'deepseek' 검색 결과

Use when creating a DeepSeek Harness plugin, choosing public names for a new external DSH plugin, validating a dsh-plugin.naming.json manifest, checking reviewed central registrations for known conflicts, or creating a workspace package inside the deepseek-harness repository. Covers the full workflow from repository-mode and plugin-form selection through separate offline naming validation, optional online registry lookup, and package validation. Routes tool, LLM adapter, hook, service, and configuration forms to their corresponding references while separating upstream-monorepo rules from external-package rules. For an existing plugin crossing Harness versions, use this Skill's built-in version-adaptation workflow before implementing against the exact target contract.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when writing or reviewing tests for DeepSeek Harness plugins, external DSH plugin packages, or package changes in the deepseek-harness repository. Select the minimum sufficient levels from unit tests, coverage, real-API end-to-end tests, snapshots, Web tests, real compositions, and built-artifact smoke tests. For Harness version migrations, use this Skill's built-in seven-touchpoint test workflow and validate the exact target-version runtime.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

DeepSeek Harness 开发文档离线 Wiki，检索导航涵盖的插件开发、工具、配置、生命周期、服务、事件、LLM 适配器和 Cordis 教程。用户询问这些开发主题时使用；不用于日常产品使用、部署运维或完整 API 参考。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

公众号写稿｜长文写作｜文章润色｜改写续写 — 公众号长文 AI 写作，从话题或提纲生成完整初稿，支持改写、续写、润色、开头结尾优化，可调 DeepSeek / GPT / Claude 或由 Agent 代写。面向自媒体作者、公众号运营、品牌文案。触发词（**单独触发仅限对已有稿子的修改**）：「改写」「润色」「续写」「续一段」「往下写」「接着这段写」「重写开头」「改结尾」「调整语气」「这段润色下」「把这段改活泼点」「优化用词」「用 GPT 重写」「用 DeepSeek 重写」。新写一篇请走 aws-wechat-article-main（main 内部会调用本 skill 生成初稿）；需要多环节串联（写+审+排+配图+发）也走 main。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build with DeepSeek Coder — open code models with strong reasoning and long-context code understanding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build with DeepSeek's models — efficient open-weight reasoning models and low-cost API inference.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Gate resumption of any multi-day LLM batch-scoring campaign that calls an unpinned model alias (deepseek-chat, gpt-*-latest, gemini-*-preview, any provider alias without a pinned version). Use when: (1) resuming a paused or credit-exhausted scoring run days after its last chunk, (2) topping up credits to finish a campaign, (3) extending a cached scoring pipeline with new items. Prevents silently splicing two model versions or serving revisions into one measure. Verified 2026-07-16: for $0.30 caught a serving-revision drift WITHIN DeepSeek v4-flash (same alias, same family, litigation scores systematically shifted across a 2-day gap) before an $83 resume spend.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

为 OpenClaw 配置硅基流动 (SiliconFlow) 作为模型源。SiliconFlow 是国内领先的 AI 模型推理平台，提供 98+ 个 chat 模型，包含多个免费模型（Qwen3-8B、DeepSeek-R1-8B 等）。使用标准 OpenAI 协议（openai-completions）。包含 provider 注册、模型定义、别名配置、fallback 链接入和验证的完整流程。当管理员说想"加硅基流动"、"配 SiliconFlow"、"接入 SF 模型"、"加 Kimi"、"加 Qwen3"、"加免费模型"、"接入 DeepSeek V3.2"时使用此 skill。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Treinar modelos de Mixture of Experts (MoE) usando DeepSpeed ou HuggingFace. Use ao treinar modelos em larga escala com computação limitada (redução de 5× em custos vs modelos densos), implementar arquiteturas esparsas como Mixtral 8x7B ou DeepSeek-V3, ou escalar capacidade de modelo sem aumento proporcional de computação. Cobre arquiteturas MoE, mecanismos de roteamento, balanceamento de carga, paralelismo de especialistas e otimização de inferência.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

