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Fireworks AI adds Kimi K2.6 model to its training platform

Fireworks AI has announced the integration of Kimi K2.6, a model from Kimi Moonshot, onto its Training Platform. This integration allows users to leverage the Kimi K2.6 model through Fireworks AI's Managed and Training API workflows. The platform supports various training methods including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning (RL), with options for both smart defaults and custom loss functions, all while supporting a 265K context window. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Expands training options for developers using Fireworks AI's platform, enabling fine-tuning of models with large context windows.

RANK_REASON Integration of a specific model (Kimi K2.6) onto an inference platform, enabling new training capabilities.

Read on X — Fireworks (inference infra) →

COVERAGE [2]

  1. X — Fireworks (inference infra) TIER_1 · FireworksAI_HQ ·

    What would you like to see next? Full Param tuning?

    What would you like to see next? Full Param tuning?

  2. X — Fireworks (inference infra) TIER_1 · FireworksAI_HQ ·

    Kimi K2.6 from @Kimi_Moonshot is now available on @FireworksAI_HQ Training Platform across the Managed and Training API workflows.

    Kimi K2.6 from @Kimi_Moonshot is now available on @FireworksAI_HQ Training Platform across the Managed and Training API workflows. Try SFT, DPO, RL with smart defaults or your own custom loss function with industry leading 265K context window. https://t.co/jqKuwWWEB0