1010happy/BALANCED_claude_max_max7_perblock35-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_max_max7_perblock35-Qwen2-5-1-5B-Instruct-seed10 is a 1.5 billion parameter instruction-tuned language model based on the Qwen2-5 architecture. This model is designed for general-purpose conversational AI tasks, leveraging its instruction-following capabilities. With a context length of 32768 tokens, it aims to provide balanced performance for various natural language processing applications. Its primary strength lies in its ability to process and generate human-like text based on given instructions.

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Model Overview

The 1010happy/BALANCED_claude_max_max7_perblock35-Qwen2-5-1-5B-Instruct-seed10 is an instruction-tuned language model with 1.5 billion parameters, built upon the Qwen2-5 architecture. It is designed to understand and follow instructions for various natural language processing tasks. The model supports a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text while maintaining coherence.

Key Capabilities

  • Instruction Following: Optimized to respond accurately and relevantly to user instructions.
  • General-Purpose Text Generation: Capable of generating human-like text for a wide array of prompts.
  • Extended Context Handling: Benefits from a 32768-token context window, suitable for tasks requiring extensive input or output.

Use Cases

This model is suitable for applications requiring robust instruction-following and text generation. While specific training details and performance benchmarks are not provided in the model card, its design suggests utility in:

  • Conversational AI and chatbots.
  • Content creation and summarization.
  • Question answering systems.

Limitations

As indicated by the model card, specific details regarding its development, training data, evaluation, biases, risks, and environmental impact are currently marked as "More Information Needed." Users should exercise caution and conduct their own evaluations before deploying the model in sensitive or critical applications.

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