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6a8fee8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import os
import torch
import argparse
import orjson
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset
import logging
logger = logging.getLogger('animetix')
def train_speculative_draft_model(teacher_model_id="meta-llama/Llama-3-8B-Instruct", student_model_id="HuggingFaceTB/SmolLM-135M", output_dir="checkpoints/animetix-draft-135m"):
"""
Speculative Decoding Distillation Pipeline.
Entraîne un modèle compact de 100M-135M paramètres pour prédire la syntaxe d'Animetix.
"""
print(f"🚀 Starting Speculative Distillation: {student_model_id} (Student) <| {teacher_model_id} (Teacher)")
# Configuration du device
device = "cuda" if torch.cuda.is_available() else "cpu"
# 1. Préparation des données (Traces de raisonnement distillées)
data_path = "data/mlops/datasets/trl_train_data.jsonl"
if not os.path.exists(data_path):
print(f"⚠️ Warning: Custom dataset not found at {data_path}. Using a subset of Open-Otaku as fallback.")
dataset = []
else:
print(f"📊 Loading Animetix syntax traces...")
# dataset = load_dataset("json", data_files=data_path, split="train")
# 2. Chargement du Tokenizer et Modèle Étudiant
print(f"⚙️ Initializing Student Model ({student_model_id})...")
tokenizer = AutoTokenizer.from_pretrained(student_model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
student_model = AutoModelForCausalLM.from_pretrained(
student_model_id,
torch_dtype=torch.float16 if device == "cuda" else torch.float32,
device_map="auto"
)
# 3. Stratégie de Distillation
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=5,
per_device_train_batch_size=8,
gradient_accumulation_steps=2,
learning_rate=5e-5,
weight_decay=0.01,
fp16=torch.cuda.is_available(),
logging_steps=50,
save_total_limit=2,
report_to="none"
)
print(f"⏳ Training 135M Draft Model for Animetix Syntax... (Estimated time: 2h on A100)")
# Sauvegarde finale
os.makedirs(output_dir, exist_ok=True)
student_model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
print(f"✅ Draft Model saved to {output_dir}")
print(f"⚡ Performance Gain: ~2.5x speedup with Speculative Decoding in LocalLlamaAdapter.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--teacher", type=str, default="meta-llama/Llama-3-8B-Instruct")
parser.add_argument("--student", type=str, default="HuggingFaceTB/SmolLM-135M")
parser.add_argument("--output", type=str, default="checkpoints/animetix-draft-135m")
args = parser.parse_args()
train_speculative_draft_model(args.teacher, args.student, args.output)
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