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import torch
from peft import LoraConfig, get_peft_model
import os
from tqdm import tqdm
import json
import random
from datasets import load_dataset
from datasets import Dataset, DatasetDict
system_message = "You are a helpful assistant who is an expert in estimating quality of translations."
output_template = '''
{
"Accuracy Issues": [
{
"Error Span": "",
"Error Explanation": "",
"Error Quality Category": "",
"Error Quality Tags": [],
"Error Severity": ""
}
],
"Accuracy Score": "",
"Readability Issues": [
{
"Error Span": "",
"Error Explanation": "",
"Error Quality Category": "",
"Error Quality Tags": [],
"Error Severity": ""
}
],
"Readability Score": ""
}'''
def create_conversation(input_sample, output_sample):
return {
"messages": [
# {"role": "system", "content": system_message},
{"role": "user", "content": input_sample},
{"role": "assistant", "content": output_sample}
]
}
data_path = (
"/root/notebooks/MT_TQ/TQ/TQTune/labeled_data/parsed/"
)
json_files = [
os.path.join(root, file)
for root, _, files in os.walk(data_path)
for file in files
if file.endswith(".json") and "PLDL" in file
]
training_samples = []
for json_file in tqdm(json_files):
with open(json_file, "r") as file:
data = json.load(file)
sampled_items = random.sample(data["data"], 20)
training_samples.extend(sampled_items)
datapoints = []
for sample in training_samples:
datapoint = {"input": {}}
datapoint["input"]["src_text"] = sample["main_src_text"]
datapoint["input"]["tgt_text"] = sample["tgt_text"]
datapoint["input"]["src_prev"] = sample["tt_src_prev"]
datapoint["input"]["src_next"] = sample["tt_src_next"]
datapoint["input"]["tgt_prev"] = sample["tt_tgt_prev"]
datapoint["input"]["tgt_next"] = sample["tt_tgt_next"]
datapoint["input"]["src_lang"] = sample["src_lang"]
datapoint["input"]["tgt_lang"] = sample["tgt_lang"]
datapoint["evaluation"] = sample["labelers"][0]["annotation"]
datapoints.append(datapoint)
def dataset_prep(datapoints, test_size=0.2):
with open("prompts.txt") as file:
template_string = file.read()
random.shuffle(datapoints)
split_index = int(len(datapoints) * (1 - test_size))
train_datapoints = datapoints[:split_index]
test_datapoints = datapoints[split_index:]
def create_dataset(datapoints):
dataset = []
for datapoint in datapoints:
src_text = datapoint['input']['src_text']
tgt_text = datapoint['input']['tgt_text']
src_prev = datapoint['input']['src_prev']
src_next = datapoint['input']['src_next']
tgt_prev = datapoint['input']['tgt_prev']
tgt_next = datapoint['input']['tgt_next']
src_lang = datapoint['input']['src_lang']
tgt_lang = datapoint['input']['tgt_lang']
output = datapoint['evaluation']
del output["Confidence Level"]
del output["Main Vs Alternate"]
del output["Score"]
if len(output['Accuracy Issues']) != 0 and len(output['Readability Issues']) != 0:
item = template_string.format(src_text=src_text, tgt_text=tgt_text,
src_prev=src_prev, src_next=src_next,
tgt_prev=tgt_prev, tgt_next=tgt_next,
src_lang=src_lang, tgt_lang=tgt_lang,
template=output_template)
dataset.append(create_conversation(item, json.dumps(output)))
return dataset
train_set = create_dataset(train_datapoints)
test_set = create_dataset(test_datapoints)
return train_set, test_set
train_dataset, test_dataset = dataset_prep(datapoints)
dataset = {"train": train_dataset, "test": test_dataset}
def convert_to_hf_dataset(dataset):
# Convert the train and test datasets into Hugging Face Dataset objects
train_dataset = Dataset.from_list(dataset['train'])
test_dataset = Dataset.from_list(dataset['test'])
# Combine them into a DatasetDict
hf_dataset = DatasetDict({
'train': train_dataset,
'test': test_dataset
})
return hf_dataset
# Convert your dataset into a Hugging Face Dataset object
hf_dataset = convert_to_hf_dataset(dataset)
# Now you can use hf_dataset for your machine learning tasks
print(hf_dataset)
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, BitsAndBytesConfig
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
device = torch.device("cuda:0")
# Hugging Face model id
model_id = "google/gemma-3-12b-it" # or `google/gemma-3-4b-pt`, `google/gemma-3-12b-pt`, `google/gemma-3-27b-pt`
# Select model class based on id
if model_id == "google/gemma-3-12b-it":
model_class = Gemma3ForConditionalGeneration
else:
model_class = AutoModelForImageTextToText
torch_dtype = torch.bfloat16
model_kwargs = dict(
attn_implementation="eager",
torch_dtype=torch_dtype,
device_map="auto", # Change from {'': 0} to "auto"
)
model_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_8bit=True,
bnb_8bit_use_double_quant=True,
bnb_8bit_quant_type='nf8',
bnb_8bit_compute_dtype=model_kwargs['torch_dtype'],
bnb_8bit_quant_storage=model_kwargs['torch_dtype'],
)
model = model_class.from_pretrained(model_id, **model_kwargs)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-12b-it") # Load the Instruction Tokenizer to use the official Gemma template
from peft import LoraConfig
peft_config = LoraConfig(
lora_alpha=128,
lora_dropout=0.05,
r=16,
bias="none",
target_modules="all-linear",
task_type="CAUSAL_LM",
modules_to_save=["lm_head", "embed_tokens"] # make sure to save the lm_head and embed_tokens as you train the special tokens
)
from trl import SFTConfig
args = SFTConfig(
output_dir="gemma-12b-tq-model",
max_seq_length=512,
packing=True,
num_train_epochs=1,
per_device_train_batch_size=1,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
optim="adamw_torch_fused",
logging_steps=1,
save_strategy="epoch",
learning_rate=2e-4,
fp16=True if torch_dtype == torch.float16 else False,
bf16=True if torch_dtype == torch.bfloat16 else False,
max_grad_norm=0.3,
warmup_ratio=0.03,
lr_scheduler_type="constant",
push_to_hub=True,
report_to="tensorboard",
dataset_kwargs={
"add_special_tokens": False,
"append_concat_token": True,
},
ddp_find_unused_parameters=False,
no_cuda=False,
)
from trl import SFTTrainer
# Create Trainer object
trainer = SFTTrainer(
model=model,
args=args,
train_dataset=hf_dataset["train"],
peft_config=peft_config,
processing_class=tokenizer
)
trainer.train()
trainer.save_model() |