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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer
model_name = "TinyPixel/Llama-2-7B-bf16-sharded"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
trust_remote_code=True
)
model.config.use_cache = False
"""Let's also load the tokenizer below"""
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
from peft import LoraConfig, get_peft_model
lora_alpha = 16
lora_dropout = 0.1
lora_r = 64
peft_config = LoraConfig(
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
r=lora_r,
bias="none",
task_type="CAUSAL_LM"
)
"""## Loading the trainer
Here we will use the [`SFTTrainer` from TRL library](https://huggingface.co/docs/trl/main/en/sft_trainer) that gives a wrapper around transformers `Trainer` to easily fine-tune models on instruction based datasets using PEFT adapters. Let's first load the training arguments below.
"""
from transformers import TrainingArguments
output_dir = "./results"
per_device_train_batch_size = 4
gradient_accumulation_steps = 4
optim = "paged_adamw_32bit"
save_steps = 100
logging_steps = 10
learning_rate = 2e-4
max_grad_norm = 0.3
max_steps = 100
warmup_ratio = 0.03
lr_scheduler_type = "constant"
training_arguments = TrainingArguments(
output_dir=output_dir,
per_device_train_batch_size=per_device_train_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
optim=optim,
save_steps=save_steps,
logging_steps=logging_steps,
learning_rate=learning_rate,
fp16=True,
max_grad_norm=max_grad_norm,
max_steps=max_steps,
warmup_ratio=warmup_ratio,
group_by_length=True,
lr_scheduler_type=lr_scheduler_type,
)
"""Then finally pass everthing to the trainer"""
from trl import SFTTrainer
max_seq_length = 512
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,
)
"""We will also pre-process the model by upcasting the layer norms in float 32 for more stable training"""
for name, module in trainer.model.named_modules():
if "norm" in name:
module = module.to(torch.float32)
"""## Train the model
Now let's train the model! Simply call `trainer.train()`
"""
trainer.train()
"""During training, the model should converge nicely as follows:
The `SFTTrainer` also takes care of properly saving only the adapters during training instead of saving the entire model.
"""
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("outputs")
lora_config = LoraConfig.from_pretrained('outputs')
model = get_peft_model(model, lora_config)
dataset['text']
text = "Écrire un texte dans un style baroque sur la glace et le feu ### Assistant: Si j'en luis éton"
device = "cuda:0"
inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
from huggingface_hub import login
login()
model.push_to_hub("llama2-qlora-finetunined-french")
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