That’s the killer feature. I always loved the idea to have a chat with an ai just like in Star Trek. Very curious. Could be a game changer or a fail. Depends on how good the ai works. It all depends on software running flawlessly
LLMS
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Fine-tune LLMs on Commodity GPUs with Ludwig Framework
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Fine-tuning #LLMs doesn't always require $$$ GPUs. Ludwig #opensource framework for building custom AI models makes it easy to efficiently fine-tune LLMs for any task Tutorial to fine-tune #Llama2 using #CodeAlpaca data for code gen on commodity GPUs.
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OpenAI DevDay: GPT-4 Turbo, Assistants API, Vision Capabilities
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Incredibly proud of the @OpenAI team for all the product launches on DevDay. Here’s a roundup of what’s new for developers: GPT-4 Turbo with 128K context and lower prices, the new Assistants API, Vision capabilities, DALL·E 3 API, TTS API, and more! https://
openai.com/blog/new-model
s-and-developer-products-announced-at-devday
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ChatGPT Web Browsing Reluctance Issues Remain Unresolved
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ChatGPT web browsing is still extremely reluctant to actually browse the web…
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Prompt Engineering Techniques for Consistent LLM Results
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Use prompt engineering to tailor your LLMS to your needs. Use reliable system prompts, few-shot prompting, or chain-of-thought prompting for desirable, consistent LLM results.
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Mastering LLM Output Control and Model Parameter Tuning
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That's just the tip of the iceberg! To dig deeper into controlling your LLM outputs and optimally tuning your model's parameters, expand your knowledge with this newsletter iteration: https://
louisbouchard.substack.com/p/our-ai-tutor
-is-live
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Stop Sequences for Controlling LLM Output Length and Structure
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Stop sequences aid in managing the length and structure of model-generated text, useful when prompts require reasoning and referencing. This is key in the React framework where LLMs tackle simpler tasks sequentially to answer complex prompts.
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Model Behavior Optimization Through Examples and Parameter Tuning
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Identify your model's desired behavior. Create user-model interaction examples to assess its performance and identify improvement areas. Enhancements can be achieved through adjusting decoding parameters.
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Temperature Parameter: Controlling Randomness and Creativity in AI Models
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Temperature in a model dictates randomness: below 1 yields deterministic predictions, above 1 increases variability. Lower temperatures aid summarization, while higher ones enhance creativity.
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Controlling LLM Outputs: Techniques to Improve Performance
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Using Large Language Models like GPT-4 and Llama 2 may be challenging, but with proper techniques, their outputs can be controlled effectively. A variety of measures exist to improve these models' performance.