আলিঙ্গন ফেস ট্রান্সফরমার এবং QloRA ব্যবহার করে ফাইন-টিউন জেমা

# Install Pytorch & other libraries
%pip install torch tensorboard
%pip install -U torchao

# Install Transformers
%pip install "transformers>=5.10.1"

# Install Hugging Face libraries
%pip install datasets accelerate evaluate bitsandbytes trl "peft>=0.19.0" protobuf sentencepiece

# COMMENT IN: if you are running on a GPU that supports BF16 data type and flash attn, such as NVIDIA L4 or NVIDIA A100
#%pip install flash-attn

# Login into Hugging Face Hub
from huggingface_hub import login
login()

{"messages": [{"role": "system", "content": "You are..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
{"messages": [{"role": "system", "content": "You are..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
{"messages": [{"role": "system", "content": "You are..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}

from datasets import load_dataset

# System message for the assistant
system_message = """You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA."""

# User prompt that combines the user query and the schema
user_prompt = """Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.

<SCHEMA>
{context}
</SCHEMA>

<USER_QUERY>
{question}
</USER_QUERY>
"""
def create_conversation(sample, idx):
  return {
    "messages": [
      {"role": "system", "content": system_message},
      {"role": "user", "content": user_prompt.format(question=sample["sql_prompt"], context=sample["sql_context"])},
      {"role": "assistant", "content": sample["sql"]}
    ]
  }

# Load dataset from the hub
dataset = load_dataset("philschmid/gretel-synthetic-text-to-sql", split="train")
dataset = dataset.select(range(1250))

# Convert dataset to OAI messages
dataset = dataset.map(create_conversation, with_indices=True, remove_columns=dataset.features)
# split dataset into 80% training samples and 20% test samples
dataset = dataset.train_test_split(test_size=0.2, shuffle=False)

# Print formatted user prompt
for item in dataset["train"][0]["messages"]:
  print(item)

{'role': 'system', 'content': 'You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.'}
{'role': 'user', 'content': "Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.\n\n<SCHEMA>\nCREATE TABLE salesperson (salesperson_id INT, name TEXT, region TEXT); INSERT INTO salesperson (salesperson_id, name, region) VALUES (1, 'John Doe', 'North'), (2, 'Jane Smith', 'South'); CREATE TABLE timber_sales (sales_id INT, salesperson_id INT, volume REAL, sale_date DATE); INSERT INTO timber_sales (sales_id, salesperson_id, volume, sale_date) VALUES (1, 1, 120, '2021-01-01'), (2, 1, 150, '2021-02-01'), (3, 2, 180, '2021-01-01');\n</SCHEMA>\n\n<USER_QUERY>\nWhat is the total volume of timber sold by each salesperson, sorted by salesperson?\n</USER_QUERY>\n"}
{'role': 'assistant', 'content': 'SELECT salesperson_id, name, SUM(volume) as total_volume FROM timber_sales JOIN salesperson ON timber_sales.salesperson_id = salesperson.salesperson_id GROUP BY salesperson_id, name ORDER BY total_volume DESC;'}

import torch
from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig
from peft import prepare_model_for_kbit_training

# Hugging Face model id
model_id = "google/gemma-4-E2B" # @param ["google/gemma-4-E2B","google/gemma-4-E4B","google/gemma-4-12B","google/gemma-4-31B","google/gemma-4-26B-A4B"] {"allow-input":true}

# Check if GPU supports bfloat16
if torch.cuda.is_bf16_supported():
    torch_dtype = torch.bfloat16
else:
    torch_dtype = torch.float16

# Define model init arguments
model_kwargs = dict(
    dtype=torch_dtype, # What torch dtype to use
    device_map="auto", # Let torch decide how to load the model
)

# BitsAndBytesConfig: Enables 4-bit quantization to reduce model size/memory usage
model_kwargs["quantization_config"] = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_compute_dtype=torch_dtype,
    bnb_4bit_quant_storage=torch_dtype,
)

# Load model and processor
model = AutoModelForMultimodalLM.from_pretrained(model_id, **model_kwargs)
processor = AutoProcessor.from_pretrained("google/gemma-4-E2B-it") # Load the Instruction Processor to use the official Gemma template

