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license: apache-2.0 |
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# Imran1/Qwen2.5-72B-Instruct-FP8 |
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## Overview |
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**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. |
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This model is well-suited for applications such as: |
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- Conversational AI and chatbots |
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- Instruction-based tasks |
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- Text generation, summarization, and dialogue completion |
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## Key Features |
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- **72 billion parameters** for powerful language generation and understanding capabilities. |
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- **FP8 precision** for reduced memory consumption and faster inference. |
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- Supports **tensor parallelism** for distributed computing environments. |
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## Usage Instructions |
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### 1. Running the Model with vLLM |
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You can serve the model using **vLLM** with tensor parallelism enabled. Below is an example command for running the model: |
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```bash |
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vllm serve Imran1/Qwen2.5-72B-Instruct-FP8 --api-key token-abc123 --tensor-parallel-size 2 |
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``` |
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### 2. Interacting with the Model via Python (OpenAI API) |
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Here’s an example of how to interact with the model using the OpenAI API interface: |
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```python |
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from openai import OpenAI |
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client = OpenAI( |
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base_url="http://localhost:8000/v1", # Your vLLM server URL |
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api_key="token-abc123", # Replace with your API key |
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) |
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# Example chat completion request |
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completion = client.chat.completions.create( |
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model="Imran1/Qwen2.5-72B-Instruct-FP8", |
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messages=[ |
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{"role": "user", "content": "Hello!"}, |
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], |
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max_tokens=500, |
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stream=True |
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) |
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print(completion) |
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``` |
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## Performance and Efficiency |
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- **Memory Efficiency**: FP8 precision significantly reduces memory requirements, allowing for larger batch sizes and faster processing times. |
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- **Speed**: The FP8 version provides faster inference, making it highly suitable for real-time applications. |
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## Limitations |
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- **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. |
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## License |
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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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--- |
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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). |
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