fine-tuning-expert

Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.

Ir a la instalación

Repositorio: Jeffallan/claude-skills
Última actividad en el origen: 29 de abril de 2026 a las 03:32
Idioma detectado de SKILL.md: inglés
Estrellas: 11.000
Forks: 1031

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.

npx skills add https://github.com/Jeffallan/claude-skills --skill fine-tuning-expert

El comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.

¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Explorador de archivos

6 archivos

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

Volver a la búsqueda Volver a la instalación

devops-engineer

Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.

2026-08-07

terraform-engineer

Use when implementing infrastructure as code with Terraform across AWS, Azure, or GCP. Invoke for module development (create reusable modules, manage module versioning), state management (migrate backends, import existing resources, resolve state conflicts), provider configuration, multi-environment workflows, and infrastructure testing.

2026-08-07

rag-architect

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

2026-08-07

Ver todos los de este repositorio

name	fine-tuning-expert
description	Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.
license	MIT
metadata	{"author":"https://github.com/Jeffallan","version":"1.1.0","domain":"data-ml","triggers":"fine-tuning, fine tuning, finetuning, LoRA, QLoRA, PEFT, adapter tuning, transfer learning, model training, custom model, LLM training, instruction tuning, RLHF, model optimization, quantization","role":"expert","scope":"implementation","output-format":"code","related-skills":"devops-engineer"}

Fine-Tuning Expert

Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.

Core Workflow

Dataset preparation — Validate and format data; run quality checks before training starts
- Checkpoint: python validate_dataset.py --input data.jsonl — fix all errors before proceeding
Method selection — Choose PEFT technique based on GPU memory and task requirements
- Use LoRA for most tasks; QLoRA (4-bit) when GPU memory is constrained; full fine-tune only for small models

from datasets import load_dataset from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments from peft import LoraConfig, get_peft_model, TaskType from trl import SFTTrainer import torch # 1. Load base model and tokenizer model_id = "meta-llama/Llama-3-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) # 2. Configure LoRA adapter lora_config = LoraConfig( task_type=TaskType.CAUSAL_LM, r=16, # rank — increase for more capacity, decrease to save memory lora_alpha=32, # scaling factor; typically 2× rank target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", ) model = get_peft_model(model, lora_config) model.print_trainable_parameters() # verify: should be ~0.1–1% of total params # 3. Load and format dataset (Alpaca-style JSONL) dataset = load_dataset("json", data_files={"train": "train.jsonl", "test": "test.jsonl"}) def format_prompt(example): return {"text": f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}"} dataset = dataset.map(format_prompt) # 4. Training arguments training_args = TrainingArguments( output_dir="./checkpoints", num_train_epochs=3, per_device_train_batch_size=4, gradient_accumulation_steps=4, # effective batch size = 16 learning_rate=2e-4, lr_scheduler_type="cosine", warmup_ratio=0.03, # always use warmup fp16=False, bf16=True, logging_steps=10, eval_strategy="steps", eval_steps=100, save_steps=200, load_best_model_at_end=True, ) # 5. Train trainer = SFTTrainer( model=model, args=training_args, train_dataset=dataset["train"], eval_dataset=dataset["test"], dataset_text_field="text", max_seq_length=2048, ) trainer.train() # 6. Save adapter weights only model.save_pretrained("./lora-adapter") tokenizer.save_pretrained("./lora-adapter")

Topic	Reference	Load When
LoRA/PEFT	`references/lora-peft.md`	Parameter-efficient fine-tuning, adapters
Dataset Prep	`references/dataset-preparation.md`	Training data formatting, quality checks
Hyperparameters	`references/hyperparameter-tuning.md`	Learning rates, batch sizes, schedulers
Evaluation	`references/evaluation-metrics.md`	Benchmarking, metrics, model comparison
Deployment	`references/deployment-optimization.md`	Model merging, quantization, serving

fine-tuning-expert

Opciones de instalación

Revisa los archivos de origen

Fine-Tuning Expert

Core Workflow

Fine-Tuning Expert

Core Workflow

Reference Guide

Minimal Working Example — LoRA Fine-Tuning with Hugging Face PEFT

Constraints

MUST DO

MUST NOT DO

Output Templates

fine-tuning-expert

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

Fine-Tuning Expert

Core Workflow

Fine-Tuning Expert

Core Workflow

Reference Guide

Minimal Working Example — LoRA Fine-Tuning with Hugging Face PEFT

Constraints

MUST DO

MUST NOT DO

Output Templates