yuq-zhou/2026-05-o-b0p3-a0p3-gc0p5-exp-td2p0-tw5p0-q2-m-7

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 17, 2026Architecture:Transformer Featherless Exclusive Cold

The yuq-zhou/2026-05-o-b0p3-a0p3-gc0p5-exp-td2p0-tw5p0-q2-m-7 is a 7.6 billion parameter causal language model developed by yuq-zhou. This model is provided as a research artifact backup in standard HuggingFace format. Its primary purpose is to serve as a checkpoint for research and development, offering a base for further experimentation and fine-tuning.

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Model Overview

The yuq-zhou/2026-05-o-b0p3-a0p3-gc0p5-exp-td2p0-tw5p0-q2-m-7 is a 7.6 billion parameter causal language model. It is presented as a research artifact backup, provided in the standard HuggingFace format, making it readily compatible with AutoModelForCausalLM.from_pretrained for easy integration into existing workflows.

Key Characteristics

  • Parameter Count: This model features 7.6 billion parameters, placing it in a size category suitable for various research and application development tasks.
  • Context Length: It supports a context length of 32,768 tokens, allowing for processing and generating longer sequences of text.
  • Format: The model is distributed in a standard HuggingFace checkpoint format, ensuring broad compatibility and ease of use within the HuggingFace ecosystem.

Intended Use

This model is primarily intended as a research artifact. It serves as a foundational checkpoint for developers and researchers looking to:

  • Experimentation: Conduct further research and development on a pre-trained base model.
  • Fine-tuning: Adapt the model for specific downstream tasks or datasets.
  • Backup: Utilize a stable checkpoint for reproducibility in research projects.

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