Estimated reading time: 3 minutes
This list is based on a combination of factors including general popularity, instruction-following capabilities, context window size, and community interest relevant to chatbot and Retrieval-Augmented Generation (RAG) applications.
1. mistralai/Mixtral-8x7B-Instruct-v0.1
Use Cases: Excellent for instruction following, complex reasoning in chatbots, and can handle long contexts for RAG.
View on Hugging Face2. meta-llama/Llama-3-8B-Instruct
Use Cases: Strong general-purpose model, suitable for chatbots with good conversational abilities and RAG applications.
View on Hugging Face3. meta-llama/Llama-3-70B-Instruct
Use Cases: More powerful version of Llama 3, ideal for complex chatbots requiring deep understanding and RAG with extensive knowledge retrieval.
View on Hugging Face4. google/gemma-7b-it
Use Cases: Instruction-tuned model from Google, good for building chatbots and can be used effectively in RAG pipelines.
View on Hugging Face5. microsoft/phi-3-mini-4k-instruct
Use Cases: Smaller and more efficient model, surprisingly capable for chatbots and RAG where resource constraints are important.
View on Hugging Face6. HuggingFaceH4/zephyr-7b-beta
Use Cases: Fine-tuned for instruction following, performs well in conversational settings and RAG tasks.
View on Hugging Face7. TheBloke/Mistral-7B-Instruct-v0.2-AWQ
Use Cases: Quantized version of Mistral 7B, offering a good balance of performance and efficiency for local chatbot and RAG deployments.
View on Hugging Face8. OpenAssistant/oasst-sft-4-pythia-12b
Use Cases: Openly developed assistant model, suitable for conversational AI and RAG experiments.
View on Hugging Face9. databricks/dolly-v2-12b
Use Cases: Instruction-tuned model focused on accessibility, can be used for building chatbots and RAG applications.
View on Hugging Face10. facebook/bart-large-cnn
Use Cases: While primarily for summarization, BART’s encoder-decoder architecture can be adapted for chatbot tasks and RAG by conditioning on retrieved documents.
View on Hugging FaceThe best model for your specific chatbot or RAG application will depend on factors like your performance requirements, available computational resources, and the nature of your data.
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