Results for “lfq”

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.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.

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 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends and integrations with HuggingFace Transformers, vLLM, and PEFT/LoRA.

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

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.

Control LG smart appliances via ThinQ API. Use when user asks about their fridge, washer, dryer, AC, or other LG appliances. Supports checking status, changing temperature, toggling modes (express, eco), and monitoring door status.

Expert guidance for Groq, the LLM inference platform that provides the fastest token generation speeds available, powered by custom LPU (Language Processing Unit) hardware. Helps developers integrate Groq's API for real-time AI applications where latency matters — chatbots, code completion, and streaming responses.

Foundational SPARQL patterns covering RDF querying, triple patterns, graph patterns, and semantic web fundamentals. Use when querying RDF data or working with knowledge graphs. This is the entry point for SPARQL development.

Foundational GraphQL patterns covering schema design, queries, mutations, subscriptions, and resolvers. Use when building or consuming GraphQL APIs. This is the entry point for GraphQL development.

Reads related records across multiple sObjects in one GraphQL request, paginates related lists with cursors, and replaces overlapping @wire(getRecord) calls with a single cache-shared query.

Orchestrates a complete bulk RNA-seq differential-expression study from raw FASTQ reads through QC, alignment, quantification, differential expression, pathway enrichment, and publication figures.

Master modern GraphQL with federation, performance optimization, and enterprise security. Build scalable schemas, implement advanced caching, and design real-time systems. Use PROACTIVELY for GraphQL architecture or performance optimization.

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

KV cache compression for LLM inference — 4.4x compression, 2x context capacity, near-lossless quality. ICLR 2026 paper implementation with vLLM integration.

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.

Use this skill when the user asks about DataQ — FMCSA's data review system at dataqs.fmcsa.dot.gov — for disputing inspection violations, crash records, or other entries that appear in a carrier's CSA / SMS score. Covers Request for Data Review (RDR) process, success rates, common dispute grounds, what evidence to attach, timeline expectations, and how successful disputes reduce BSI (BASIC Severity Indicator) scores. Cite 49 CFR 392.7 and the FMCSA DataQs User Guide.

Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

Use this skill to evaluate which DataQ challenges have winning evidence and which don't. Covers documented vs anecdotal evidence and the 8 high-success patterns.

LinQR API skill. Use when working with LinQR for qrcode, batch, images. Covers 14 endpoints.

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

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.

GraphQL query language and runtime for APIs enabling clients to request exactly the data they need with strongly-typed schemas and single endpoint architecture.

Use when doing lightweight FASTA/FASTQ transformations such as conversion, subsampling, subsequence extraction, trimming, or quick QC with seqtk.

QA cycling workflow - test, verify, fix, repeat until goal met

Evaluates the faithfulness of abstractive summaries by generating questions from summary sentences and verifying if the answers can be extracted from the source document, reporting Pearson and Spearman correlations with human judgments.

Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.

Perform coverage-guided fuzzing of compiled binaries using AFL++ to discover memory corruption, crashes, and security vulnerabilities.

用户咨询腾讯云产品资源、AWS、阿里云等多云资源时，查看智能顾问架构图、架构目录、架构详情、架构评估结果、绘制架构图、开通智能顾问时、AI智能巡检、AI容量监测、AI混沌演练、AI云诊断、主动预警、架构健康度、云运维问答、云资源查询、云成本优化、安全合规、云资源盘点、闲置资源检查、云产品最佳实践等AIOps、ChatOps、CloudOps操作时使用。

Build and run custom fuzzers with LibAFL, a modular Rust fuzzing library, including drop-in libFuzzer replacement and custom component configuration.

You are an expert in urql, the highly customizable and lightweight GraphQL client for React, Vue, Svelte, and vanilla JavaScript. You help developers fetch GraphQL data with minimal bundle size, document caching, normalized caching via Graphcache, exchanges (middleware pipeline), subscriptions, and offline support — providing a leaner alternative to Apollo Client with better extensibility.

Use RWKV, a linear-time RNN-Transformer hybrid, for efficient long-context inference and training with constant memory usage.