slime
49 posts
@slime_framework
The LLM post-training framework for RL Scaling. https://t.co/4ILpx8hfKN
Today, we are thrilled to officially launch RadixArk with $100M in Seed funding at a $400M valuation. The round was led by @Accel and co-led by @sparkcapital. RadixArk exists to make frontier AI infrastructure open and accessible to everyone. Today, the systems behind the most capable AI models are concentrated in a small number of companies. As a result, most AI teams are forced to rebuild training and inference stacks from scratch, duplicating the same infrastructure work instead of focusing on new models, products, and ideas. RadixArk was founded to change that. We are building an AI platform that makes it easier for teams to train and serve the best models at scale. RadixArk comes from the open-source community. We started with SGLang, where many of us are core developers and maintainers, and expanded our work to Miles for large-scale RL and post-training. We will continue contributing to both projects and working with the community to make them the strongest open-source infrastructure foundations for frontier AI. We would like to thank our long-term partners, contributors, and the broader SGLang community for believing in this mission. We're also grateful to @Accel and @sparkcapital, NVentures (Venture capital arm of @nvidia), Salience Capital, A&E Investment, @HOFCapital, @walden_catalyst, @AMD, LDVP, WTT Fubon Family, @MediaTek, Vocal Ventures, @Sky9Capital and our angel investors @ibab, @LipBuTan1, Hock Tan, @johnschulman2, @soumithchintala, @lilianweng, @oliveur, @Thom_Wolf, @LiamFedus, @robertnishihara, @ericzelikman, @OfficialLoganK, and @multiply_matrix among others. Thanks for the exclusive interview with @MeghanBobrowsky at @WSJ about our vision.
Training DeepSeek V4 @deepseek_ai at scale? SGLang + Miles is the Day 0 path. @lmsysorg Miles and SGLang enable full-parameter RL training for DSV4 with stability, efficiency, and broad hardware support. ✅ Verified stability - Rollout Routing Replay (R3) and indexer replay (experimental) - Tensor-level validation across the Miles & Megatron mixed-precision training stack - Step-0 train-inference diff: ~0.02–0.03 ✅ Efficient full-parameter RL - DP / TP / SP / EP / PP / CP support - Tilelang attention and indexer kernels - FP8/BF16 rollout and FP8/BF16 training support ✅ Broad hardware support - Verified training on NVIDIA Hopper and Grace Blackwell clusters - Ready for DeepSeek V4 RL from Day 0 This is the exclusive Day 0 path to scale DeepSeek V4 with rock-solid reliability. Full technical docs & setup guide below! 👇 #DeepSeekV4 #SGLang #RL
Introducing GLM-5.1: The Next Level of Open Source - Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo. - Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. Blog: z.ai/blog/glm-5.1 Weights: huggingface.co/zai-org/GLM-5.1 API: docs.z.ai/guides/llm/glm… Coding Plan: z.ai/subscribe Coming to chat.z.ai in the next few days.
Presenting the GLM-5 Technical Report! arxiv.org/abs/2602.15763 After the launch of GLM-5, we’re pulling back the curtain on how it was built. Key innovations include: - DSA Adoption: Significantly reduces training and inference costs while preserving long-context fidelity - Asynchronous RL Infrastructure: Drastically improves post-training efficiency by decoupling generation from training - Agent RL Algorithms: Enables the model to learn from complex, long-horizon interactions more effectively Through these innovations, GLM-5 achieves SOTA performance among open-source models, with particularly strong results in real-world software engineering tasks.
Introducing GLM-5: From Vibe Coding to Agentic Engineering GLM-5 is built for complex systems engineering and long-horizon agentic tasks. Compared to GLM-4.5, it scales from 355B params (32B active) to 744B (40B active), with pre-training data growing from 23T to 28.5T tokens. Try it now: chat.z.ai Weights: huggingface.co/zai-org/GLM-5 Tech Blog: z.ai/blog/glm-5 OpenRouter (Previously Pony Alpha): openrouter.ai/z-ai/glm-5 Rolling out from Coding Plan Max users: z.ai/subscribe
Introducing GLM-4.7-Flash: Your local coding and agentic assistant. Setting a new standard for the 30B class, GLM-4.7-Flash balances high performance with efficiency, making it the perfect lightweight deployment option. Beyond coding, it is also recommended for creative writing, translation, long-context tasks, and roleplay. Weights: huggingface.co/zai-org/GLM-4.… API: docs.z.ai/guides/overvie… - GLM-4.7-Flash: Free (1 concurrency) - GLM-4.7-FlashX: High-Speed and Affordable
GLM-4.7 is featured on Artificial Analysis Intelligence Index, positioned as a leading open-source model.
GLM-4.7 is here! GLM-4.7 surpasses GLM-4.6 with substantial improvements in coding, complex reasoning, and tool usage, setting new open-source SOTA standards. It also boosts performance in chat, creative writing, and role-play scenarios. Default Model for Coding Plan: z.ai/subscribe Try it now: chat.z.ai Weights: huggingface.co/zai-org/GLM-4.7 Tech Blog: z.ai/blog/glm-4.7
