YFC-112358/Qwen3.8-27B-TA-Aux-v1-2

VISIONPricing:Input $1.6 / Cached $0.15 / Output $12Concurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 16, 2026Architecture:Transformer Featherless Exclusive Cold

YFC-112358/Qwen3.8-27B-TA-Aux-v1-2 is a 27 billion parameter language model, created by YFC-112358, with a context length of 32768 tokens. This model is a merged version, combining full weights from various sources as detailed in its build manifest. Its primary characteristic is being a composite model, where the merge process focuses on tensor shapes and finite values rather than direct quality measurement. It is suitable for applications requiring a large parameter count and extended context, where the specific blend of its merged components might offer unique performance characteristics.

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Overview

YFC-112358/Qwen3.8-27B-TA-Aux-v1-2 is a 27 billion parameter language model with a 32768-token context window. This model is a result of merging full weights from multiple sources, with the exact recipe and input commits detailed in its build_manifest.json. The merging process specifically verifies tensor shapes and finite values, rather than directly assessing the resulting model's quality.

Key Characteristics

  • Merged Architecture: Composed of full weights from various upstream models, offering a potentially unique blend of capabilities.
  • Large Scale: Features 27 billion parameters, suitable for complex language understanding and generation tasks.
  • Extended Context: Supports a substantial context length of 32768 tokens, enabling processing of longer inputs and maintaining coherence over extended dialogues or documents.
  • Transparent Build: The build_manifest.json provides full transparency into the components and process used to create this merged model.

Good for

  • Experimental Applications: Ideal for researchers and developers looking to explore the emergent properties of merged large language models.
  • Applications Requiring Large Context: Its 32768-token context window makes it suitable for tasks involving extensive text analysis, summarization of long documents, or maintaining long-form conversations.
  • Custom Fine-tuning: Can serve as a robust base model for further fine-tuning on specific datasets, leveraging its diverse merged origins.

Finetunes
1 models