This video shows different deploying strategies with Qwen 2 using the easiest method available using 1 cost effective and 1 expensive method. Qwen 2 regardless of size can be deployed on AWS, GCP or azure.

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Timestamp:
What is Qwen 2 0:00
Steps to Take 0:42
Setup Serverless Inference 1:15
Limitation of Serverless deployment 2:45
Setup dedicated Inference server 3:28
Inference using dedicated endpoint 4:58
Inference Speed 5:56
HF Inference Langchain & LlamaIndex 6:31
Cost Estimate and Use Case 7:12

Hugginface Inference ?
Hugging Face Inference Endpoints simplify LLM deployment by turning models into production-ready APIs with minimal setup. Key benefits include autoscaling, advanced security, and optimization for high throughput. The guide provides practical steps for deployment, testing, and streaming responses.


What is Qwen 2?
The Qwen 2 model series from Alibaba Cloud represents a significant advancement in the field of large language models (LLMs). Built on the robust Transformer architecture, Qwen 2 models incorporate advanced techniques such as SwiGLU activation, attention QKV bias, and group query attention, enhancing their capability to handle diverse linguistic and contextual nuances. The Mixture of Experts (MoE) architecture in some variants of Qwen 2 further optimizes performance by activating only a subset of parameters during inference, thereby improving efficiency and reducing computational costs.

Qwen 2 models excel in various benchmarks, outperforming baseline models of similar sizes in tasks like natural language understanding, mathematical problem-solving, and coding. Notably, the Qwen-72B model surpasses the performance of LLaMA3-70B and even outperforms GPT-3.5 on several key tasks. This high level of performance is complemented by the models' scalability, with sizes ranging from 0.5B to 72B parameters, making them suitable for a wide range of applications.

One of the standout features of Qwen 2 is its multilingual support, thanks to an improved tokenizer that adapts to multiple natural languages and coding contexts. Additionally, instruction-tuned models within the Qwen 2 series are fine-tuned to follow human instructions accurately, enhancing their utility in applications requiring precise and context-aware responses.

Qwen 2 is also open-source, encouraging community collaboration and innovation. Comprehensive documentation and support provided by the Qwen team ensure that developers and researchers can effectively implement and utilize these models. With its combination of advanced technology, high performance, and community-driven development, Qwen 2 is poised to be a valuable tool for AI-driven applications across various industries.

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