Results for “mqtt”

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Build, optimize, and execute quantum circuits on IBM Quantum hardware or local simulators using Qiskit, including transpilation, primitives, and algorithm libraries.

Build and execute quantum circuits on IBM Quantum hardware, simulators, and third-party providers using the Qiskit framework.

Build, optimize, and execute quantum circuits on IBM Quantum hardware or local simulators using Qiskit, including transpilation, primitives, and algorithm libraries.

Build, optimize, and execute quantum circuits using Qiskit on simulators or real quantum hardware from IBM, IonQ, and Amazon Braket.

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

Quantize large language models to 4-bit precision using activation-aware weight quantization, reducing memory footprint and speeding up inference with minimal accuracy loss.

Search local knowledge bases, notes, docs, and meeting transcripts with hybrid retrieval combining BM25, vector search, and LLM reranking, all running on-device.

Routing guide for the built-in QwenWork Connector tools (mcp__qw-builtin__qw_query / mcp__qw-builtin__qw_action). Load ONLY right before calling them to view or manage QwenWork's OWN tasks/sessions or app configuration, or when a qw tool result explicitly asks. Before loading, always check whether another skill or tool can do the job — if so, use that instead. Unless the user explicitly asks, never use it to view skills, plugins, MCP servers, or third-party connectors. Never load in any non-essential scenario (content creation, PPT/docs, coding, research, web tasks), even when the topic is QwenWork itself. If in doubt, do not load.

Quantize large language models to 4-bit with minimal accuracy loss using GPTQ, enabling deployment of 70B+ models on consumer GPUs with 4× memory reduction and 3-4× faster inference.

Quantize LLMs to 8/4/3/2/1-bit precision without calibration data, using multiple backends and HuggingFace/vLLM integration.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.