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reacted to DedeProGames's post with 👍 about 12 hours ago
Hi everyone! I want to share a few updates about GRM development. *GRM-3.4 has been canceled* Although GRM-3.4 showed strong reasoning and coding capabilities, testing revealed several issues. It tended to overthink, and its code often contained errors that GRM-3.2-Sky handled correctly. Its tool use also needed improvement, and its terminal performance did not reach the level we were aiming for. We repeatedly saw it rewrite files from scratch instead of using editing tools, and use shell commands to create or modify files when dedicated tools were available. *Looking ahead to GRM-3.6* GRM-3.6 is a possible next generation for the GRM models. We are still deciding whether it will be a single large model or a complete model family. The flagship is set to be **GRM-3.6-Moon**, a **35B-A3B** model. *Experimental models* Orion LLM Labs may release experimental models more regularly from now on. These experiments will help us research and test ideas for future GRM models. Thanks for following the project!
repliedto DedeProGames's post about 12 hours ago
Hi everyone! I want to share a few updates about GRM development. *GRM-3.4 has been canceled* Although GRM-3.4 showed strong reasoning and coding capabilities, testing revealed several issues. It tended to overthink, and its code often contained errors that GRM-3.2-Sky handled correctly. Its tool use also needed improvement, and its terminal performance did not reach the level we were aiming for. We repeatedly saw it rewrite files from scratch instead of using editing tools, and use shell commands to create or modify files when dedicated tools were available. *Looking ahead to GRM-3.6* GRM-3.6 is a possible next generation for the GRM models. We are still deciding whether it will be a single large model or a complete model family. The flagship is set to be **GRM-3.6-Moon**, a **35B-A3B** model. *Experimental models* Orion LLM Labs may release experimental models more regularly from now on. These experiments will help us research and test ideas for future GRM models. Thanks for following the project!
reacted to crp4222's post with 👍 about 12 hours ago
I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm. It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds. On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings. It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: https://huggingface.co/crp4222/privaite-pii-minilm
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