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from datasets import load_dataset
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import os
base_model_id = "mistralai/Mistral-7B-Instruct-v0.1"
WORK = "vn_v2"
new_model_id = f"kmichiru/Nikaido-7B-mistral-instruct-v0.3-{WORK}"
# DSET = {
# "train": f"dataset_iroseka/{WORK}_dataset.jsonl",
# "eval": f"dataset_iroseka/{WORK}_validations.jsonl"
# }
DSET = {
"train": f"dataset_iroseka/{WORK}_train.jsonl",
"eval": f"dataset_iroseka/{WORK}_val.jsonl"
}
dataset = load_dataset("json", data_files=DSET)
# model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
# max_length = 1024
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
def dialogue(role, content):
return {
"role": role,
"content": content
}
def format_chat_history(example):
user_msgs = []
for msg in example["messages"]:
if msg["role"] == "user":
user_msgs.append(msg["content"])
messages = [
dialogue("user", "\n".join(user_msgs)), # join user messages together
example["messages"][-1], # the last message is the bot's response
]
encodeds = tokenizer.apply_chat_template(messages, tokenize=False)
return encodeds
def prep_speaker(msg: str):
msg = msg.replace("\u3000", " ") # replace full-width spaces
speaker, content = msg.split(":", 1)
speaker = speaker.strip()
content = content.strip()
if len(speaker) == 0:
speaker = "傍白"
return f"{speaker}: {content}"
def format_chat_history_v2(example):
user_msg = []
user_msg.append("<s>")
for msg in example["messages"]:
# [INST] What is your favourite condiment? [/INST]
if msg["role"] != "system":
user_msg.append(f"[INST] {prep_speaker(msg['content'])} [/INST]")
# user_msg.append("</s>")
return " ".join(user_msg)
# def format_chat_history_v2(example):
# user_msgs = []
# for msg in example["messages"]:
# if msg["role"] == "user":
# user_msgs.append(msg["content"])
# messages = [
# dialogue("user", "\n".join(user_msgs)), # join user messages together
# example["messages"][-1], # the last message is the bot's response
# ]
# encodeds = tokenizer.apply_chat_template(messages, tokenize=False)
# return encodeds
print(format_chat_history_v2(dataset['train'][0]))
def generate_and_tokenize_prompt(prompt, max_length=2048):
result = tokenizer(
format_chat_history_v2(prompt),
truncation=True,
max_length=max_length,
padding="max_length",
)
result["labels"] = result["input_ids"]
return result
tokenized_dataset = dataset.map(generate_and_tokenize_prompt)
print(tokenized_dataset['train'][0])
# # stats data length
# def plot_data_lengths(tokenized_dataset):
# lengths = []
# for split in tokenized_dataset:
# lengths += [len(x['input_ids']) for x in tokenized_dataset[split]]
# print(f"Max length: {max(lengths)}")
# print(f"Min length: {min(lengths)}")
# print(f"Mean length: {sum(lengths)/len(lengths)}")
# print(f"Median length: {sorted(lengths)[len(lengths)//2]}")
# plot_data_lengths(tokenized_dataset)
print(tokenized_dataset['train'][0])
#Adding the adapters in the layers
from peft import LoraConfig, get_peft_model
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params:,} || all params: {all_param:,} || trainable%: {100 * trainable_params / all_param}"
)
model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.bfloat16)
# model = prepare_model_for_kbit_training(model)
peft_config = LoraConfig(
r=64,
lora_alpha=64,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj","gate_proj", "up_proj", "down_proj"]
)
model = get_peft_model(model, peft_config)
print_trainable_parameters(model)
print(model)
import wandb, os
# wandb.login()
wandb_project = "NikaidoLM"
if len(wandb_project) > 0:
os.environ["WANDB_PROJECT"] = wandb_project
import transformers
from datetime import datetime
project = wandb_project
base_model_name = "mistral"
run_name = base_model_name + "-" + project
output_name = f"{run_name}-{WORK}-{datetime.now().strftime('%Y-%m-%d-%H-%M')}"
output_dir = "/scratch/generalvision/mowentao/mistral-out/" + output_name
trainer = transformers.Trainer(
model=model,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["eval"],
args=transformers.TrainingArguments(
output_dir=output_dir,
warmup_steps=500,
per_device_train_batch_size=1,
gradient_accumulation_steps=2,
num_train_epochs=3,
weight_decay=5e-4,
# max_steps=10_000,
learning_rate=1e-4, # Want a small lr for finetuning
bf16=True,
optim="paged_adamw_32bit",
logging_steps=100, # When to start reporting loss
logging_dir=output_dir, # Directory for storing logs
save_strategy="steps", # Save the model checkpoint every logging step
save_steps=500, # Save checkpoints every 50 steps
evaluation_strategy="steps", # Evaluate the model every logging step
eval_steps=100, # Evaluate and save checkpoints every 50 steps
do_eval=True, # Perform evaluation at the end of training
report_to="wandb", # Comment this out if you don't want to use weights & baises
run_name=output_name, # Name of the W&B run (optional)
lr_scheduler_type="cosine",
),
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
trainer.train()
trainer.model.save_pretrained(new_model_id)
wandb.finish()
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