NEWVectors or files. Pick a path.Start →
    Models/Image Text To Text/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2

    Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2

    by Jackrong

    131Kdl/month
    122likes
    Identifier
    Model ID
    Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2

    Tags

    safetensorsqwen3_5unslothqwenqwen3.5reasoningchain-of-thoughtloraimage-text-to-textconversationalenzhkodataset:nohurry/Opus-4.6-Reasoning-3000x-filtereddataset:Jackrong/Qwen3.5-reasoning-700xdataset:Roman1111111/claude-opus-4.6-10000xbase_model:Qwen/Qwen3.5-27Bbase_model:adapter:Qwen/Qwen3.5-27Blicense:apache-2.0region:us

    Use Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2 on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

    Open Studio

    How It Runs on Mixpeek

    On Mixpeek, Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2 runs as a managed extractor inside a processing pipeline. Point a bucket of image text to text data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.