How to fine-tune or prompt engineer these available models ?

[FaheemOnHub]

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Having access to these models is great, but how to implement RAG on them ?

[Rohit-RA-2020]

You can pass the context as a user query {"role": "user", "content": "Answer the following question {query} based on given {context}"}

[KelvinCode1234]

To fine-tune or prompt engineer models:

Fine-Tuning

1. Select a Pre-trained Model: Choose one like GPT-3 or BERT.
2. Prepare Data: Gather and preprocess your dataset.
3. Set Up: Use frameworks like TensorFlow, PyTorch, or Hugging Face. Configure training parameters.
4. Train: Train the model with your data.
5. Evaluate: Validate and adjust as needed.

Prompt Engineering

1. Understand the Model: Learn its strengths and limitations.
2. Craft Prompts: Write clear, effective prompts. Experiment with different phrasings.
3. Test and Refine: Test outputs and refine prompts.
4. Optimize: Use techniques like prompt chaining for better results.

Check the model's documentation and community resources for more details.

Good luck!

[KelvinCode1234]

To implement RAG with the models you have:

1. Choose Your Models:

   • Retriever: For fetching relevant documents (e.g., BM25, DPR).
   • Generator: For generating answers (e.g., GPT-3, T5).

2. Set Up the Retriever:

   • Index your knowledge base using a retriever model.

3. Set Up the Generator:

   • Load and fine-tune a generative model if needed.

4. Integrate Both:

   • Query Processing: Retrieve documents using the retriever.
   • Generate Answer: Use the generative model to create the final answer based on retrieved documents.

Check out the model documentation and community resources for more detailed steps. Good luck!

[KateCatlin]

Hey @FaheemOnHub! Thanks for the question! RAG support is coming soon.

I'd love to chat more with you about what you're building and how both RAG and fine tuning tie into it. If you're open to a 30-minute zoom call, you can book me here! We have a ton of work planned for expanding GitHub Models and I really want to learn more about how we can make it great for users like you.

[waletmaker0]

Great question! To implement RAG, you'll need to combine retrieval (like using a vector database) with generation. Fine-tuning isn't always needed—prompt engineering with a proper context window and retrieved documents can go a long way.

[harshhrawte]

Yes, you can finetune these models easily by using the LoRA technique: you can quantize the model from high memory format to lower memory format and then also adjust the learning rate, decay, and all such factors, provide your own custom dataset, and then finally your finetuned model is ready. Here, instead of directly asking questions like "What is Python?" you can ask "You are a Python expert, guide me through and solve my doubt," so this way is better to get an output.

Similarly, you can build a RAG as well, where you use your custom dataset and model. Along with this, you also add a prompt like "You are a helpful guide," and all such. You can also use a query like {"role": "user", "content": "Answer the following question {query} based on the given {context}"} frame this properly.

[rifialdiif]

RAG (Retrieval Augmented Generation) is a technique to make LLMs smarter and more factual. Here's how it works:

Retrieve Information: The LLM fetches relevant data from an external knowledge base (your up-to-date information).

Combine & Generate: This retrieved information is then combined with your query, and the LLM uses this combined context to generate an accurate and relevant answer.

You can implement RAG using:

Prompt Engineering: Structuring the LLM's prompt to include the retrieved context, without needing to retrain the model. This is the most common and efficient method.

Fine-tuning: Retraining parts of the model (e.g., the embedding model for retrieval, or the LLM itself) to better handle your specific context retrieval and usage. This is more complex but can improve performance in niche domains.