1010happy/BALANCED_claude_stagger_cur1to7_perblock5-Qwen2-5-1-5B-Instruct-seed10

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 11, 2026Architecture:Transformer Featherless Exclusive Cold

The 1010happy/BALANCED_claude_stagger_cur1to7_perblock5-Qwen2-5-1-5B-Instruct-seed10 model is a 1.5 billion parameter instruction-tuned language model with a 32768 token context length. Developed by 1010happy, this model is based on the Qwen2-5 architecture. Its specific fine-tuning, indicated by "BALANCED_claude_stagger_cur1to7_perblock5" and "seed10", suggests an optimization for balanced performance across various tasks, potentially influenced by Claude-like response characteristics.

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Model Overview

This model, named 1010happy/BALANCED_claude_stagger_cur1to7_perblock5-Qwen2-5-1-5B-Instruct-seed10, is an instruction-tuned language model with 1.5 billion parameters and a substantial context length of 32768 tokens. It is built upon the Qwen2-5 architecture, indicating its foundational design. The model's name suggests a specific fine-tuning approach, likely aimed at achieving a balanced output style or performance profile, possibly drawing inspiration from Claude's conversational characteristics, as implied by "BALANCED_claude_stagger_cur1to7_perblock5". The "seed10" further points to a specific training configuration.

Key Characteristics

  • Parameter Count: 1.5 billion parameters, offering a balance between computational efficiency and capability.
  • Context Length: Features a large 32768 token context window, enabling the processing and generation of extensive text.
  • Instruction-Tuned: Optimized to follow instructions effectively, making it suitable for a variety of prompt-based tasks.
  • Qwen2-5 Architecture: Based on the Qwen2-5 model family, providing a robust and recognized foundation.

Potential Use Cases

Given its instruction-tuned nature and balanced fine-tuning, this model could be suitable for:

  • General-purpose text generation and completion.
  • Conversational AI and chatbots requiring nuanced responses.
  • Summarization and question-answering tasks over long documents due to its large context window.
  • Applications where a balanced and consistent output style is preferred.

Finetunes
2 models