Generative models

A generative model produces text as output. These models provide the reasoning capabilities needed to understand tasks, determine a course of action, and generate responses. A generative model serves as an agent’s cognitive engine, and every agent requires one to function. Most generative models take text as input. Vision models also accept images, which they describe or reason about in their text response. Generative models are also the models you fine-tune and deploy for inference. For models that produce vectors rather than text, see Embedding models.

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Model types

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Instruct models

Instruct models are instruction-tuned and ready to use. They follow instructions, hold conversations, and respond to prompts without further training. Most fine-tuning also starts from an Instruct model, since fine-tuning adapts a model that already follows instructions.

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Base models

Base models are pretrained weights without instruction tuning. They are not conversational without further training.

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Fine-tuned models

Fine-tuned models are custom-trained models adapted to specific domains or use cases. These models are created through SeekrFlow’s fine-tuning component, which embeds specialized knowledge and behaviors directly into model parameters.

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Available models

The Type column reports whether a model is Instruct or Base. Whether a given model can be fine-tuned or deployed is reported per model in the catalog and is not implied by its type. See Model attributes.

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Meta Llama models

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Qwen models

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Mistral AI models

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DeepSeek models

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Google Gemma models

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Seekr models

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Other models

Mixed means a model has no single country of origin. Empty cells are values the model catalog does not currently report. See Country of origin.

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Select a model

When configuring an agent, you specify which model to use. Consider these factors when selecting a model:

• Type – Instruct models are ready to use as-is. Base models require fine-tuning before they behave conversationally.
• Task complexity – More complex reasoning tasks may benefit from larger or specialized models.
• Response requirements – Balance between speed and accuracy based on your use case.
• Domain specificity – Fine-tuned models perform better for specialized domains with unique terminology or requirements.
• Cost and performance – Larger models provide enhanced capabilities but with higher computational costs.
• Country of origin – Required if your deployment restricts model provenance. See Country of origin.

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Model configuration

Models are specified during agent creation and can be updated by modifying the agent configuration. The model works in conjunction with other agent components like instructions and tools to determine overall agent behavior.