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Long Context Fine-Tuning: A Technical Deep Dive

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The landscape of Large Language Models (LLMs) is rapidly evolving, with context lengths expanding from a few thousand tokens a year ago to millions of tokens now.

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Together Fine-Tuning Platform, Now With Preference Optimization and Continued TrainingTogether Long context retrieval models with Monarch MixerTogether Preparing for the era of 32K context: Early learnings and explorationsTogether Hyena Hierarchy: Towards larger convolutional language modelsTogether Kimi K3: the complete developer guideTogether How to choose the right open model for productionTogether Automate Audio Transcription with Zapier + DeepgramDeepgram DevSecOps: Why Security Shouldn’t be Sacrificed for SpeedThenewstack How We Eliminated Long-Lived CI Secrets Across 70+ ReposPulumi Go Concurrency Patterns: Context - The Go Programming LanguageGo How Ramp automated receipt processing with fine-tuned LLMsModal SQL is Dead, Long Live SQL: Engineering a Reliable Analytics Agent from ScratchMotherduck Cerebras Systems Enables GPU-Impossible™ Long Sequence Lengths Improving Accuracy in NaturalCerebras The Livecycle Docker Extension: Instantly Share Changes and Get Feedback in ContextDocker Rolling out a new featureVercel You can just ship agentsVercel 6 tips every developer should know when using Cursor and Windsurf AIMintlify From Hawking to Siri: The Evolution of Speech SynthesisDeepgram What You Need To Know About OpenAI’s New 3D Model-Making AI, Point-E - Deepgram Blog ⚡️Deepgram Breakdown: The Kubernetes-Run AI Video Generation Pipeline for NIUS.TVThenewstack