nano1243/Qwen2-1.5B-Instruct

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Qwen2-1.5B-Instruct is a 1.5 billion parameter instruction-tuned causal language model from the Qwen2 series, developed by Qwen. Built on a Transformer architecture with SwiGLU activation and group query attention, it features an improved tokenizer for multiple natural languages and code. This model demonstrates strong performance across benchmarks for language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning, making it suitable for a wide range of general-purpose conversational AI applications.

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Qwen2-1.5B-Instruct: A Compact Yet Powerful LLM

Qwen2-1.5B-Instruct is an instruction-tuned model from the new Qwen2 series, developed by Qwen. This 1.5 billion parameter model is part of a family ranging from 0.5B to 72B parameters, including a Mixture-of-Experts variant. It is designed with a Transformer architecture, incorporating features like SwiGLU activation, attention QKV bias, and group query attention, alongside an enhanced tokenizer for diverse natural languages and code.

Key Capabilities & Performance

Qwen2-1.5B-Instruct shows significant improvements over its predecessor, Qwen1.5, and other open-source models across various benchmarks. It excels in:

  • Language Understanding & Generation: Demonstrates strong performance in general language tasks.
  • Multilingual Support: Features an improved tokenizer adaptive to multiple natural languages.
  • Coding & Mathematics: Achieves notable scores in HumanEval (37.8) and GSM8K (61.6), indicating proficiency in code generation and mathematical reasoning.
  • Reasoning: Performs well in complex reasoning tasks, as evidenced by its C-Eval score of 63.8 and MMLU score of 52.4.

Training & Optimization

The model was pretrained on a vast dataset and further optimized through supervised finetuning and direct preference optimization. This training methodology contributes to its competitive performance against both open-source and proprietary models.

Ideal Use Cases

This model is well-suited for applications requiring a capable yet efficient language model, particularly for:

  • General-purpose conversational AI and chatbots.
  • Code generation and assistance in programming tasks.
  • Educational tools requiring mathematical problem-solving.
  • Multilingual content generation and understanding.

For more details, refer to the Qwen2 blog and GitHub repository.

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