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"""
QUICK START: Qwen3.5-0.8B → Student (100-150M)
For RTX 2050 (4GB VRAM) on Arch Linux
"""
import subprocess
import sys
from pathlib import Path
import logging
import time
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# ============================================================================
# STEP 0: Install Dependencies
# ============================================================================
def install_dependencies():
"""Install required packages with uv"""
logger.info("Installing dependencies with uv...")
packages = [
"torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121",
"transformers>=4.40.0",
"accelerate",
"datasets",
"bitsandbytes", # For quantization
"peft", # For LoRA
]
for pkg in packages:
logger.info(f"Installing: {pkg}")
subprocess.run([sys.executable, "-m", "pip", "install", pkg], check=False)
logger.info("✓ Dependencies installed")
# ============================================================================
# STEP 1: GGUF to HuggingFace Conversion
# ============================================================================
def convert_gguf_to_hf(gguf_path: str, output_dir: str = "models/qwen_teacher"):
"""
Convert GGUF to HuggingFace format
Note: This requires the model architecture config
For Qwen3.5-0.8B, we can also just download from HuggingFace instead
"""
logger.info(f"Converting GGUF: {gguf_path}")
# Option 1: Use ollama/llama.cpp to load and export
try:
from llama_cpp import Llama
logger.info("Loading GGUF with llama.cpp...")
llm = Llama(model_path=gguf_path, n_gpu_layers=-1)
# Note: llama.cpp doesn't easily export to HuggingFace format
logger.warning("GGUF loading for inference only. For training, use HuggingFace model instead.")
return llm
except ImportError:
logger.error("llama-cpp-python not installed. Install with: pip install llama-cpp-python")
logger.info("Alternative: Download Qwen from HuggingFace")
return None
# ============================================================================
# STEP 2: Download Teacher Model
# ============================================================================
def download_qwen_teacher(output_dir: str = "models/teacher"):
"""Download Qwen teacher from HuggingFace"""
logger.info("Downloading Qwen teacher model...")
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-0.5B" # Use 0.5B as proxy for 0.8B
# Alternative options:
# - "Qwen/Qwen1.5-0.5B"
# - "Qwen/Qwen2-0.5B"
Path(output_dir).mkdir(parents=True, exist_ok=True)
logger.info(f"Downloading {model_name}...")
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map="auto",
)
model.save_pretrained(output_dir)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.save_pretrained(output_dir)
logger.info(f"✓ Model saved to {output_dir}")
return output_dir
# ============================================================================
# STEP 3: Prepare Training Data
# ============================================================================
def prepare_dataset(dataset_name: str = "wikitext", split: str = "train", output_file: str = "data/train.txt"):
"""Download and prepare training data"""
logger.info(f"Preparing dataset: {dataset_name}")
from datasets import DownloadConfig, load_dataset
Path(output_file).parent.mkdir(parents=True, exist_ok=True)
logger.info(f"Loading {dataset_name}...")
if dataset_name == "wikitext":
# Prefer canonical repo/config names and retry transient network failures.
wikitext_candidates = [
("Salesforce/wikitext", "wikitext-2-raw-v1"),
("Salesforce/wikitext", "wikitext-2-v1"),
("wikitext", "wikitext-2-raw-v1"),
("wikitext", "wikitext-2"),
]
max_attempts = 4
backoff_seconds = 2
download_config = DownloadConfig(max_retries=8)
texts = None
last_error = None
for dataset_id, config_name in wikitext_candidates:
for attempt in range(1, max_attempts + 1):
try:
logger.info(
"Loading %s (%s), split=%s [attempt %s/%s]",
dataset_id,
config_name,
split,
attempt,
max_attempts,
)
dataset_split = load_dataset(
dataset_id,
config_name,
split=split,
download_config=download_config,
)
texts = dataset_split["text"]
break
except Exception as exc:
last_error = exc
if attempt < max_attempts:
sleep_s = backoff_seconds * attempt
logger.warning(
"Dataset load failed for %s (%s): %s. Retrying in %ss...",
dataset_id,
config_name,
exc,
sleep_s,
)
time.sleep(sleep_s)
if texts is not None:
break
if texts is None:
raise RuntimeError(
"Failed to load WikiText after retries/fallbacks. "
"Please check internet connectivity and Hugging Face availability."
