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SkillsMP は ProfSynapse/Synaptic-Tuner から 9 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 9 件中 9 件を表示しています。
Complete reference for the fine-tuning pipeline (SFT, KTO, GRPO), cloud HF Jobs workflows, autonomous experiment search, checkpoint evaluation, and LoRA surgery. Covers training CLI flags, YAML configuration, model presets, dataset requirements, LoRA…
原文の言語: 英語
Turn local agent transcripts (Claude Code, Codex CLI, or any format with an adapter) into high-quality fine-tuning rows. Use when someone wants to mine their own coding-agent conversation logs into an SFT/KTO/DPO/GRPO dataset — parse the logs, extract…
原文の言語: 英語
Complete reference for model upload and deployment. Covers HuggingFace upload, save strategies (LoRA, merged 16-bit, merged 4-bit), GGUF conversion, model merging, model cards, and the full upload workflow. Use when uploading models, creating GGUF files,…
原文の言語: 英語
Complete reference for the SynthChat synthetic dataset generation system. Covers CLI commands (generate, improve, validate), scenario YAML authoring, rubric YAML authoring, settings configuration, evaluation, and full workflow. Use when generating datasets,…
原文の言語: 英語
Complete reference for the embedding fine-tuning pipeline (SentenceTransformers bi-encoders) — the `embedding` training method, model registry, dual loader (Unsloth fast path + ST fallback), adapter modes (full/lora/frozen_head), triplet/pairs dataset format,…
原文の言語: 英語
Complete reference for the config-first model evaluation system. Covers the Evaluator CLI, assertion-driven YAML scenarios, response views, backend configuration, presets, scoring, LLM-as-judge, model comparison, and HuggingFace integration. Use when…
原文の言語: 英語
Publish local dataset artifacts to a Hugging Face dataset repo. Use when uploading a JSONL dataset, pushing a filtered dataset variant, syncing a matching .metadata.json sidecar, or renaming a dataset file in the target repo. This skill is about USING the…
原文の言語: 英語
End-to-end case studies showing how to implement the full training pipeline for different skill types. Covers three complete worked examples — tool-calling training, essay-style training, and agentic search (RAG agent) training — demonstrating dataset design,…
原文の言語: 英語
Create structured research notes from experiment runs and analysis artifacts. Use when creating a note at run launch, updating it as training/evaluation/loss stages finish, summarizing a finished run, comparing experiment outcomes, extracting hypotheses from…
原文の言語: 英語