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reacted to AdinaY's post with šŸ‘€ about 10 hours ago
Skywork-Reward-V2šŸ”„ Reward models by Skywork AI. https://huggingface.co/collections/Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84 āœØ 0.6B - 8B āœØ Trained on 26M human-LLM preference pairs āœØ 0.6B > 27B in many tasks
reacted to YerbaPage's post with šŸ‘€ about 10 hours ago
How to achieve 100% Pass Rate on HumanEval ? šŸ”„ Meet MGDebugger if you are tired of LLMs failing on complex bugs šŸ¤” Our MGDebugger, just hit 100% accuracy on HumanEval using the DeepSeek-R1 model. šŸš€ āœØ Demo: https://huggingface.co/spaces/learnmlf/MGDebugger šŸ“ Paper: https://huggingface.co/papers/2410.01215 šŸ’» Code: https://github.com/YerbaPage/MGDebugger HumanEval may be retired, we're ready for the next challenge In more complex scenarios! You may also take look at this repo for a collection of awesome repo-level coding tasks! šŸ–„ļø https://github.com/YerbaPage/Awesome-Repo-Level-Code-Generation
reacted to mlabonne's post with šŸ‘ about 10 hours ago
https://huggingface.co/LiquidAI open-sources a new generation of edge LLMs! šŸ„³ Based on a new hybrid architecture, these 350M, 700M, and 1.2B models are both fast and performant, ideal for on-device deployment. I recommend fine-tuning them to power your next edge application. We already provide Colab notebooks to guide you. More to come soon! šŸ“ Blog post: https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models šŸ¤— Models: https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38
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