Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/qwen2.5-1.5b-instruct-bnb-4bit
+base_model: Qwen/Qwen2.5-1.5B-Instruct
 tags:
 - text-generation-inference
 - transformers
@@ -11,12 +11,82 @@ 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/qwen2.5-1.5b-instruct-bnb-4bit
+# Athena-1 1.5B:
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+Athena-1 1.5B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). Designed for efficiency and high-quality text generation, Athena-1 1.5B maintains a compact size, making it ideal for real-world applications where performance and resource efficiency are critical, such as lightweight applications, conversational AI, and structured data tasks.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+---
+
+## Key Features
+
+### ⚡ Lightweight and Efficient
+
+*   **Compact Size:** At just **1.5 billion parameters**, Athena-1 1.5B offers excellent performance with reduced computational requirements.
+*   **Instruction Following:** Fine-tuned for precise and reliable adherence to user prompts.
+*   **Coding and Mathematics:** Proficient in solving coding challenges and handling mathematical tasks.
+
+### 📖 Long-Context Understanding
+
+*   **Context Length:** Supports up to **32,768 tokens**, enabling the processing of moderately lengthy documents or conversations.
+*   **Token Generation:** Can generate up to **8K tokens** of output.
+
+### 🌍 Multilingual Support
+
+*   Supports **29+ languages**, including:
+    *   English, Chinese, French, Spanish, Portuguese, German, Italian, Russian
+    *   Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+### 📊 Structured Data & Outputs
+
+*   **Structured Data Interpretation:** Processes structured formats like tables and JSON.
+*   **Structured Output Generation:** Generates well-formatted outputs, including JSON and other structured formats.
+
+---
+
+## Model Details
+
+*   **Base Model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+*   **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
+*   **Parameters:** 1.5B total (Adjust non-embedding count if you have it).
+*   **Layers:** (Adjust if different from the 3B model)
+*   **Attention Heads:** (Adjust if different from the 3B model)
+*   **Context Length:** Up to **32,768 tokens**.
+
+---
+
+
+## Applications
+
+Athena 1.5B is designed for a variety of real-world applications:
+
+*   **Conversational AI:** Build fast, responsive, and lightweight chatbots.
+*   **Code Generation:** Generate, debug, or explain code snippets.
+*   **Mathematical Problem Solving:** Assist with calculations and reasoning.
+*   **Document Processing:** Summarize and analyze moderately large documents.
+*   **Multilingual Applications:** Support for global use cases with diverse language requirements.
+*   **Structured Data:** Process and generate structured data, such as tables and JSON.
+
+---
+
+## Quickstart
+
+Here’s how you can use Athena 1.5B for quick text generation:
+
+```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/Athena-1-1.5B") # Update model name
+print(pipe(messages))
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-1.5B") # Update model name
+model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-1.5B") # Update model name
+```