Fine-tuning with Retrieval Augmentation with llama or any open source model

[Manikanta5112]

Hi Community,

Did anyone have tried/experience in implementing a Fine-tuning with Retrieval Augmentation with llama or any open source model? is so, I really appreciate the heads up to know how you did

[dosubot[bot]]

🤖

Hey @Manikanta5112! Nice to see you back in our little corner of the code world. Hope this day finds you in top coding shape!

Yes, fine-tuning with Retrieval Augmentation has been implemented using the Llama model. The core idea is to allow the Language Model (LLM) to better use the context from a given retriever or ignore it entirely. This is referenced from the RA-DIT paper: https://arxiv.org/abs/2310.01352.

You can find a notebook that demonstrates this in the /examples/finetuning/knowledge/finetune_retrieval_aug.ipynb path of the repository.

As for the specific functionality of the Llama model in the LlamaIndex repository, the Clarifai class in the llama_index/llms/clarifai.py file is a specific implementation of the LLM (Language Learning Model) class for the Clarifai model. It provides functionalities such as initialization, chat and completion, streaming, asynchronous operations, and metadata. You can find more details in the llama_index/llms/clarifai.py file.

To implement fine-tuning with Retrieval Augmentation using the Llama model in the LlamaIndex repository, you would need to follow these steps:

1. Generate synthetic queries over documents: Use the generate_synthetic_queries_over_documents function. This function takes a list of documents and generates synthetic queries over them. It uses the LLM or Language Model to generate the queries. The function returns a list of questions.

questions = generate_synthetic_queries_over_documents(documents)

2. Generate the Cross-Encoder fine-tuning dataset: Use the generate_ce_fine_tuning_dataset function. This function takes the list of documents and the list of questions generated in the previous step. It uses the LLM or Language Model to determine the relevance of the retrieved document to the query. The function returns a list of CrossEncoderFinetuningDatasetSample objects, each containing a query, context, and score.

ce_dataset_list = generate_ce_fine_tuning_dataset(documents, questions)

You can find more details in the llama_index/finetuning/cross_encoders/dataset_gen.py file.

I hope this helps! If you have any more questions, feel free to ask.

Sources

• docs/optimizing/fine-tuning/fine-tuning.md
• llama_index/llms/clarifai.py
• llama_index/finetuning/cross_encoders/dataset_gen.py

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