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docs: Clarify kfp provider is both inline and remote (#2144)

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The provider selling point *is* using the same provider for both.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
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Ihar Hrachyshka 2025-05-14 03:37:07 -04:00 • committed by GitHub
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docs/source/providers/external.md
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@ -53,7 +53,7 @@ Here's a list of known external providers that you can use with Llama Stack:
| Name | Description | API | Type | Repository |
|------|-------------|-----|------|------------|
| KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) |
| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) |
| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Inline **and** Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) |
| RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) |
| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) |