技能档案

LLM Fine-tuning

LoRA, QLoRA, PEFT, RLHF, instruction tuning, evaluation

Machine Learning & AI LLM & Generative AI

角色数

包含此技能的角色

级别数

结构化成长路径

必要要求

其余 5 个可选

Machine Learning & AI

LLM & Generative AI

2026/3/17

选择当前级别并对比期望。下方卡片显示晋升所需掌握的内容。

各级别期望

表格展示从初级到首席的技能深度变化。点击行查看详情。

角色	必要性	描述
Data Scientist		Understands the concept of LLM fine-tuning: full fine-tuning vs parameter-efficient methods. Uses Hugging Face API for fine-tuning small models on custom data. Prepares training data in the correct format for various LLM platforms.
LLM Engineer	必要	Knows LLM fine-tuning basics: full fine-tuning vs LoRA, instruction-tuning data format. Runs basic fine-tuning of a small model via Hugging Face Trainer under mentor guidance.

角色	必要性	描述
Data Scientist		Independently conducts LLM fine-tuning using LoRA, QLoRA, and prefix-tuning. Configures training hyperparameters, monitors loss curves. Evaluates fine-tuned model quality through domain-specific benchmarks and human evaluation.
LLM Engineer	必要	Independently conducts LLM fine-tuning: LoRA/QLoRA, instruction dataset preparation, hyperparameter tuning. Monitors training via W&B, evaluates results on held-out datasets.

角色	必要性	描述
Data Scientist		Designs fine-tuning pipelines for production LLM systems. Applies RLHF, DPO for model alignment. Optimizes training through DeepSpeed, FSDP. Conducts systematic evaluation via automated benchmarks and red-teaming.
LLM Engineer	必要	Designs production fine-tuning pipelines: data curation, multi-stage training (SFT → DPO), distributed fine-tuning. Optimizes LoRA rank, learning rate, and batch size for maximum quality.

角色	必要性	描述
Data Scientist		Defines LLM fine-tuning strategy for the organization. Establishes data preparation, training, and evaluation standards for custom LLMs. Coordinates GPU infrastructure and budgets for LLM experiments.
LLM Engineer	必要	Defines fine-tuning strategy for the LLM team. Establishes best practices for data preparation, training configuration, evaluation. Coordinates fine-tuning experiments and model selection process.

角色	必要性	描述
Data Scientist		Shapes custom LLM development strategy at organizational level. Defines buy vs build for LLM, evaluates open-source vs proprietary models. Influences industry through publications and open-source contributions.
LLM Engineer	必要	Shapes enterprise fine-tuning platform. Defines approaches to automated fine-tuning, model versioning, and A/B testing of fine-tuned models. Optimizes cost and speed of fine-tuning at scale.

Junior 2 要求

Data Scientist

Understands the concept of LLM fine-tuning: full fine-tuning vs parameter-efficient methods. Uses Hugging Face API for fine-tuning small models on custom data. Prepares training data in the correct format for various LLM platforms.
LLM Engineer
必要

Knows LLM fine-tuning basics: full fine-tuning vs LoRA, instruction-tuning data format. Runs basic fine-tuning of a small model via Hugging Face Trainer under mentor guidance.

Middle 2 要求

Data Scientist

Independently conducts LLM fine-tuning using LoRA, QLoRA, and prefix-tuning. Configures training hyperparameters, monitors loss curves. Evaluates fine-tuned model quality through domain-specific benchmarks and human evaluation.
LLM Engineer
必要

Independently conducts LLM fine-tuning: LoRA/QLoRA, instruction dataset preparation, hyperparameter tuning. Monitors training via W&B, evaluates results on held-out datasets.

Senior 2 要求

Data Scientist

Designs fine-tuning pipelines for production LLM systems. Applies RLHF, DPO for model alignment. Optimizes training through DeepSpeed, FSDP. Conducts systematic evaluation via automated benchmarks and red-teaming.
LLM Engineer
必要

Designs production fine-tuning pipelines: data curation, multi-stage training (SFT → DPO), distributed fine-tuning. Optimizes LoRA rank, learning rate, and batch size for maximum quality.

Lead / Staff 2 要求

Data Scientist

Defines LLM fine-tuning strategy for the organization. Establishes data preparation, training, and evaluation standards for custom LLMs. Coordinates GPU infrastructure and budgets for LLM experiments.
LLM Engineer
必要

Defines fine-tuning strategy for the LLM team. Establishes best practices for data preparation, training configuration, evaluation. Coordinates fine-tuning experiments and model selection process.

Principal 2 要求

Data Scientist

Shapes custom LLM development strategy at organizational level. Defines buy vs build for LLM, evaluates open-source vs proprietary models. Influences industry through publications and open-source contributions.
LLM Engineer
必要

Shapes enterprise fine-tuning platform. Defines approaches to automated fine-tuning, model versioning, and A/B testing of fine-tuned models. Optimizes cost and speed of fine-tuning at scale.

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