skills_go_to_github / trl /scripts /train_sft_example.py

Upload 15 files

fe6b91f verified about 1 year ago

2.68 kB

#!/usr/bin/env python3
# /// script
# dependencies = [
#     "trl>=0.12.0",
#     "peft>=0.7.0",
#     "transformers>=4.36.0",
#     "accelerate>=0.24.0",
#     "trackio",  # For real-time monitoring
# ]
# ///

"""
Production-ready SFT training example with all best practices.
This script demonstrates:
- Trackio integration for real-time monitoring
- LoRA/PEFT for efficient training
- Proper Hub saving configuration
- Checkpoint management
- Optimized training parameters
Usage with hf_jobs MCP tool:
    hf_jobs("uv", {
        "script": '''<paste this entire file>''',
        "flavor": "a10g-large",
        "timeout": "3h",
        "secrets": {"HF_TOKEN": "$HF_TOKEN"},
    })
Or submit the script content directly inline without saving to a file.
"""

import trackio
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig

# Initialize Trackio for real-time monitoring
trackio.init(
    project="qwen-capybara-sft",
    space_id="username/my-trackio-dashboard",  # Creates Space if it doesn't exist
    config={
        "model": "Qwen/Qwen2.5-0.5B",
        "dataset": "trl-lib/Capybara",
        "learning_rate": 2e-5,
        "num_epochs": 3,
        "peft_method": "LoRA",
    }
)

# Load and validate
dataset = load_dataset("trl-lib/Capybara", split="train")
print(f"✅ Dataset loaded: {len(dataset)} examples")

# Training configuration
config = SFTConfig(
    # CRITICAL: Hub settings
    output_dir="qwen-capybara-sft",
    push_to_hub=True,
    hub_model_id="username/qwen-capybara-sft",
    hub_strategy="every_save",  # Push checkpoints

    # Training parameters
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,

    # Logging & checkpointing
    logging_steps=10,
    save_strategy="steps",
    save_steps=100,
    save_total_limit=2,

    # Optimization
    warmup_ratio=0.1,
    lr_scheduler_type="cosine",

    # Monitoring
    report_to="trackio",  # Integrate with Trackio
)

# LoRA configuration
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=["q_proj", "v_proj"],
)

# Initialize and train
trainer = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,
    args=config,
    peft_config=peft_config,
)

print("🚀 Starting training...")
trainer.train()

print("💾 Pushing to Hub...")
trainer.push_to_hub()

# Finish Trackio tracking
trackio.finish()

print("✅ Complete! Model at: https://huggingface.co/username/qwen-capybara-sft")
print("📊 View metrics at: https://huggingface.co/spaces/username/my-trackio-dashboard")

	#!/usr/bin/env python3
	# /// script
	# dependencies = [
	# "trl>=0.12.0",
	# "peft>=0.7.0",
	# "transformers>=4.36.0",
	# "accelerate>=0.24.0",
	# "trackio", # For real-time monitoring
	# ]
	# ///

	"""
	Production-ready SFT training example with all best practices.

	This script demonstrates:
	- Trackio integration for real-time monitoring
	- LoRA/PEFT for efficient training
	- Proper Hub saving configuration
	- Checkpoint management
	- Optimized training parameters

	Usage with hf_jobs MCP tool:
	hf_jobs("uv", {
	"script": '''<paste this entire file>''',
	"flavor": "a10g-large",
	"timeout": "3h",
	"secrets": {"HF_TOKEN": "$HF_TOKEN"},
	})

	Or submit the script content directly inline without saving to a file.
	"""

	import trackio
	from datasets import load_dataset
	from peft import LoraConfig
	from trl import SFTTrainer, SFTConfig

	# Initialize Trackio for real-time monitoring
	trackio.init(
	project="qwen-capybara-sft",
	space_id="username/my-trackio-dashboard", # Creates Space if it doesn't exist
	config={
	"model": "Qwen/Qwen2.5-0.5B",
	"dataset": "trl-lib/Capybara",
	"learning_rate": 2e-5,
	"num_epochs": 3,
	"peft_method": "LoRA",
	}
	)

	# Load and validate
	dataset = load_dataset("trl-lib/Capybara", split="train")
	print(f"✅ Dataset loaded: {len(dataset)} examples")

	# Training configuration
	config = SFTConfig(
	# CRITICAL: Hub settings
	output_dir="qwen-capybara-sft",
	push_to_hub=True,
	hub_model_id="username/qwen-capybara-sft",
	hub_strategy="every_save", # Push checkpoints

	# Training parameters
	num_train_epochs=3,
	per_device_train_batch_size=4,
	gradient_accumulation_steps=4,
	learning_rate=2e-5,

	# Logging & checkpointing
	logging_steps=10,
	save_strategy="steps",
	save_steps=100,
	save_total_limit=2,

	# Optimization
	warmup_ratio=0.1,
	lr_scheduler_type="cosine",

	# Monitoring
	report_to="trackio", # Integrate with Trackio
	)

	# LoRA configuration
	peft_config = LoraConfig(
	r=16,
	lora_alpha=32,
	lora_dropout=0.05,
	bias="none",
	task_type="CAUSAL_LM",
	target_modules=["q_proj", "v_proj"],
	)

	# Initialize and train
	trainer = SFTTrainer(
	model="Qwen/Qwen2.5-0.5B",
	train_dataset=dataset,
	args=config,
	peft_config=peft_config,
	)

	print("🚀 Starting training...")
	trainer.train()

	print("💾 Pushing to Hub...")
	trainer.push_to_hub()

	# Finish Trackio tracking
	trackio.finish()

	print("✅ Complete! Model at: https://huggingface.co/username/qwen-capybara-sft")
	print("📊 View metrics at: https://huggingface.co/spaces/username/my-trackio-dashboard")