gradients-io-tournaments/qwen2.5-3b-en-ja-finetune-upwork2

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 2, 2026Architecture:Transformer Featherless Exclusive Cold

The gradients-io-tournaments/qwen2.5-3b-en-ja-finetune-upwork2 is a 3.09 billion parameter Qwen2.5-3B-Instruct model, fine-tuned for enhanced performance in both English and Japanese language tasks. Developed by gradients-io-tournaments, this model utilizes a Qwen2ForCausalLM architecture with a 32,768 token context length. It is specifically optimized for bilingual applications requiring robust language understanding and generation in English and Japanese.

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Model Overview

This model, gradients-io-tournaments/qwen2.5-3b-en-ja-finetune-upwork2, is a fine-tuned version of the Qwen2.5-3B-Instruct base model, specifically optimized for English and Japanese language use cases. It is a merged checkpoint, indicating a comprehensive training process to enhance its bilingual capabilities.

Key Technical Specifications

  • Base Model: Qwen/Qwen2.5-3B-Instruct
  • Architecture: Qwen2ForCausalLM, featuring 36 hidden layers, 16 attention heads, and 2 key-value heads.
  • Parameters: Approximately 3.09 billion parameters, providing a balance between performance and computational efficiency.
  • Precision: Operates in bfloat16 precision.
  • Context Length: Supports a substantial context window of 32,768 tokens, utilizing a rope theta of 1,000,000, which is beneficial for processing longer texts and maintaining coherence over extended conversations.

Primary Use Case

This model is particularly well-suited for applications requiring strong performance in both English and Japanese, such as:

  • Bilingual chatbots and virtual assistants.
  • Content generation in either language.
  • Cross-lingual information retrieval and summarization.
  • Educational tools supporting both English and Japanese learners.

Finetunes
1 models