jdad334/Qwen2-7B-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 2, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Qwen2-7B-Instruct is a 7.6 billion parameter instruction-tuned causal language model from the Qwen2 series, developed by Qwen. It is built on a Transformer architecture with SwiGLU activation and group query attention, supporting a context length of up to 131,072 tokens through YARN. This model demonstrates strong performance across language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning benchmarks, making it suitable for a wide range of general-purpose AI applications.

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Qwen2-7B-Instruct: A Powerful General-Purpose LLM

Qwen2-7B-Instruct is a 7.6 billion parameter instruction-tuned model from the Qwen2 series, designed for broad applicability. It leverages a Transformer architecture with SwiGLU activation and group query attention, and features an improved tokenizer for multiple natural languages and code. The model was pretrained on a large dataset and further refined with supervised finetuning and direct preference optimization.

Key Capabilities & Features

  • Extended Context Window: Supports an impressive context length of up to 131,072 tokens, utilizing YARN for efficient long-text processing.
  • Strong Benchmark Performance: Outperforms many open-source models and competes with proprietary alternatives across diverse benchmarks, including MMLU, GPQA, Humaneval, GSM8K, and C-Eval.
  • Multilingual Proficiency: Demonstrates robust capabilities in language understanding and generation across various languages.
  • Coding & Mathematics: Shows particular strength in coding tasks (e.g., Humaneval, MultiPL-E) and mathematics (e.g., GSM8K, MATH).

Good For

  • General Instruction Following: Excels at understanding and executing a wide array of user instructions.
  • Long Context Applications: Ideal for tasks requiring the processing and generation of very long texts, such as document analysis or extended conversations.
  • Coding Assistance: Suitable for code generation, completion, and debugging tasks.
  • Multilingual AI Systems: Can be effectively used in applications requiring support for multiple languages.

Finetunes
1 models