1010happy/claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed51485

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 8, 2026Architecture:Transformer Featherless Exclusive Cold

The 1010happy/claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed51485 model is a 3.1 billion parameter instruction-tuned causal language model based on the Qwen2 architecture. This model is designed for general-purpose natural language understanding and generation tasks, leveraging its instruction-following capabilities. With a substantial 32768 token context length, it is suitable for applications requiring processing of longer inputs and generating coherent, extended responses.

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Model Overview

This model, 1010happy/claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed51485, is an instruction-tuned variant built upon the Qwen2 architecture, featuring approximately 3.1 billion parameters. It is designed to follow instructions effectively for various natural language processing tasks. A notable characteristic is its extended context window of 32768 tokens, which allows for processing and generating longer sequences of text.

Key Capabilities

  • Instruction Following: Optimized to understand and execute user instructions for diverse NLP tasks.
  • Extended Context: Supports a 32768-token context length, beneficial for complex queries, summarization of long documents, or maintaining conversational coherence over extended interactions.
  • General-Purpose Language Generation: Capable of generating human-like text across a wide range of topics and styles.

Potential Use Cases

  • Chatbots and Conversational AI: Its instruction-following and long context capabilities make it suitable for engaging in detailed conversations.
  • Content Generation: Can be used for generating articles, creative writing, or marketing copy based on specific prompts.
  • Summarization: Effective for summarizing lengthy documents or discussions due to its large context window.
  • Question Answering: Capable of answering questions by processing provided context and instructions.