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Lean how to use TGI-gaudi and Langchain to build and deploy a RAG application
One of the main challenges in training Large Language Models (LLMs) is that they are often too large to fit on a single node or even if they fit, the training may be too slow. To address this issue, their training can be parallelized across multiple Gaudi accelerators (HPUs).
If you want to train a large model using Megatron-DeepSpeed, but the model you want is not included in the implementation, you can port it to the Megatron-DeepSpeed package. Assuming your model is transformer-based, you can add your implementation easily, basing it on existing code.
We are excited to see Meta release Llama 2, to help further democratize access to LLMs. Making such models more widely available will facilitate efforts across the AI community to benefit the world at large.
We’re excited to participate in this year’s ISC High Performance Compute 2023 event in Hamburg Germany. This year our team will demonstrate the capabilities of our Habana Gaudi2® processors, which deliver high-performance, high-efficiency deep learning training and inference.
One of the key challenges in Large Language Model (LLM) training is reducing the memory requirements needed for training without sacrificing compute/communication efficiency and model accuracy.
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