CLIP… But For Decisions (Contrastive Language Models Explained)
CLM is a new System 1 model from Stanford and NVIDIA Research that works like CLIP for decisions, embedding the situation and every possible action into one space and picking the closest match instead of generating tokens. I break down how it's trained on a frozen 8B backbone with only small heads learning, then run it on a DGX Spark across 1,080 tools, where latency stayed around 80ms but accuracy fell from 86% with 8 tools to 17% with all of them. I also show where it fits today: as a fast first-stage shortlist before a model that can compare options side by side. Resources: - CLM-8B: https://contrastive-lm.notion.site/ - 📄 CLM blog: https://contrastive-lm.notion.site - 💻 CLM repo (code + playground): https://github.com/Contrastive-LM/CLM - 🤗 Model weights: https://huggingface.co/Contrastive-LM/CLM-v0.1-8B - Jev launch post (TypeSafe): https://typesafe.ai/blog/introducing-system-one-models-and-jev - Thinking, Fast and Slow by Daniel Kahneman #AI #LLM #ReasoningModels #system1 My voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 00:00 System 1 Returns 01:49 What Is a CLM 03:47 Contrastive Training Basics 04:58 Why It’s So Fast 07:32 Three-Stage Training Recipe 09:54 DGX Spark Demos 13:07 Limits and Best Uses
