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LLMS
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Python ML MLOps CV NLP LLMs Daily Tutorials
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If you are interested in: – Python – Machine Learning – MLOps – CV/NLP – LLMs Find me → @Sumanth_077 Everyday, I share tutorials on above topics! Like/RT the first tweet to help this reach more people!
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Alpaca Format Model Getting Chat-Tuned Version Soon
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Looks like Alpaca format 🙁 But I can tell you that a chat-tuned version will hit, for sure… the model is so good
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Mixtral 8x22B Function Calling Now Officially Working
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Officially have function calling working with base Mixtral 8x22B. This model is insanely powerful, especially with few-shot.
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Prompt Engineering Techniques for PaLM-2 and GPT-4
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Of course, if it is PaLM-2, you want to start by asking the AI to "take a deep breath." Or just show GPT-4 a couple of pictures to get started.
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Mixtral 8x22B: Use Examples Instead of Instructions
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FYI for anyone trying to get the most out of Mixtral 8x22B: don't try to prompt it like you prompt other models. It's a base model, not a tuned model. It isn't trained to follow instructions. Instead, provide examples of the behavior you're looking for. Far better results.
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Rerank 3 Achieves 2-3x Speed Improvement in Inference Performance
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Rerank 3 is extremely efficient, offering state-of-the-art throughput with a 2-3x improvement in inference speed compared to prior models. We understand that in many business domains, such as customer support, quality results need to be delivered quickly.
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Rerank 3 Improves RAG Efficiency While Reducing Costs Significantly
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An increasingly common approach for RAG is using generative LLMs to prioritize information from large sets of documents. Rerank 3 provides a better solution, outperforms on ranking accuracy while being between 90-98% less expensive.
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Rerank 3 Expands Context Length to 4K Tokens
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Rerank 3 boasts a 4k context length. This enables customers to pass longer documents to our model, allowing more context from the document to be considered when determining a relevance score (and reducing the need for chunking).
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Rerank 3: Foundation Model for Enterprise Search and RAG
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Introducing Rerank 3: our newest foundation model purpose built to enhance enterprise search and Retrieval Augmented Generation (RAG) systems, enabling accurate retrieval of multi-aspect and semi-structured data in 100+ languages.