safetensors qwen2 license:apache-2.0 region:us
---
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.

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For more details and updates, visit the [model page on Hugging Face](https://huggingface.co/Imran1/Qwen2.5-72B-Instruct-FP8).
