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Llama3-ChatQA-1.5-8B-GGUF

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Model creator: nvidia
Original model: Llama3-ChatQA-1.5-8B
GGUF quantization: provided by bartowski based on llama.cpp release b2777

ChatQA 1.5 is a series of models trained to excel at RAG (retrieval augmented generation) tasks.
This model may work for general uses, but it primarily meant for use as a context sumarizer or context extraction.
Using the context provided after the system message, the model is able to provide contextual and accurate answers to queries.

For now, you'll need to make your own template. Choose the LM Studio Blank Preset in your LM Studio.

Then, set the system prompt to whatever you'd like (check the recommended one below), and set the following values:
System Message Prefix: 'System: '
User Message Prefix: '\n\nUser: '
User Message Suffix: '\n\nAssistant: <|begin_of_text|>'

If you want to provide context, place that in the system message suffix like so:

System Message Suffix: '\n\n{context}'

Under the hood, the model will see a prompt that's formatted like:

System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.

This is some context

User: {Question}

Assistant: 

nVidia also seems to recommend starting your query with "Please give a full and complete answer for the question."

Llama3-ChatQA-1.5 excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from ChatQA (1.0), and it is built on top of Llama-3 base model.
Specifically, more conversational QA data was used to enhance its tabular and arithmetic calculation capability.

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for the IQ1_M and IQ2_XS quants, which makes them usable even at their tiny size!

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