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🚀 Yi-Coder is open-sourced! The 'Small but Mighty' LLM offers SOTA coding performance under 10B parameters. Excel in code editing, completion, debugging, and math reasoning. ✅ 2 sizes: 9B & 1.5B (Chat & Base) ✅ 128K context length ✅ Support 52 programming languages Explore it now👇 #YiCoder #01AI #LLMs #OpenSource

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Yi-01.AI

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57,164 просмотров • 1 год назад •via X (Twitter)

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(Z.ai) is now a model provider inside TRAE. You can BYOK and add GLM-4.6 and GLM-4.5 to TRAE IDE. GLM-4.5 supports up to 128K context and GLM-4.6 expands up to 200K context. Both deliver strong coding and reasoning performance on the benchmark and offers dual “thinking/fast” modes. Available now in the latest version.
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(Z.ai) is now a model provider inside TRAE. You can BYOK and add GLM-4.6 and GLM-4.5 to TRAE IDE. GLM-4.5 supports up to 128K context and GLM-4.6 expands up to 200K context. Both deliver strong coding and reasoning performance on the benchmark and offers dual “thinking/fast” modes. Available now in the latest version.

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🚀 Introducing Code Experiment Mode in MicroGPT! 🧪✨ Test multiple variations of your code instantly! 🔄 ✅ AI generates different function versions ✅ Compare outputs & optimize performance ✅ Supercharge your coding workflow

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Google just open-sourced LangExtract Python library! It uses LLMs to extract entities, attributes, and relations—with exact source grounding—from unstructured documents. Flexible LLM support (Gemini, OpenAI, Ollama) 100% open-source.
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Google just open-sourced LangExtract Python library! It uses LLMs to extract entities, attributes, and relations—with exact source grounding—from unstructured documents. Flexible LLM support (Gemini, OpenAI, Ollama) 100% open-source.

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Our AI financial agent now shows its thinking Much like how Perplexity shows its tasks Here is how it works: • we have two LLMs • LLM #1 receives raw user query • LLM #1 creates tasks to answer query • LLM #2 receives these tasks • LLM #2 uses tools to execute tasks • LLM #2 streams answer to you We can add a 3rd LLM to do the last step. This would let LLM #2 focus only on task execution. I will add this in a follow-up. The code is open source so you can see how everything is done.

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