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Update databricks.md (#6006)

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@ -8,7 +8,7 @@ Databricks embraces the LangChain ecosystem in various ways:
1. Databricks connector for the SQLDatabase Chain: SQLDatabase.from_databricks() provides an easy way to query your data on Databricks through LangChain 1. Databricks connector for the SQLDatabase Chain: SQLDatabase.from_databricks() provides an easy way to query your data on Databricks through LangChain
2. Databricks-managed MLflow integrates with LangChain: Tracking and serving LangChain applications with fewer steps 2. Databricks-managed MLflow integrates with LangChain: Tracking and serving LangChain applications with fewer steps
3. Databricks as an LLM provider: Deploy your fine-tuned LLMs on Databricks via serving endpoints or cluster driver proxy apps, and query it as langchain.llms.Databricks 3. Databricks as an LLM provider: Deploy your fine-tuned LLMs on Databricks via serving endpoints or cluster driver proxy apps, and query it as langchain.llms.Databricks
4. Databricks Dolly: Databricks open-sourced Dolly which allows for commercial use, and can be accessed through the HuggingFace Hub 4. Databricks Dolly: Databricks open-sourced Dolly which allows for commercial use, and can be accessed through the Hugging Face Hub
Databricks connector for the SQLDatabase Chain Databricks connector for the SQLDatabase Chain
---------------------------------------------- ----------------------------------------------
@ -28,9 +28,9 @@ Databricks as an LLM provider
The notebook [Wrap Databricks endpoints as LLMs](../modules/models/llms/integrations/databricks.html) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. The notebook [Wrap Databricks endpoints as LLMs](../modules/models/llms/integrations/databricks.html) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development.
Databricks endpoints support Dolly, but are also great for hosting models like MPT-7B or any other models from the HuggingFace ecosystem. Databricks endpoints can also be used with proprietary models like OpenAI to provide a governance layer for enterprises. Databricks endpoints support Dolly, but are also great for hosting models like MPT-7B or any other models from the Hugging Face ecosystem. Databricks endpoints can also be used with proprietary models like OpenAI to provide a governance layer for enterprises.
Databricks Dolly Databricks Dolly
---------------- ----------------
Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [HuggingFace Hub](../modules/models/llms/integrations/huggingface_hub.html) for instructions to access it through the HuggingFace Hub integration with LangChain. Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [Hugging Face Hub](../modules/models/llms/integrations/huggingface_hub.html) for instructions to access it through the Hugging Face Hub integration with LangChain.