license: llama3
language:
- en
pipeline_tag: text-generation
tags:
- nvidia
- chatqa-1.5
- chatqa
- llama-3
- pytorch
base_model: nvidia/Llama3-ChatQA-1.5-8B
quantized_by: bartowski
lm_studio:
param_count: 8b
use_case: RAG
release_date: 28-04-2024
model_creator: nvidia
prompt_template: chatQA
system_prompt: >-
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.
base_model: llama
original_repo: nvidia/Llama3-ChatQA-1.5-8B
💫 Community Model> Llama 3 ChatQA 1.5 8B by NVIDIA
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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
Model Summary:
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.
Prompt Template:
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."
Technical Details
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
🙏 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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