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also reimplement from scratch HNSWs (you can see my implementation in hnsw.c), because that was going to be my core data structure, and I didn’t want to grab some random code from GitHub and be happy with it. However, as I started reading the papers, I started to understand that a few pieces were missing. So, as I did in the past with HyperLogLog, where I had to fill a few gaps (here: https://antirez.com/news/75 ), there was already some new algorithmic challenges. Especially I wanted two things

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iv preprint arXiv:2502.03461. [link]; Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023). Swe-bench: Can language models resolve real-world github issues? arXiv preprint arXiv:2310.06770. [link]; Chowdhury, N., Aung, J., Shern, C. J., Jaffe, O., Sherburn, D., Starace, G., Mays, E., Dias, R., Aljubeh, M., Glaese, M., Jimenez, C. E., Yang, J., Liu, K., & Madry, A. (2024). Introducing swe-bench verified. OpenAI. [link]; Dunlap, L., Mandal, K., Darrell, T., St

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ons seeking to build, test, and deploy LLM applications at scale without sacrificing performance or simplicity. If you’re as excited as we are, don’t wait! Clone the repo: https://github.com/vllm-project/production-stack; Kick the tires; Let us know what you think!; Interest Form Join us to build a future where every application can harness the power of LLM inference—reliably, at scale, and without breaking a sweat. Happy deploying! What AIBrix adds as a Kubernetes control plane for vLLM: LoRA m

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her Devin sessions start in existing or new Slack threads, whether Devin waits for you to approve its plan, and more in Settings Customization. Devin can send Slack updates on its Github activity. Configure the channel these updates are sent to in Settings Integrations. Share Devin sessions you start in the web app to Slack. You can now change the default channel. Configure + monitor Devin’s machine If you need to increase Devin’s machine size (disk space, RAM, CPU), we’ve added additional optio

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as well as captured detailed metrics for time-to-first-token (TTFT) and time-per-output-token (TPOT), please refer to the appendix for more data and analysis. You can follow this github issue to reproduce our benchmark. Limitation of current optimizations. Although our current optimizations give a significant throughput gain, there are performance trade-offs from our current optimizations, especially from multi-step scheduling: Bumpy inter-token latency: In our current implementation of multi-s

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s a popular open-source framework by Microsoft for building multi-agent applications. For this, example we’ll use the Code Llama model: Install Autogen: Then create a Python script example.py to use Ollama with Autogen: Lastly, run the example to have the assistant write the code to plot a chart: More to come This is initial experimental support for the OpenAI API. Future improvements under consideration include: GitHub issues are welcome! For more information, see the OpenAI compatibility docs.

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GitHub | Documentation | Paper LLMs promise to fundamentally change how we use AI across all industries. However, actually serving these models is challenging and can be surprisingly slow even on expensive hardware. Today we are excited to introduce vLLM, an open-source library for fast LLM inference and serving. vLLM utilizes PagedAttention, our new attention algorithm that effectively manages attention keys and values. vLLM equipped with PagedAttention redefines the new state of the art in LLM

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. Please sign up here if you'd like to help us test it. Here is what alpha users are saying about it: "The first thing me and all my friends noticed was how much faster it is than GitHub Copilot. It is at least 2x faster, maybe 3x. It's a little detail but it makes a big difference."; "It makes web development so much easier. I feel like I'm only writing 50% of the code."; "After using the feature for only week, I can't imagine life without it"; "It's crazy how much faster I can learn new things

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