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DeepSeek V4 Flash gains basic vision via connector training

A user has successfully integrated basic vision capabilities into the DeepSeek V4 Flash language model without retraining the core model. By training a separate 40.1 million parameter connector on 100,000 image-text examples, the user enabled the model to process and respond to image prompts. This approach froze both the DeepSeek V4 Flash and a MoonViT image encoder, focusing all learning on the new connector module. The resulting NVFP4 model demonstrates rudimentary vision, though it is noted as a pilot and not yet production-ready. AI

IMPACT Demonstrates a method for adding vision to text-only LLMs without full retraining, potentially lowering the barrier for multimodal model development.

RANK_REASON User-developed modification of an existing model, not a direct release from a frontier lab. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepSeek V4 Flash gains basic vision via connector training

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/ButtercupLyn100 ·

    I gave DeepSeek V4 Flash basic vision by training a 40M connector on 100K examples

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vl6ior/i_gave_deepseek_v4_flash_basic_vision_by_training/"> <img alt="I gave DeepSeek V4 Flash basic vision by training a 40M connector on 100K examples" src="https://preview.redd.it/9qnqst7s0oih1.png?width=1…