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ai:generalinfo [2024/04/25 16:01] – Wulf Rajekai:generalinfo [2024/07/23 19:56] (current) – [Hardware] Wulf Rajek
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 Instruct/chat models: These models 'expect' to be involved in a conversation with different actors. In contrast non-instruct tuned models will simply generate an output that follows on from the prompt. If you are making a chatbot, implementing RAG or using agents, use instruct or chat models. Instruct/chat models: These models 'expect' to be involved in a conversation with different actors. In contrast non-instruct tuned models will simply generate an output that follows on from the prompt. If you are making a chatbot, implementing RAG or using agents, use instruct or chat models.
 +
 +Embedding models: 
 +Embedding models are used to represent your documents using a sophisticated numerical representation. Embedding models take text as input, and return a long list of numbers used to capture the semantics of the text. These embedding models have been trained to represent text this way, and help enable many applications, including search!
 +[[https://www.marktechpost.com/2024/05/28/nv-embed-nvidias-groundbreaking-embedding-model-dominates-mteb-benchmarks/|NV Embed model]]
  
 Frameworks/GUIs: Frameworks/GUIs:
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 - Tensors: A tensor is an algebraic object that describes a multilinear relationship between sets of algebraic objects related to a vector space. Tensors are a generalisation of scalars and vectors; a scalar is a zero rank tensor, and a vector is a first rank tensor. The rank (or order) of a tensor is defined by the number of directions (and hence the dimensionality of the array) required to describe it. It can be thought of as a multidimensional numerical array. An example of a tensor would be 1000 video frames of 640×480 size.  - Tensors: A tensor is an algebraic object that describes a multilinear relationship between sets of algebraic objects related to a vector space. Tensors are a generalisation of scalars and vectors; a scalar is a zero rank tensor, and a vector is a first rank tensor. The rank (or order) of a tensor is defined by the number of directions (and hence the dimensionality of the array) required to describe it. It can be thought of as a multidimensional numerical array. An example of a tensor would be 1000 video frames of 640×480 size. 
 - Vector: Vectors are used to represent both the input data (features) and the output data (labels or   predictions). Each data point is represented as a feature vector, where each component of the vector corresponds to a specific feature or attribute of the data. - Vector: Vectors are used to represent both the input data (features) and the output data (labels or   predictions). Each data point is represented as a feature vector, where each component of the vector corresponds to a specific feature or attribute of the data.
-- Temperature: Temperature in AI settings control the diversity and creativity of the generated text. Value ranges between 0 and 2 depending on model. Higher temperature values result in more diverse outputs but less logically coherent, while lower values lead to more focused and deterministic text. +- Temperature: Temperature in AI settings control the diversity and creativity of the generated text. Value ranges between 0 and 2 depending on model. Higher temperature values result in more diverse outputs but less logically coherent, while lower values lead to more focused and deterministic text. 
 +- [[https://neo4j.com/blog/graphrag-manifesto/|GraphRAG]] RAG using a graph database instead or in addition to a vector database to return additional knowledge context to an LLM to answer a question or request. 
 ===== Evolution of AI ===== ===== Evolution of AI =====
  
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 GPT-3 175B model: Microsoft built a supercomputer with 285,000 CPU codes and 10,000 Nvidia V100 GPUs [[https://news.microsoft.com/source/features/innovation/openai-azure-supercomputer/|exclusively for OpenAI]], hosted in Azure.  Researchers calculated that to train GPT-3 OpenAI could have needed 34 days using 1024 A100 GPUs costing $5M just in compute time based on 175 billion parameters if the A100 had been available at the time. GPT-3 175B model: Microsoft built a supercomputer with 285,000 CPU codes and 10,000 Nvidia V100 GPUs [[https://news.microsoft.com/source/features/innovation/openai-azure-supercomputer/|exclusively for OpenAI]], hosted in Azure.  Researchers calculated that to train GPT-3 OpenAI could have needed 34 days using 1024 A100 GPUs costing $5M just in compute time based on 175 billion parameters if the A100 had been available at the time.
  
 +Llama 3.1 used 16,000 Nvidia H100 GPUs to train the [[https://venturebeat.com/ai/meta-unleashes-its-most-powerful-ai-model-llama-3-1-with-405b-parameters/|405B model]]. 
 +
 +===== Evaluation =====
  
 +https://www.philschmid.de/llm-evaluation
ai/generalinfo.1714057313.txt.gz · Last modified: by Wulf Rajek