# NOTE: You should call the prepare_model_for_kbit_training() function to preprocess the quantized model for training.
# On T4, we are skipping this step purely due to VRAM limitation and for a quick demonstration.
if (torch.cuda.get_device_properties(0).total_memory/1024**3) > 16:
    model = prepare_model_for_kbit_training(model)

WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so
WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so
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/usr/local/lib/python3.12/dist-packages/bitsandbytes/backends/cuda/ops.py:213: FutureWarning: _check_is_size will be removed in a future PyTorch release along with guard_size_oblivious.     Use _check(i >= 0) instead.
  torch._check_is_size(blocksize)

from peft import LoraConfig

peft_config = LoraConfig(
    lora_alpha=16,
    lora_dropout=0.05,
    r=16,
    bias="none",
    # no target_modules — PEFT's Gemma 4 defaults scope to the LM layers
    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
    ensure_weight_tying=True,
)

import torch
from trl import SFTConfig

args = SFTConfig(
    output_dir="gemma-text-to-sql",         # directory to save and repository id
    max_length=512,                         # max length for model and packing of the dataset
    num_train_epochs=3,                     # number of training epochs
    per_device_train_batch_size=1,          # batch size per device during training
    per_device_eval_batch_size=1,           # batch size per device during evaluation
    optim="adamw_torch_fused",              # use fused adamw optimizer
    logging_steps=10,                       # log every 10 steps
    save_strategy="epoch",                  # save checkpoint every epoch
    eval_strategy="epoch",                  # evaluate checkpoint every epoch
    learning_rate=2e-4,                     # learning rate
    fp16=True if torch_dtype == torch.float16 else False,  # use float16 precision
    bf16=True if torch_dtype == torch.bfloat16 else False, # use bfloat16 precision
    lr_scheduler_type="constant",           # use constant learning rate scheduler
    push_to_hub=True,                       # push model to hub
    report_to="tensorboard",                # report metrics to tensorboard
    dataset_kwargs={"skip_prepare_dataset": True}, # important for collator
    remove_unused_columns = False,                 # important for collator
)

# Data collator
def collate_fn(examples):
    texts = []

    for example in examples:
        full_text = processor.apply_chat_template(
            example["messages"], add_generation_prompt=False, tokenize=False
        )
        texts.append(full_text.strip())

    # Tokenize the texts and process the audios
    batch = processor(text=texts, return_tensors="pt", padding=True)

    # The labels are the input_ids, and we mask the padding tokens and audio tokens in the loss computation
    labels = batch["input_ids"].clone()

    target_tokens = [
        processor.tokenizer.convert_tokens_to_ids("<|turn>"),
        processor.tokenizer.convert_tokens_to_ids("model"),
        processor.tokenizer.convert_tokens_to_ids("\n")
    ]
    target_len = len(target_tokens)

    for i in range(labels.size(0)):
        row_tokens = batch["input_ids"][i].tolist()

        # Find where the assistant block begins
        assistant_start_idx = None
        for idx in range(len(row_tokens) - target_len + 1):
            if row_tokens[idx : idx + target_len] == target_tokens:
                # We want to keep loss calculation on the assistant transcription tokens,
                # so we move the index right past the assistant header ('<|turn>\nmodel\n')
                assistant_start_idx = idx + target_len
                break

        if assistant_start_idx is not None:
            # Mask everything from index 0 up to the start of the actual Japanese text response
            labels[i, :assistant_start_idx] = -100
        else:
            # Fallback safety: if template matching fails for an anomalous row, mask padding anyway
            print("WARNING: maybe the sample is too long, try to increase `token_limit` value.")
            labels[i, labels[i] == processor.tokenizer.pad_token_id] = -100

        """
        # --- DEBUG PRINT CODE ---
        print(f"\n--- Example {i} (Split index: {assistant_start_idx}) ---")
        debug_string = []
        for token_id, label_id in zip(row_tokens, labels[i].tolist()):
            # Decode token by token so we can see exactly what is masked
            decoded_token = processor.tokenizer.decode([token_id])

            if label_id == -100:
                # Red text for masked tokens (ANSI Escape Code)
                debug_string.append(f"\033[91m{decoded_token}\033[0m")
            else:
                # Green text for active loss tokens
                debug_string.append(f"\033[92m{decoded_token}\033[0m")

        print("".join(debug_string))
        # ------------------------
        """


