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Change hyperlink for llmforge to docs instead of versions table #395

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Change hyperlink for llmforge to docs instead of versions table #395

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2 changes: 1 addition & 1 deletion templates/fine-tune-llm_v2/README.ipynb
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"**⏱️ Time to complete**: ~3 hours (includes the time for training the model)\n",
"\n",
"\n",
"This template comes with a installed library for training LLMs on Anyscale called [LLMForge](https://docs.anyscale.com/reference/llmforge-versions). It provides the fastest way to try out training LLMs with Ray on Anyscale. You can read more about this library and its features in the [docs](https://docs.anyscale.com/llms/finetuning/intro). For learning on how to serve the model online or offline for doing batch inference you can refer to the [serving template](https://console.anyscale.com/v2/template-preview/endpoints_v2) or the [offline batch inference template](https://console.anyscale.com/v2/template-preview/batch-llm), respecitvely.\n"
"This template comes with a installed library for training LLMs on Anyscale called [LLMForge](https://docs.anyscale.com/llms/finetuning/intro). It provides the fastest way to try out training LLMs with Ray on Anyscale. You can read more about this library and its features in the [docs](https://docs.anyscale.com/llms/finetuning/intro). For learning on how to serve the model online or offline for doing batch inference you can refer to the [serving template](https://console.anyscale.com/v2/template-preview/endpoints_v2) or the [offline batch inference template](https://console.anyscale.com/v2/template-preview/batch-llm), respecitvely.\n"
]
},
{
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2 changes: 1 addition & 1 deletion templates/fine-tune-llm_v2/README.md
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**⏱️ Time to complete**: ~3 hours (includes the time for training the model)


This template comes with a installed library for training LLMs on Anyscale called [LLMForge](https://docs.anyscale.com/reference/llmforge-versions). It provides the fastest way to try out training LLMs with Ray on Anyscale. You can read more about this library and its features in the [docs](https://docs.anyscale.com/llms/finetuning/intro). For learning on how to serve the model online or offline for doing batch inference you can refer to the [serving template](https://console.anyscale.com/v2/template-preview/endpoints_v2) or the [offline batch inference template](https://console.anyscale.com/v2/template-preview/batch-llm), respecitvely.
This template comes with a installed library for training LLMs on Anyscale called [LLMForge](https://docs.anyscale.com/llms/finetuning/intro). It provides the fastest way to try out training LLMs with Ray on Anyscale. You can read more about this library and its features in the [docs](https://docs.anyscale.com/llms/finetuning/intro). For learning on how to serve the model online or offline for doing batch inference you can refer to the [serving template](https://console.anyscale.com/v2/template-preview/endpoints_v2) or the [offline batch inference template](https://console.anyscale.com/v2/template-preview/batch-llm), respecitvely.


## Getting Started
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