vllm

vLLM multimodal inference mismatch between image and text

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Problem Description During multimodal inference on an on‑premises server, a custom‑trained vision‑language model integrated with vLLM produces captions and answers that do not correspond to the supplied image. The generated text is either generic (e.g., “a person standing in a room”) or completely unrelated to the visual content. The failure is reproducible across different images … Read more

RAG answer extraction fails during canary deployment in vLLM

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Problem: RAG Answer Extraction Fails During Canary Deployment in vLLM During a canary rollout of a new vLLM pod (10 % of traffic) the downstream RAG post‑processor intermittently returns an empty string or raises RAGExtractorError. The symptom is observed as incomplete or incorrect answers returned to the client, while the baseline version continues to work. Typical … Read more

RAG retrieval empty results with vLLM in hybrid cloud

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Problem Description In a hybrid‑cloud deployment of a Retrieval‑Augmented Generation (RAG) pipeline that uses vLLM for inference, the retrieval step consistently returns an empty list or null despite: Valid user queries arriving at the API endpoint. Document embeddings successfully indexed in the vector store (e.g., Milvus or Pinecone). No errors reported by the vLLM server … Read more

vLLM text image modality mismatch after GPU passthrough

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Problem – Text‑Image Modality Mismatch after GPU Passthrough When running a multimodal model (e.g., llava‑1.5‑7b) inside a Docker container that uses the NVIDIA Container Toolkit, the generated responses contain text that is unrelated to the supplied image. The model appears to process the textual prompt correctly but ignores the visual content, resulting in incoherent or … Read more