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title: README |
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emoji: ⚡ |
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colorTo: blue |
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sdk: static |
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pinned: false |
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--- |
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The Arch Family. |
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The Arch family of LLMs are designed to fast and efficient LLMs for common scenarios in agentic application worloads - helping developers stay focused on higher level objectives |
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of their agents. These scenario include fast agent routing and hand-off, tools calls for common agentic scenarios to improve speed, guadrails and input/output validation of prompts and dynamic routing to LLM based on human preferences. |
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The Arch family of LLMs power the intelligence for [Arch](https://github.com/katanemo/archgw) (The AI-native proxy server and universal data plane for agents). |
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Current |
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1. Arch-Guard: A fast and efficient model for jailbreak attempts; improves performance over Meta Prompt Guard |
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2. Arch-Router: A fast preference-aligned routing model that guides LLM selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) – offers a practical mechanism to encode preferences in routing decision. |
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3. Arch-Agent: Designed to power sophisticated multi-step and multi-turn workflows, Arch-Agent excels at handling complex, multi-step tasks that require intelligent tool selection, adaptive planning, and seamless integration with external APIs and services. |
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History |
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2. Arch-Function-Chat: A state-of-the-art (SOTA) function calling model also trained to chat - especially useful in scenarios where the model must clarify and refine inputs from the user, |
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accurately deterime user's downstream intent, and manage decision making in long-form context and complext user interactions. Achieving performance on par with GPT-4. |
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1. Arch-Function: State-of-the-art (SOTA) function calling models designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. |
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Achieving performance on par with GPT-4. |
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