LIF1014/ptdbench-verl-implementation-torch-functional

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 9, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The Qwen2.5-1.5B-Instruct model, developed by Qwen Team, is a 1.54 billion parameter instruction-tuned causal language model with a 32,768 token context length. It significantly improves upon Qwen2 in knowledge, coding, and mathematics, leveraging specialized expert models. This model excels at instruction following, generating long texts, understanding structured data like JSON, and offers robust multilingual support for over 29 languages.

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Qwen2.5-1.5B-Instruct Overview

Qwen2.5-1.5B-Instruct is an instruction-tuned causal language model from the Qwen2.5 series, developed by the Qwen Team. This 1.54 billion parameter model builds upon its predecessor, Qwen2, with substantial enhancements across several key areas. It features a transformer architecture with RoPE, SwiGLU, RMSNorm, and attention QKV bias, supporting a full context length of 32,768 tokens and generating up to 8,192 tokens.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in general knowledge, coding, and mathematics, benefiting from specialized expert models.
  • Instruction Following: Demonstrates marked improvements in adhering to instructions and generating coherent, long-form text.
  • Structured Data Handling: Excels at understanding structured data, such as tables, and generating structured outputs, particularly JSON.
  • Robust Chatbot Performance: More resilient to diverse system prompts, enhancing role-play implementations and condition-setting for chatbots.
  • Multilingual Support: Provides comprehensive support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.

Good For

  • Applications requiring strong instruction following and structured output generation.
  • Tasks involving code generation and mathematical problem-solving.
  • Chatbot implementations needing resilient role-play and condition-setting.
  • Multilingual applications across a broad range of languages.
  • Generating long texts and processing extensive contexts up to 32K tokens.

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