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These skills require heavy GPU/CUDA stacks or are niche enough that they shouldn't be active by default. Moved to optional-skills/ where users opt-in via `hermes skills install official/...`. Moved: - mlops/training/axolotl - mlops/training/trl-fine-tuning - mlops/training/unsloth - mlops/inference/outlines Counts: 91 -> 87 built-in, 72 -> 76 optional. Auto-regenerated docs (per-skill pages + catalogs) reflect the move.
168 lines
3.2 KiB
Markdown
168 lines
3.2 KiB
Markdown
# SFT Training Guide
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Complete guide to Supervised Fine-Tuning (SFT) with TRL for instruction tuning and task-specific fine-tuning.
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## Overview
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SFT trains models on input-output pairs to minimize cross-entropy loss. Use for:
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- Instruction following
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- Task-specific fine-tuning
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- Chatbot training
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- Domain adaptation
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## Dataset Formats
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### Format 1: Prompt-Completion
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```json
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[
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{
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"prompt": "What is the capital of France?",
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"completion": "The capital of France is Paris."
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}
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]
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```
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### Format 2: Conversational (ChatML)
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```json
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[
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{
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"messages": [
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{"role": "user", "content": "What is Python?"},
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{"role": "assistant", "content": "Python is a programming language."}
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]
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}
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]
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```
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### Format 3: Text-only
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```json
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[
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{"text": "User: Hello\nAssistant: Hi! How can I help?"}
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]
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```
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## Basic Training
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```python
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from trl import SFTTrainer, SFTConfig
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from datasets import load_dataset
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# Load model
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
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# Load dataset
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dataset = load_dataset("trl-lib/Capybara", split="train")
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# Configure
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config = SFTConfig(
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output_dir="Qwen2.5-SFT",
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per_device_train_batch_size=4,
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num_train_epochs=1,
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learning_rate=2e-5,
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save_strategy="epoch"
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)
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# Train
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trainer = SFTTrainer(
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model=model,
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args=config,
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train_dataset=dataset,
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tokenizer=tokenizer
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)
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trainer.train()
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```
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## Chat Templates
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Apply chat templates automatically:
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```python
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trainer = SFTTrainer(
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model=model,
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args=config,
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train_dataset=dataset, # Messages format
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tokenizer=tokenizer
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# Chat template applied automatically
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)
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```
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Or manually:
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```python
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def format_chat(example):
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messages = example["messages"]
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text = tokenizer.apply_chat_template(messages, tokenize=False)
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return {"text": text}
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dataset = dataset.map(format_chat)
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```
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## Packing for Efficiency
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Pack multiple sequences into one to maximize GPU utilization:
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```python
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config = SFTConfig(
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packing=True, # Enable packing
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max_seq_length=2048,
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dataset_text_field="text"
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)
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```
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**Benefits**: 2-3× faster training
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**Trade-off**: Slightly more complex batching
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## Multi-GPU Training
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```bash
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accelerate launch --num_processes 4 train_sft.py
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```
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Or with config:
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```python
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config = SFTConfig(
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output_dir="model-sft",
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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num_train_epochs=1
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)
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```
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## LoRA Fine-Tuning
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```python
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from peft import LoraConfig
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lora_config = LoraConfig(
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r=16,
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lora_alpha=32,
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target_modules="all-linear",
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lora_dropout=0.05,
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task_type="CAUSAL_LM"
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)
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trainer = SFTTrainer(
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model=model,
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args=config,
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train_dataset=dataset,
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peft_config=lora_config # Add LoRA
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)
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```
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## Hyperparameters
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| Model Size | Learning Rate | Batch Size | Epochs |
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|------------|---------------|------------|--------|
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| <1B | 5e-5 | 8-16 | 1-3 |
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| 1-7B | 2e-5 | 4-8 | 1-2 |
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| 7-13B | 1e-5 | 2-4 | 1 |
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| 13B+ | 5e-6 | 1-2 | 1 |
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## References
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- TRL docs: https://huggingface.co/docs/trl/sft_trainer
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- Examples: https://github.com/huggingface/trl/tree/main/examples/scripts
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