSON effectively.
+*   **Structured Output Generation:** Produces well-formatted outputs for data-specific tasks.
+
+---
+
+## Model Details
+
+*   **Base Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+*   **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
+*   **Parameters:** 500M total.
+*   **Layers:** (Adjust if different from the base model)
+*   **Attention Heads:** (Adjust if different from the base model)
+*   **Context Length:** Up to **16,384 tokens**.
+
+---
+
+## Applications
+
+Athena-1 0.5B is optimized for:
+
+*   **Conversational AI:** Power lightweight and responsive chatbots.
+*   **Code Assistance:** Basic code generation, debugging, and explanations.
+*   **Mathematical Assistance:** Solves fundamental math problems.
+*   **Document Processing:** Summarizes and analyzes smaller documents effectively.
+*   **Multilingual Tasks:** Supports global use cases with a compact model.
+*   **Structured Data:** Reads and generates structured formats like JSON and tables.
+
+---
+
+## Quickstart
+
+Here’s how you can use Athena-1 0.5B for quick text generation:
+
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+messages = [
+    {"role": "user", "content": "What can you do?"},
+]
+pipe = pipeline("text-generation", model="Spestly/Athena-1-0.5B") # Update model name
+print(pipe(messages))
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
+model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
+```