Hugging Face Production Support — Fix Live LLM, Fine-Tuning & Serving Issues Fast

CUDA out-of-memory during training and inference, tokenizer/model config and vocab mismatches, safetensors/checkpoint loading errors, LoRA adapter loading and merging failures, quantization dtype and device_map errors, gated-model 401/403 and token-scope problems, Inference Endpoint cold starts and autoscaling, slow tokens/sec and high TTFT, poor RAG retrieval and embedding-dimension mismatch, vLLM/TGI serving errors, and runaway GPU cost. We work the incident live until the system is stable.

Usually within the same working session. Message us on WhatsApp with the symptoms, the library or endpoint, and the stack trace or error, and we assign an engineer who has handled that class of incident before. For active outages we prioritize immediate response.

Yes. RAG is a core focus — chunking and embedding issues, embedding-dimension mismatch after a model change, cross-encoder reranking, hybrid search tuning, vector-database configuration, and answers that are wrong or hallucinated. We diagnose and fix answer-quality and reliability issues end to end, including the TEI and Sentence Transformers layers.

Yes. Cost blowups are a common trigger. We help with quantization (4-bit/8-bit, GPTQ/AWQ), right-sizing endpoints and batch sizes, continuous batching and KV-cache tuning on vLLM, choosing between Inference Providers, dedicated Endpoints and self-hosting, and cutting redundant retries so the bill comes back under control without breaking the workload.

Absolutely. Every engagement is confidential, NDAs are available on request, and we never access your accounts or infrastructure without your explicit direction. We document the root cause and fix so your team can prevent a repeat.