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Ornith-1.5-122B

LLMsAI Models & LLMsOpen source · Web

Ornith-1.5-122B is a state-of-the-art LLM designed for efficient single-GPU deployment. It excels in benchmarks, offering strong task decomposition and real-world scenario understanding.

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Ornith-1.5-122B is a highly capable member of the Ornith-1.5 family, designed to deliver exceptional performance in various benchmarks. It is a 122 billion parameter model optimized for efficient deployment on a single GPU, making it accessible for a wide range of applications. The model is particularly noted for its ability to follow instructions accurately, decompose tasks effectively, and understand real-world work scenarios. This makes it a valuable tool for developers and researchers looking to leverage advanced AI capabilities without the need for extensive computational resources.

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The model's open weights are available under the MIT license, promoting open-source collaboration and innovation. It supports a context window of up to 262,144 tokens, which can be extended to approximately 1 million tokens using RoPE scaling. This flexibility allows users to handle large-scale tasks that require extensive input and output capabilities.

Ornith-1.5-122B is compatible with OpenAI's API, enabling seamless integration with existing AI frameworks and tools. It is optimized for terminal-based coding agents and can be used with various agent frameworks like Ollama, Atomic.chat, and Hermes Agent. The model's quantized variant, Ornith-1.5-122B-Mobile, extends its deployment capabilities to mobile devices, further broadening its applicability.

In terms of performance, Ornith-1.5-122B achieves impressive results across multiple benchmarks, including TerminalBench, CyberGym, and SWE-bench, among others. It consistently outperforms or matches other leading models in its class, demonstrating its efficiency and effectiveness in handling complex tasks.

  • CategoryAI Models & LLMs
  • Pricing ModelOpen source
  • DeploymentCloud
  • PlatformsWeb

  • 122 billion parameters

  • Single-GPU deployment

  • Open-source under MIT license

  • Supports up to 262,144 token context window

  • Quantized mobile variant available

  • OpenAI API compatible

  • Optimized for terminal-based coding agents

  • State-of-the-art benchmark performance

  1. Access model page: Visit the Hugging Face model card

  2. Authenticate: Ensure you have the necessary credentials

  3. Configure: Set up the model with recommended parameters

  4. Send prompts: Use the API to interact with the model

  5. Fine-tune: Adjust settings for specific tasks

  • Efficient Deployment
  • Mobile Applications
  • Coding Assistance
  • Real-World Scenarios
  • Open-Source Projects

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