Qwen2-72B-Instruct

Qwen2-72B is the flagship model of the series, featuring 72 billion parameters. Its architecture includes 80 layers with a hidden size of 8192 and implements the Grouped Query Attention mechanism with 64 query heads and 8 shared key-value heads. Combined with Dual Chunk Attention and YARN technologies, this design ensures maximum performance in processing long contexts and efficient management of KV-cache memory.

The model was trained on a high-quality dataset of 7 trillion tokens, offering maximum data diversity. The base version of the model achieves outstanding results on key benchmarks: 84.2 on MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH. The instruction-tuned version, Qwen2-72B-Instruct, scores 9.1 on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench, placing it among the top-tier proprietary models.

Qwen2-72B demonstrates exceptional capabilities in complex reasoning, step-by-step problem solving, advanced programming, and deep contextual understanding. Its multilingual support enables professional-level performance in more than 30 languages, including Russian. Accordingly, it is designed for the most demanding AI use cases—high-level scientific research, complex software development, creation of high-quality professional content, advanced data analysis, automation of complex business processes, and intelligent decision-making systems.

Announce Date: 24.07.2024
Parameters: 72B
Context: 33K
Layers: 80
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.40.1
License: Tongyi-Qianwen

Use our pre-built public endpoints for free to test inference and explore Qwen2-72B-Instruct capabilities. You can obtain an API access token on the token management page after registration and verification.

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

• achieve maximum endpoint performance,
• access the full context window for processing large requests and conversations,
• ensure maximum security by processing data in a dedicated, isolated environment.

Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.