1010happy/BALANCED_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/BALANCED_claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed51485 model is a 3.1 billion parameter instruction-tuned language model based on the Qwen2 architecture. This model is designed for general-purpose language tasks, leveraging its 32768-token context length for processing extensive inputs. Its instruction-following capabilities make it suitable for a variety of conversational and text generation applications.

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Model Overview

This model, 1010happy/BALANCED_claude_max_max7_perblock35-Qwen2-5-3B-Instruct-seed51485, is an instruction-tuned variant built upon the Qwen2 architecture. With approximately 3.1 billion parameters, it is designed to handle a broad range of natural language processing tasks, focusing on instruction following and conversational abilities. A notable feature is its substantial context window of 32768 tokens, allowing it to process and generate longer, more coherent texts.

Key Characteristics

  • Architecture: Based on the Qwen2 model family.
  • Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a large context window of 32768 tokens, beneficial for complex queries and extended conversations.
  • Instruction-Tuned: Optimized to follow user instructions effectively, making it versatile for various applications.

Potential Use Cases

Given its instruction-following nature and considerable context length, this model is well-suited for:

  • General Text Generation: Creating diverse forms of content based on prompts.
  • Conversational AI: Developing chatbots or virtual assistants that can maintain context over longer interactions.
  • Summarization: Processing lengthy documents or conversations to extract key information.
  • Question Answering: Providing detailed answers by leveraging its large context window.