3MPER0RR/Ornith1.5-9B-3MPER0RR-abliterated

VISIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 31, 2026License:mitArchitecture:Transformer Open Weights Featherless Exclusive Cold

Ornith1.5-9B-3MPER0RR-abliterated is a 9 billion parameter language model developed by 3MPER0RR, based on the Ornith architecture. This model is a result of specific research and experimentation trials, offering a refined version of the original Ornith model. With a context length of 32768 tokens, it is suitable for general language understanding and generation tasks where a balance of performance and context handling is required.

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Ornith1.5-9B-3MPER0RR-abliterated Overview

This model, Ornith1.5-9B-3MPER0RR-abliterated, is a 9 billion parameter language model developed by 3MPER0RR. It represents a refined iteration, building upon the original Ornith model architecture. The development involved specific research and experimentation trials, indicating a focused effort on its capabilities and performance.

Key Characteristics

  • Model Base: Derived from the Ornith model family.
  • Parameter Count: Features 9 billion parameters, offering a balance between computational efficiency and performance.
  • Context Length: Supports a substantial context window of 32768 tokens, enabling it to process and generate longer sequences of text.
  • Development: The model is a product of dedicated research and experimentation by 3MPER0RR, suggesting targeted optimizations.
  • License: Distributed under the MIT License, providing flexibility for various applications.

Use Cases

This model is well-suited for developers and researchers looking for a 9B parameter model with a generous context window. Its experimental background suggests potential for tasks requiring robust language understanding and generation, making it a versatile choice for applications such as:

  • General text generation and completion.
  • Summarization of longer documents.
  • Conversational AI where extended context is beneficial.
  • Exploratory research in natural language processing.