guillaumetell-7b / finetuning.py
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import os
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
LlamaTokenizerFast
)
from peft import LoraConfig, PeftModel, get_peft_model
from trl import SFTTrainer
# Le modèle que nous allons utiliser dans le Hugging Face hub
model_name = "mistral-hermes-2.5"
torch.cuda.empty_cache()
#project_directory = "~/finetuning/sigmund-spplus"
# Le nom du nouveau modèle
new_model_name = "mistral-mfs-reference-2"
# The output directory where the model predictions and checkpoints will be written
output_dir = "./mistral-mfs-reference-2"
# Tensorboard logs
tb_log_dir = "./mistral-mfs-reference-2/logs"
# Nombre de steps : à ajuster selon la taille du corpus et le nombre d'epochs à faire tourner.
max_steps = 2000
# Les paramètres importants !!
per_device_train_batch_size = 4 #Nombre d'exemples envoyés par batch. En mettre plus pour aller plus vite.
learning_rate = 2e-5 #De préférence un taux d'apprentissage bas comme Mistral-Hermes se débrouille bien en français
max_seq_length = 4096 #C'est la fenêtre contextuelle. Elle peut être portée jusqu'à 4096 tokens (mais attention à la mémoire disponible !)
save_steps = 1000 # Sauvegarde des steps (permet de faire redémarrer l'entraînement si le fine-tuning ne fonctionne pas)
# Learning rate schedule (constant a bit better than cosine, and has advantage for analysis)
lr_scheduler_type = "linear"
#Les autres paramètres
local_rank = -1
per_device_eval_batch_size = 1
gradient_accumulation_steps = 4
max_grad_norm = 0.3
weight_decay = 0.001
lora_alpha = 16
lora_dropout = 0.1
lora_r = 64
# Group sequences into batches with same length (saves memory and speeds up training considerably)
group_by_length = True
# Activate 4-bit precision base model loading
use_4bit = True
# Activate nested quantization for 4-bit base models
use_nested_quant = False
# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"
# Quantization type (fp4 or nf4=
bnb_4bit_quant_type = "nf4"
# Number of training epochs
num_train_epochs = 1
# Enable fp16 training
fp16 = True
# Enable bf16 training
bf16 = False
# Use packing dataset creating
packing = False
# Enable gradient checkpointing
gradient_checkpointing = True
# Optimizer to use, original is paged_adamw_32bit
optim = "paged_adamw_32bit"
# Fraction of steps to do a warmup for
warmup_ratio = 0.03
# Log every X updates steps
logging_steps = 1
# Load the entire model on the GPU 0
device_map = {"": 0}
# Visualize training
report_to = "tensorboard"
#2. Import du tokenizer.
peft_config = LoraConfig(
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
r=lora_r,
inference_mode=False,
task_type="CAUSAL_LM",
target_modules = ["q_proj", "v_proj"]
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# This is the fix for fp16 training
#tokenizer.padding_side = "right"
tokenizer.pad_token = tokenizer.eos_token
#3. Préparation de la base de données
from datasets import load_dataset
def format_alpaca(sample):
prompt = f"{sample['conversation']}"
return prompt
# template dataset to add prompt to each sample
def template_dataset(sample):
sample["text"] = f"{format_alpaca(sample)}{tokenizer.eos_token}"
return sample
# Chargement du dataset.
#dataset = load_dataset("databricks/databricks-dolly-15k", split="train")
data_files = {"train": "corpus_guillaume_tell_2.json"}
dataset = load_dataset("json", data_files=data_files, split="train")
# Shuffle the dataset
dataset_shuffled = dataset.shuffle(seed=42)
# Select the first 250 rows from the shuffled dataset, comment if you want 15k
#dataset = dataset_shuffled.select(range(512))
#Transformation du dataset pour utiliser le format guanaco
dataset = dataset.map(template_dataset, remove_columns=list(dataset.features))
print(dataset[40])
#4. Import du modèle
# Load tokenizer and model with QLoRA configuration
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
bnb_config = BitsAndBytesConfig(
load_in_4bit=use_4bit,
bnb_4bit_quant_type=bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_nested_quant,
)
if compute_dtype == torch.float16 and use_4bit:
major, _ = torch.cuda.get_device_capability()
if major >= 8:
print("=" * 80)
print("Your GPU supports bfloat16, you can accelerate training with the argument --bf16")
print("=" * 80)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map=device_map,
quantization_config=bnb_config
)
model.config.use_cache = False
model.config.pretraining_tp = 1
#5. Fine-tuning
torch.cuda.empty_cache()
training_arguments = TrainingArguments(
output_dir=output_dir,
per_device_train_batch_size=per_device_train_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
gradient_checkpointing=True,
optim=optim,
save_steps=save_steps,
logging_steps=logging_steps,
learning_rate=learning_rate,
fp16=fp16,
bf16=bf16,
max_grad_norm=max_grad_norm,
max_steps=max_steps,
warmup_ratio=warmup_ratio,
group_by_length=group_by_length,
lr_scheduler_type=lr_scheduler_type,
report_to="tensorboard"
)
trainer = SFTTrainer(
model=model,
train_dataset=dataset,
peft_config=peft_config,
dataset_text_field="text",
max_seq_length=max_seq_length,
tokenizer=tokenizer,
args=training_arguments,
packing=packing
)
trainer.train()
#trainer.train(resume_from_checkpoint=True)
#6. Sauvegarde
model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model # Take care of distributed/parallel training
model_to_save.save_pretrained(new_model_name)
torch.cuda.empty_cache()
from peft import AutoPeftModelForCausalLM
model = AutoPeftModelForCausalLM.from_pretrained(new_model_name, device_map="auto", torch_dtype=torch.bfloat16)
model = model.merge_and_unload()
output_merged_dir = os.path.join(new_model_name, new_model_name)
model.save_pretrained(output_merged_dir, safe_serialization=True)
tokenizer.save_pretrained(output_merged_dir)