    # Mask tokens for not being used in the loss computation
    labels[labels == processor.tokenizer.pad_token_id] = -100

    batch["labels"] = labels
    return batch

/tmp/ipykernel_164873/3952412390.py:4: FutureWarning: The default `loss_type` will change from `'nll'` to `'chunked_nll'` in TRL 1.7. For standard models this is transparent (same math, lower memory) and no action is needed — you'll get the new default automatically on upgrade. If you use a custom model, check ahead of time that `loss_type='chunked_nll'` runs and yields the same loss as `'nll'`; if it doesn't, pin `loss_type='nll'` to keep the current behavior and please open an issue at https://github.com/huggingface/trl/issues so we can address the edge case.
  args = SFTConfig(

from trl import SFTTrainer

# Create Trainer object
trainer = SFTTrainer(
    model=model,
    args=args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["test"],
    peft_config=peft_config,
    processing_class=processor,
    data_collator=collate_fn,
)

# Start training, the model will be automatically saved to the Hub and the output directory
trainer.train()

# Save the final model again to the Hugging Face Hub
trainer.save_model()

[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 1, 'bos_token_id': 2, 'pad_token_id': 0}.

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# free the memory again
del model
del trainer
torch.cuda.empty_cache()

from transformers import AutoModelForMultimodalLM, AutoProcessor
from peft import PeftModel

# Load Model base model
model = AutoModelForMultimodalLM.from_pretrained(model_id, low_cpu_mem_usage=True)

# Merge LoRA and base model and save
peft_model = PeftModel.from_pretrained(model, args.output_dir)
merged_model = peft_model.merge_and_unload()
merged_model.save_pretrained("merged_model", safe_serialization=True, max_shard_size="2GB")

processor = AutoProcessor.from_pretrained("google/gemma-4-E2B-it")
processor.save_pretrained("merged_model")

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['merged_model/processor_config.json']

from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "merged_model"

# Load Model with PEFT adapter
model = AutoModelForMultimodalLM.from_pretrained(
  model_id,
  device_map="auto",
  dtype="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

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from random import randint
import re
from transformers import pipeline, GenerationConfig, pipeline

config = GenerationConfig.from_pretrained(model_id)
config.max_new_tokens = 256
config.eos_token_id = [processor.tokenizer.convert_tokens_to_ids("<turn|>")]

# Load the model and tokenizer into the pipeline
pipe = pipeline("text-generation", model=model, tokenizer=processor.tokenizer)

# Load a random sample from the test dataset
rand_idx = randint(0, len(dataset["test"]))
test_sample = dataset["test"][rand_idx]

# Convert as test example into a prompt with the Gemma template
prompt = processor.tokenizer.apply_chat_template(test_sample["messages"][:2], tokenize=False, add_generation_prompt=True)
print(prompt)

# Generate our SQL query.
outputs = pipe(text_inputs=prompt, generation_config=config)

# Extract the user query and original answer
print(f"Context:\n", re.search(r'<SCHEMA>\n(.*?)\n</SCHEMA>', test_sample['messages'][1]['content'], re.DOTALL).group(1).strip())
print(f"Query:\n", re.search(r'<USER_QUERY>\n(.*?)\n</USER_QUERY>', test_sample['messages'][1]['content'], re.DOTALL).group(1).strip())
print(f"Original Answer:\n{test_sample['messages'][2]['content']}")
print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip().removesuffix("<turn|>")}")

<bos><|turn>system
You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.<turn|>
<|turn>user
Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.

<SCHEMA>
CREATE TABLE Auto_Shows (id INT, show_name VARCHAR(255), show_year INT, location VARCHAR(255)); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (1, 'New York International Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (2, 'Chicago Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (3, 'North American International Auto Show', 2018, 'United States');
</SCHEMA>

<USER_QUERY>
How many auto shows were held in the United States in the year 2019?
</USER_QUERY><turn|>
<|turn>model

Context:
 CREATE TABLE Auto_Shows (id INT, show_name VARCHAR(255), show_year INT, location VARCHAR(255)); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (1, 'New York International Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (2, 'Chicago Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (3, 'North American International Auto Show', 2018, 'United States');
Query:
 How many auto shows were held in the United States in the year 2019?
Original Answer:
SELECT COUNT(*) FROM Auto_Shows WHERE show_year = 2019 AND location = 'United States';
Generated Answer:
SELECT COUNT(*) FROM Auto_Shows WHERE location = 'United States' AND show_year = 2019;