ritaberrada/iolai-qwen25-baseline

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The ritaberrada/iolai-qwen25-baseline is an instruction-tuned 1.54 billion parameter causal language model from the Qwen2.5 series, developed by Qwen. It features a 32,768 token context length and is built on a transformer architecture with RoPE, SwiGLU, and RMSNorm. This model significantly improves capabilities in coding, mathematics, instruction following, and generating structured outputs like JSON, making it suitable for diverse chatbot and text generation tasks.

Loading preview...

Model Overview

This repository hosts the ritaberrada/iolai-qwen25-baseline model, an instruction-tuned variant of the Qwen2.5 series developed by Qwen. It is a 1.54 billion parameter causal language model with a transformer architecture, supporting a substantial context length of 32,768 tokens and generation up to 8,192 tokens. The Qwen2.5 series, including this model, represents an advancement over Qwen2, incorporating specialized expert models for enhanced performance.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics due to specialized expert models.
  • Instruction Following: Demonstrates substantial improvements in adhering to instructions and generating long texts (over 8K tokens).
  • Structured Data & Output: Better understanding of structured data (e.g., tables) and improved generation of structured outputs, particularly JSON.
  • Robustness: More resilient to diverse system prompts, which enhances role-play implementation and condition-setting for chatbots.
  • Multilingual Support: Offers support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.

Ideal Use Cases

This model is well-suited for applications requiring:

  • Chatbot Development: Its improved instruction following and resilience to system prompts make it effective for creating interactive and adaptable chatbots.
  • Code Generation & Mathematics: Leveraging its enhanced capabilities in these domains for technical assistance and problem-solving.
  • Structured Data Processing: Generating and understanding structured outputs like JSON, useful for data extraction and API interactions.
  • Multilingual Applications: Supporting a broad range of languages for global content generation and understanding.

Finetunes
1 models