Announcing new visual evaluation comparisons in W&B Weave. Easily compare model performance, latency, and token usage in a comprehensive view. Watch a lead engineer from Weights & Biases demo the feature: https://lnkd.in/gQw__Pjm
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Weights & Biases is doing great things in the AIML space. With all the hype and attention on GenAI it’s easy to forget that Tree and neural net models are still doing the bulk of day to day value addition in production systems.
Announcing new visual evaluation comparisons in W&B Weave. Easily compare model performance, latency, and token usage in a comprehensive view. Watch a lead engineer from Weights & Biases demo the feature: https://lnkd.in/gQw__Pjm
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Apples to apples evaluation comparisons. No more "vibes" based deployment decisions - quickly set up flexible evaluation dashboards, enabling true data driven deployment for your LLM powered products. #llm #genai #ai #mlops #ml #llmops #modelevaluation
Announcing new visual evaluation comparisons in W&B Weave. Easily compare model performance, latency, and token usage in a comprehensive view. Watch a lead engineer from Weights & Biases demo the feature: https://lnkd.in/gQw__Pjm
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For those interested in going a little bit deeper in the technical side, here is a blog explaining how to debug transaction MD04. https://lnkd.in/eY-Qf5xZ
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🚀Day206 of #300daysofdata 🧠 Today I learned about the KV cache techniques in detail with implementation which is one of the ways for reducing inference time in LLM by reducing subsequent token generation time. 💡 In simple words during token generation past key values is also returned with token id then the token id with past key values is passed to model as input which reduced redundant calculation of key values and include some concatenations only. 📚Resources : https://lnkd.in/djeMANqj 📚 github : https://lnkd.in/d_THQm9V 😁 Check out the decrease in subsequent token generation time represented by yellow for KV cache.
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It's time to talk about closures 👨🎓 Closures in Swift are a convenient and powerful tool for handling functions as objects. One of the important aspects when working with closures is their ability to capture values and use them within their context. This property, called "value capture," plays a key role in creating flexible and efficient functions. Closures in Swift capture values and references to variables from the surrounding context in which they were created. This occurs when the closure uses these values within its body. 🙂 Capturing strong references: A closure captures and retains a reference to an object, preventing it from being deallocated from memory as long as the closure exists. 🙃 Capturing weak references: A closure creates a weak reference to an object, allowing the object to be deallocated from memory if no strong references to it exist anymore.
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We’ve all ran into a segmentation fault once, haven’t we? Here is a quick check of what it is and how it is handled. A Segmentation Fault is an error that occurs when a program tries to access a memory location it does not have permission to access. It's a mechanism that prevents you from corrupting the memory and adding hard-to-debug memory bugs. It also occurs if you are using dynamic memory allocation, and you access an address that is already freed. The most cases where I see it happening in embedded systems is during a stack overflow. With limited memory configs, in extreme scenarios when the allocated stack space is exhausted and the code uses memory beyond the allocated stack space. Finding the root cause for segmentation fault is very important. If it occurs for running out of stack space, it can be fixed simply by increasing the allocation whereas something like dereferencing an uninitialized pointer or accessing freed memory may require deeper dig into the code.
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Today I've just learned that 2 different RAM doesn't give of the quality of transferring data as well as dual RAM's. The reason of that would be the difference of the frequencies in the singular RAM but on the other hand dual RAM's also have the same frequency so transferring data would be easier and faster.
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While the amount of tokens per second (T/s) is an important metric when determining the inference performance of an LLM, the inference time also consists of the following metrics: - TTFT: Time to first token - Latency/Total Time: Measures the delay/total time between sending a request and receiving a response (from request to final token) Here's a leaderboard highlighting the inference time benchmark of all LLMs https://thefastest.ai/
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Read-write and read-only transaction routing with Spring https://lnkd.in/dhDYrwuK
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