Model overview
Carnice-9b is a specialized 9-billion parameter model built from Qwen3.5-9B through a two-stage training process. Unlike generic chat models optimized for benchmarks, this model targets a specific runtime environment: the Hermes Agent framework. The training focused on teaching the model to work with agent-native tool calling, terminal commands, browser automation, and multi-step task execution. Created by kai-os, it represents a shift away from broad capability optimization toward deep integration with a particular agent harness. Models like OmniCoder-9B and Hermes-4-14B pursue similar agent-focused approaches, though Carnice-9b is purpose-built specifically for Hermes workflows.
Model inputs and outputs
The model accepts text prompts in Hermes Agent format and produces structured responses aligned with agent action patterns. Rather than generating free-form text, it outputs tool calls, terminal commands, and navigation instructions that the Hermes runtime interprets directly.
Inputs
- Natural language instructions and multi-turn conversations within the Hermes Agent environment
- Structured tool-calling requests aligned with Hermes-native formatting
- Context from previous agent actions and results from earlier tool executions
Outputs
- Tool invocation requests with proper parameters for file editing, terminal use, and browser control
- Formatted responses that integrate with the Hermes Agent execution pipeline
- Multi-step action sequences that the harness can chain together automatically
Capabilities
The model excels at understanding complex task decomposition within agent workflows. It can break terminal-heavy operations into executable steps, edit files with awareness of their structure, navigate web pages with clear instructions to the browser tool, and maintain context across many agent interactions. The training on actual Hermes traces means it understands the exact message patterns and formatting conventions the agent runtime expects, reducing friction between reasoning and execution.
What can I use it for?
Developers building systems on top of the Hermes Agent framework can deploy this model to handle agentic reasoning without retraining. Use cases include automated terminal-based system administration, document generation and editing pipelines that modify files on disk, research assistance with live web browsing, and complex debugging workflows where the agent needs to read logs, identify issues, and propose fixes. The model's focus on tool calling makes it suitable for any workflow where a language model coordinates external tools rather than acting as a pure chat interface.
Things to try
Test the model's behavior by giving it complex filesystem tasks that require understanding directory structure and file dependencies. Experiment with multi-turn interactions where each step depends on the output of the previous one, since it was specifically trained on this pattern. Try providing it with terminal error messages and observing whether it forms reasonable debugging strategies. Compare its tool-calling consistency with models trained on generic agent data to see how the Hermes-specific training affects task completion rates versus hallucination of invalid commands.
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