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Copy file name to clipboardExpand all lines: comps/dataprep/README.md
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## Use LVM (Large Vision Model) for Summarizing Image Data
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Occasionally unstructured data will contain image data, to convert the image data to the text data, LVM can be used to summarize the image. To leverage LVM, please refer to this [readme](../lvms/README.md) to start the LVM microservice first and then set the below environment variable, before starting any dataprep microservice.
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Occasionally unstructured data will contain image data, to convert the image data to the text data, LVM can be used to summarize the image. To leverage LVM, please refer to this [readme](../lvms/llava/README.md) to start the LVM microservice first and then set the below environment variable, before starting any dataprep microservice.
Copy file name to clipboardExpand all lines: comps/finetuning/README.md
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### 3.4 Leverage fine-tuned model
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After fine-tuning job is done, fine-tuned model can be chosen from listed checkpoints, then the fine-tuned model can be used in other microservices. For example, fine-tuned reranking model can be used in [reranks](../reranks/README.md) microservice by assign its path to the environment variable `RERANK_MODEL_ID`, fine-tuned embedding model can be used in [embeddings](../embeddings/README.md) microservice by assign its path to the environment variable `model`, LLMs after instruction tuning can be used in [llms](../llms/README.md) microservice by assign its path to the environment variable `your_hf_llm_model`.
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After fine-tuning job is done, fine-tuned model can be chosen from listed checkpoints, then the fine-tuned model can be used in other microservices. For example, fine-tuned reranking model can be used in [reranks](../reranks/fastrag/README.md) microservice by assign its path to the environment variable `RERANK_MODEL_ID`, fine-tuned embedding model can be used in [embeddings](../embeddings/README.md) microservice by assign its path to the environment variable `model`, LLMs after instruction tuning can be used in [llms](../llms/text-generation/README.md) microservice by assign its path to the environment variable `your_hf_llm_model`.
Copy file name to clipboardExpand all lines: comps/guardrails/llama_guard/langchain/README.md
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### 1.4 Start Guardrails Service
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Optional: If you have deployed a Guardrails model with TGI Gaudi Service other than default model (i.e., `meta-llama/Meta-Llama-Guard-2-8B`) [from section 1.2](## 1.2 Start TGI Gaudi Service), you will need to add the eviornment variable `SAFETY_GUARD_MODEL_ID` containing the model id. For example, the following informs the Guardrails Service the deployed model used LlamaGuard2:
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Optional: If you have deployed a Guardrails model with TGI Gaudi Service other than default model (i.e., `meta-llama/Meta-Llama-Guard-2-8B`) [from section 1.2](#12-start-tgi-gaudi-service), you will need to add the eviornment variable `SAFETY_GUARD_MODEL_ID` containing the model id. For example, the following informs the Guardrails Service the deployed model used LlamaGuard2:
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