schwyzquants/Qwen2.5-32B

TEXT GENERATIONPricing:Input $2.72 / Output $4.8Concurrent Unit Cost:2Model Size:32.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 14, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

schwyzquants/Qwen2.5-32B is a 32.5 billion parameter causal language model from the Qwen2.5 series, developed by the Qwen Team. This base model features a transformer architecture with RoPE, SwiGLU, and RMSNorm, supporting a context length of 131,072 tokens. It offers significantly improved capabilities in coding, mathematics, instruction following, and generating long texts, with multilingual support for over 29 languages. It is designed for further post-training like SFT or RLHF rather than direct conversational use.

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Qwen2.5-32B: A Powerful Base Language Model

Qwen2.5-32B is a 32.5 billion parameter base causal language model, part of the latest Qwen2.5 series developed by the Qwen Team. This model builds upon Qwen2 with substantial enhancements across several key areas, making it a robust foundation for various NLP applications.

Key Capabilities & Improvements

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates marked improvements in adhering to instructions and handling diverse system prompts, beneficial for role-play and chatbot implementations.
  • Long-Context & Generation: Supports an extensive context length of up to 131,072 tokens and can generate long texts, up to 8,000 tokens.
  • Structured Data Handling: Better at understanding structured data like tables and generating structured outputs, particularly JSON.
  • Multilingual Support: Offers broad multilingual capabilities, supporting over 29 languages including Chinese, English, French, Spanish, German, Japanese, and Korean.
  • Architecture: Utilizes a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias.

When to Use This Model

This base model is ideal for developers and researchers looking to perform further post-training. It is specifically designed for applications such as:

  • Fine-tuning (SFT): Adapting the model for specific tasks or datasets.
  • Reinforcement Learning with Human Feedback (RLHF): Aligning the model's behavior with human preferences.
  • Continued Pretraining: Extending the model's knowledge on new or specialized data.

Note: This base model is not recommended for direct conversational use without further fine-tuning.

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