AgentSkillsCN

model

具备算法与模型开发及调优技能。适用于数据集设计与清洗、监督微调（SFT）、偏好优化（DPO/RLHF理念）、LoRA/QLoRA模型训练、训练配置优化、离线与在线评估、安全性检测、部署打包，以及成本与性能之间的权衡取舍等任务。

SKILL.md
--- frontmatter
name: model
description: Algorithm/model development and fine-tuning skill. Use for tasks like dataset design/cleaning, supervised fine-tuning (SFT), preference optimization (DPO/RLHF concepts), LoRA/QLoRA, training configs, evaluation (offline/online), safety checks, deployment packaging, and cost/performance trade-offs.

model

Use this skill for 算法/模型开发/模型微调：从数据到训练到评测再到上线。

Defaults / assumptions to confirm

  • •Goal: improve quality, reduce cost/latency, add domain knowledge, safety alignment?
  • •Base model and license constraints
  • •Hardware: local GPU / multi-GPU / cloud
  • •Target inference stack (vLLM, TGI, llama.cpp, etc.)

Workflow

  1. •Define the objective and success metrics
  • •Task definition and input/output format.
  • •Primary metrics (task-specific) + guardrails (safety, latency, cost).
  • •Failure analysis categories (hallucination, format errors, refusal, toxicity).
  1. •Data strategy (most important)
  • •Collect/curate dataset; define labeling guidelines.
  • •Remove duplicates, leakage, PII, and near-duplicates.
  • •Balance by scenario; ensure coverage of edge cases.
  • •Split train/val/test with strict leakage prevention.
  1. •Choose training approach
  • •SFT for instruction following and domain formatting.
  • •LoRA/QLoRA for efficient fine-tuning (default for most cases).
  • •DPO/Preference tuning when “style/quality preference” is the target.
  • •Avoid fine-tuning when RAG or prompting solves it cheaper.
  1. •Training setup
  • •Pick tokenizer/model family compatibility.
  • •Hyperparameters: LR, batch size, sequence length, warmup, weight decay.
  • •Checkpoints and resume strategy; deterministic seeds.
  • •Track experiments (configs, metrics, artifacts).
  1. •Evaluation
  • •Offline eval set: small but representative; include hard negatives.
  • •Automatic metrics where meaningful; human eval for subjective qualities.
  • •Regression tests: keep a fixed “golden set” across iterations.
  1. •Safety & compliance
  • •Filter sensitive data; define refusal policy and tests.
  • •Measure unsafe outputs; create adversarial eval prompts.
  1. •Deployment
  • •Export adapters/merged weights; document inference requirements.
  • •Quantization plan if needed; benchmark latency and throughput.
  • •Monitor in production: quality signals, drift, safety incidents.

Outputs

  • •Data spec: sources, schema, labeling rules, splits.
  • •Training plan: method (SFT/LoRA/DPO), configs, compute estimate.
  • •Eval plan: datasets, metrics, sampling, acceptance thresholds.
  • •Deployment plan: packaging, quantization, benchmarks, monitoring.