DeepSeek HarnessのWeb版・公式Desktop版の会話や設定を操作する。対象インスタンスを特定し、認証済みHTTP RPCまたはアプリ画面で読取・依頼・追記・中断を行う。既存チャットの操作をheadlessの別実行と取り違えない。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Configure AIsa as a first-class model provider for OpenClaw, enabling production access to major Chinese AI models (Qwen, DeepSeek, Kimi K2.5, Doubao) through official partnerships with Alibaba Cloud, BytePlus, and Moonshot. Use this skill when the user wants to set up Chinese AI models, configure AIsa API access, compare pricing between AIsa and other providers (OpenRouter, Bailian), switch between Qwen/DeepSeek/Kimi models, or troubleshoot AIsa provider configuration in OpenClaw. Also use when the user mentions AISA_API_KEY, asks about Chinese LLM pricing, Kimi K2.5 setup, or needs help with Qwen Key Account setup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

답변 평가를 활용하는 강화 학습으로 대규모 언어 모델을 추가 훈련하는 절차를 안내합니다. Megatron-LM과 SGLang을 결합해 데이터 준비, 병렬 실행, 맞춤형 생성을 다룹니다.

• 언어 모델의 GRPO 훈련 시작
• 답변 생성과 훈련을 동시에 실행
• 도구를 사용하는 에이전트 훈련

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Guides large language model reinforcement learning with miles, covering environment setup, low-precision training, consistency between training and inference, and speculative decoding, where a smaller model proposes tokens for a larger model to verify.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure reinforcement learning for large MoE models
• Investigate instability in FP8 training
• Set up speculative decoding for faster rollout generation

Configure large language model pretraining with torchtitan across multiple GPUs. Covers ways to split training work, launch jobs on computing clusters, enable Float8 numerical processing, and save or resume training through distributed checkpoints.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Pretraining Llama 3.1 8B on eight GPUs
• Configuring multi-node training with SLURM
• Enabling Float8 training on H100 GPUs

Configure large language model training with NVIDIA Megatron-Core across multiple GPUs. This skill helps choose how to split computation, set training parameters, monitor GPU efficiency, and address memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning multi-GPU training for a LLaMA-style model
• Distributing MoE model experts across GPUs
• Tuning training throughput and GPU utilization

Configure large language model pretraining with torchtitan across multiple GPUs. Covers ways to split training work, launch jobs on computing clusters, enable Float8 numerical processing, and save or resume training through distributed checkpoints.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Pretraining Llama 3.1 8B on eight GPUs
• Configuring multi-node training with SLURM
• Enabling Float8 training on H100 GPUs

Configure large language model training across multiple NVIDIA GPUs with Megatron-Core. The skill helps choose how to split models and workloads, set training parameters, monitor performance, and address memory shortages or low GPU utilization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning distributed training for a LLaMA-style model
• Distributing MoE expert networks across GPUs
• Tuning GPU utilization and training throughput

Guides large language model reinforcement learning with miles, including MoE models that route work to specialized components. Covers low-precision training, matching training and inference behavior, and accelerating training response generation with draft models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up reinforcement learning for large MoE models
• Configuring FP8 or INT4 training
• Checking consistency between training and inference

Set up reinforcement learning, training guided by rewards, for language models with slime. The skill covers Megatron-LM training, SGLang response generation, data preparation, asynchronous execution, and custom workflows for agents that use tools across multiple turns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare GRPO training for GLM or Qwen3 models
• Overlap response generation and model training
• Train agents with multi-turn tool use

Manage consumers, credentials, AI providers, and model routing in Higress AI Gateway through its Console API. The skill explains session authentication, access permissions, and how to update existing settings while preserving required version information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create consumers and replace their authentication keys
• Route requests to providers by model name
• Grant workers access to AI routes

Change the language model used by a Worker Agent through the agt command-line tool. The controller resolves model settings, regenerates configuration, and recreates the container to apply the change without a manual restart.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Change a Worker's language model
• Apply registered model settings automatically
• Check whether a model change was accepted