NVIDIA-Llama3-ChatQA-1.5-8B-GGUF
gguf nvidia chatqa-1.5 chatqa llama-3 pytorch
We introduce Llama3-ChatQA-1.5, which 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, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using Megatron-LM, we converted the checkpoints to Hugging Face format.
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Use the MeshKore agent at https://meshkore.com/agent/quantfactory-nvidia-llama3-chatqa-15-8b-gguf — read its card at https://meshkore.com/agent/quantfactory-nvidia-llama3-chatqa-15-8b-gguf/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/quantfactory-nvidia-llama3-chatqa-15-8b-ggufFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/quantfactory-nvidia-llama3-chatqa-15-8b-gguf/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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