Jordansky/augmented-f560e4e6ee71e78d

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:May 14, 2026Architecture:Transformer Warm

Jordansky/augmented-f560e4e6ee71e78d is a 4 billion parameter language model with a 32768 token context length. This model card has been automatically generated, indicating it is a base model without specific fine-tuning details provided. Its primary characteristics and intended use cases are not explicitly defined in the available documentation, suggesting it may serve as a foundational model for further development or specific applications.

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Model Overview

This model, Jordansky/augmented-f560e4e6ee71e78d, is a 4 billion parameter language model with a substantial context length of 32768 tokens. The model card indicates it is a Hugging Face Transformers model that has been automatically pushed to the Hub. As a base model, specific details regarding its development, funding, language support, or fine-tuning origins are currently marked as "More Information Needed" in its documentation.

Key Characteristics

  • Parameter Count: 4 billion parameters, placing it in the medium-sized category for LLMs.
  • Context Length: Features a 32768 token context window, allowing for processing of extensive inputs and generating longer, more coherent outputs.
  • Development Status: The model card is largely a placeholder, suggesting it is a foundational model awaiting further specification or intended for general-purpose use where specific optimizations are not yet defined.

Intended Use Cases

Given the lack of specific guidance in the model card, this model is likely suitable for:

  • General Language Tasks: As a base model, it can be adapted for various NLP tasks such as text generation, summarization, and question answering.
  • Further Fine-tuning: Its substantial parameter count and context length make it a strong candidate for fine-tuning on domain-specific datasets or for particular applications where custom performance is required.
  • Research and Experimentation: Developers can use this model to explore different fine-tuning strategies or integrate it into novel AI systems.