) from last_error
elif dataset_name == "pile":
dataset = load_dataset("the_pile", split=f"{split}[:5000]") # Subset
texts = dataset["text"]
else:
logger.error(f"Unknown dataset: {dataset_name}")
return None
# Save to text file
logger.info(f"Writing to {output_file}...")
with open(output_file, 'w') as f:
for text in texts:
if text.strip():
f.write(text + "\n")
logger.info(f"✓ Dataset saved: {output_file}")
return output_file
# ============================================================================
# STEP 4: Configuration
# ============================================================================
def create_config_template():
"""Create config.py template"""
config_content = '''
# config.py - Training configuration
from qwen_distill import QwenDistillationConfig
class MyConfig(QwenDistillationConfig):
def __init__(self):
super().__init__()
# Paths
self.data_file = "data/train.txt"
self.teacher_model_name = "Qwen/Qwen2.5-0.5B"
# Student size (adjust based on your needs)
# Small: 3 layers, 128 hidden = ~30M params
# Medium: 5 layers, 256 hidden = ~100M params
# Large: 8 layers, 384 hidden = ~250M params
self.student_num_layers = 5
self.student_hidden_dim = 256
self.student_num_heads = 4
# Training
self.batch_size = 2
self.gradient_accumulation_steps = 4
self.max_steps = 2000
self.learning_rate = 8e-4
# Distillation
self.temperature = 3.0
self.alpha = 0.8 # 80% KD loss
self.beta = 0.2 # 20% feature loss
# Memory
self.use_gradient_checkpointing = True
self.mixed_precision = "fp16"
'''
with open("config.py", 'w') as f:
f.write(config_content)
logger.info("✓ Created config.py template")
# ============================================================================
# STEP 5: Training Script
# ============================================================================
def create_train_script():
"""Create training script"""
train_script = '''#!/usr/bin/env python3
from qwen_distill import QwenDistillationConfig, QwenDistillationTrainer, TextDataset
from torch.utils.data import DataLoader
import torch
# Load config
config = QwenDistillationConfig()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Initialize trainer
trainer = QwenDistillationTrainer(config, device)
# Load data
with open("data/train.txt", "r") as f:
texts = [line.strip() for line in f if line.strip()]
print(f"Loaded {len(texts)} text samples")
# Create dataset & dataloader
dataset = TextDataset(texts, trainer.tokenizer, max_length=config.max_seq_length)
dataloader = DataLoader(dataset, batch_size=config.batch_size, shuffle=True)
# Train
trainer.train(dataloader)
print("✓ Training complete!")
print(f"Student saved to: checkpoints/student_final.pt")
'''
with open("train.py", 'w') as f:
f.write(train_script)
logger.info("✓ Created train.py")
# ============================================================================
# USAGE
# ============================================================================
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--setup", action="store_true", help="Setup environment")
parser.add_argument("--download", action="store_true", help="Download teacher")
parser.add_argument("--data", action="store_true", help="Prepare dataset")
parser.add_argument("--config", action="store_true", help="Create config")
parser.add_argument("--all", action="store_true", help="Do all steps")
args = parser.parse_args()
if args.setup or args.all:
install_dependencies()
if args.download or args.all:
download_qwen_teacher()
if args.data or args.all:
prepare_dataset("wikitext", "train", "data/train.txt")
if args.config or args.all:
create_config_template()
create_train_script()
if args.all:
logger.info("""
✓ Setup complete!
Next steps:
1. Edit config.py to customize settings
2. Run: python train.py
3. Monitor training in logs/
4. Evaluate student model (see eval.py)
""")
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