Update README.md

README.md

@@ -1,3 +1,66 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+
+# Imran1/Qwen2.5-72B-Instruct-FP8
+
+## Overview
+**Imran1/Qwen2.5-72B-Instruct-FP8** is an optimized version of the base model **Qwen2.5-72B-Instruct**, utilizing **FP8** (8-bit floating point) precision. This reduces memory usage and increases computational efficiency, making it ideal for large-scale inference tasks without sacrificing the model's performance.
+
+This model is well-suited for applications such as:
+- Conversational AI and chatbots
+- Instruction-based tasks
+- Text generation, summarization, and dialogue completion
+
+## Key Features
+- **72 billion parameters** for powerful language generation and understanding capabilities.
+- **FP8 precision** for reduced memory consumption and faster inference.
+- Supports **tensor parallelism** for distributed computing environments.
+
+## Usage Instructions
+
+### 1. Running the Model with vLLM
+You can serve the model using **vLLM** with tensor parallelism enabled. Below is an example command for running the model:
+
+```bash
+vllm serve Imran1/Qwen2.5-72B-Instruct-FP8 --api-key token-abc123 --tensor-parallel-size 2
+```
+
+### 2. Interacting with the Model via Python (OpenAI API)
+Here’s an example of how to interact with the model using the OpenAI API interface:
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",  # Your vLLM server URL
+    api_key="token-abc123",  # Replace with your API key
+)
+
+# Example chat completion request
+completion = client.chat.completions.create(
+    model="Imran1/Qwen2.5-72B-Instruct-FP8",
+    messages=[
+        {"role": "user", "content": "Hello!"},
+    ],
+    max_tokens=500,
+    stream=True
+)
+
+print(completion)
+```
+
+## Performance and Efficiency
+- **Memory Efficiency**: FP8 precision significantly reduces memory requirements, allowing for larger batch sizes and faster processing times.
+- **Speed**: The FP8 version provides faster inference, making it highly suitable for real-time applications.
+
+## Limitations
+- **Precision Trade-offs**: While FP8 enhances speed and memory usage, tasks that require high precision (e.g., numerical calculations) may see a slight performance degradation compared to FP16/FP32 versions.
+
+## License
+This model is licensed under the [Apache-2.0](LICENSE) license. Feel free to use this model for both commercial and non-commercial purposes, ensuring compliance with the license terms.
+
+---
+
+For more details and updates, visit the [model page on Hugging Face](https://huggingface.co/Imran1/Qwen2.5-72B-Instruct-FP8).