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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 hyperparamet
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add Jeffallan/claude-skills --skill fine-tuning-expert -a cursor -y
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.
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.
Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.
python validate_dataset.py --input data.jsonl — fix all errors before proceedingLoad detailed guidance based on context:
| 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 |
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")
QLoRA variant — add these lines before loading the model to enable 4-bit quantization:
from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
Merge adapter into base model for deployment:
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
merged = PeftModel.from_pretrained(base, "./lora-adapter").merge_and_unload()
merged.save_pretrained("./merged-model")
When implementing fine-tuning, always provide:
TrainingArguments + LoraConfig block, commented)Maintained by @jeffallan, Principal Consultant at Synergetic Solutions