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Together enhances model deployment with adapter comparison and RL-tuned infrastructure

By PulseAugur Editorial · [2 sources] ·

Together has announced new features for its inference and open-source model deployment platform. The platform now allows users to compare adapter runs side-by-side and deploy the best performing ones. This update includes support for models from Meta, Z.ai, Moonshot, and Qwen, with more to be added. The underlying infrastructure features optimized GPU setups and rollout engines tuned for reinforcement learning, ensuring consistent quality from training to deployment. AI

IMPACT Together's platform updates aim to streamline the deployment of open-source models, potentially accelerating their adoption by providing better tools for comparison and optimized infrastructure.

RANK_REASON The cluster describes updates to an existing inference and deployment platform, not a novel model release or core research.

Read on X — Together (inference / OSS) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Together enhances model deployment with adapter comparison and RL-tuned infrastructure

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Tool
The cluster describes updates to an existing inference and deployment platform, not a novel model release or core research.
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COVERAGE [2]

  1. X — Together (inference / OSS) TIER_1 English(EN) · togethercompute ·

    When you have a few good runs, compare the adapters side by side on one endpoint. Then deploy the best one on Together, or download the weights and run it anywh

    When you have a few good runs, compare the adapters side by side on one endpoint. Then deploy the best one on Together, or download the weights and run it anywhere. Available today on open models from Meta, https://t.co/CXob9kQ0Xj, Moonshot and Qwen, with more on the way. https:…

  2. X — Together (inference / OSS) TIER_1 English(EN) · togethercompute ·

    Under the hood, every model gets a GPU setup we’ve already tested and rollout engines tuned for RL.

    Under the hood, every model gets a GPU setup we’ve already tested and rollout engines tuned for RL. We also keep training and generation in sync, so the quality you see in training holds up when you serve the model. https://t.co/DJgNpqvMug

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