LLM_2024_final_round_model

Uploaded model

• Developed by: nagayaoh
• License: apache-2.0
• Finetuned from model : llm-jp/llm-jp-3-13b

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

---

Introduction

This is a result of LLM2024 competition on UTokyo seminor.

• Dashboard score is 3.05

Including

• Fine-tuned Model
• README (this file)

Dataset

This model used the following dataset for fine tuning only.

How to build this model

You can use Google colab in T4 runtime:

• It takes about 18 - 40 minutes
• If possible, strongly recommend you followings:
  • Use {model|data}.to('cuda') to shorten your learning duration
  • Use A100
• I used following code (removed):
  • Evalution on Learning to avoid over-learning
  • WandB to check parameter tuning

Code

This is ipynb code

# -*- coding: utf-8 -*-
#  !!! paste below code as ipynb in Google Colab

# ===============================================================================================
# --- Install Python Packages
!pip uninstall unsloth -y
!pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" -qU
!pip install --upgrade torch torchvision torchaudio -qU
!pip install --upgrade xformers -qU

# Install Flash Attention 2 for softcapping support
import torch
if torch.cuda.get_device_capability()[0] >= 8:
    !pip install --no-deps packaging ninja einops "flash-attn>=2.6.3" -qU

# ===============================================================================================
# --- Parameter definition
device = "cuda" if torch.cuda.is_available() else "cpu"

# --- Put data files according to path definition
proj_path   = "/content"
input_path  = proj_path + "/input/ichikara-instruction-003-001-1.json"
eval_path   = proj_path + "/eval/elyza-tasks-100-TV_0.jsonl"
result_path = proj_path + "/results"

# ===============================================================================================
# Setting Parameters
model_id = "llm-jp/llm-jp-3-13b"
new_model_id = "llm-jp-3-13b-it"      # Adaptor name
dtype = None                          # None is OK                                                +
load_in_4bit = True                   # True for 13B model
max_seq_length = 512                  # Any length can be used because of RoPE

# パラメータをPack
config={
    "model_id": model_id,
    "learning_rate": 2e-5,
    "per_device_train_batch_size": 4,
    "gradient_accumulation_steps": 4,
    "num_train_epochs":3,
    "warmup_steps": 10,
    "max_steps": -1,
    "lora_r": 32,
    "lora_alpha": 32,
    "lora_dropout": 0.05,
    "lora_bias": "none",
    "lora_use_rslora": False,
    "lora_loftq_config": None,
    "model_max_seq_length": max_seq_length,
    "model_dtype": dtype,
    "model_load_in_4bit": load_in_4bit,
    "seed": 3407,
    "max_seq_length": max_seq_length
}

# ===============================================================================================
# Load llm-jp/llm-jp-3-13b as 4bit-quantized qLoRA
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(   
    model_name   = config.model_id,
    dtype        = config.model_dtype,
    load_in_4bit = config.model_load_in_4bit,
    trust_remote_code= True,
)

model = FastLanguageModel.get_peft_model(     
    model,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",],
    r              = config.lora_r,
    lora_alpha     = config.lora_alpha,
    lora_dropout   = config.lora_dropout,
    bias           = config.lora_bias,
    use_rslora     = config.lora_use_rslora,
    loftq_config   = config.lora_loftq_config,
    max_seq_length = config.max_seq_length,
    use_gradient_checkpointing = "unsloth",
    random_state   = config.seed,
)

# ===============================================================================================
# Load dataset and spilit into train and test
from datasets import load_dataset
train_dataset = load_dataset("json", data_files= input_path, split="train[:80%]" )
test_dataset = load_dataset("json", data_files= input_path, split="train[80%:]")

# Formatting in prompt style
EOS_TOKEN = tokenizer.eos_token    
prompt = f"""### 指示\n{input}\n### 回答\n"""

def formatting_prompts_func(examples):
    input = examples["text"]                          
    output = examples["output"]                       
    text = prompt.format(input, output) + EOS_TOKEN   
    return { "formatted_text" : text, }               # Retrun new field "formatted_text"

# Assign Prompt style
train_dataset = train_dataset.map( formatting_prompts_func, num_proc= 4 )
test_dataset = test_dataset.map( formatting_prompts_func, num_proc= 4 )

# ===============================================================================================
from trl import SFTTrainer
from transformers import TrainingArguments, EarlyStoppingCallback
from unsloth import is_bfloat16_supported

# EarlyStoppingCallback
early_stopping_callback = EarlyStoppingCallback(
    early_stopping_patience  = 3,   
    early_stopping_threshold = 0.0
)

trainer = SFTTrainer(
    model              = model,
    tokenizer          = tokenizer,
    train_dataset      = train_dataset,
    eval_dataset       = test_dataset,
    max_seq_length     = config.max_seq_length,
    dataset_text_field = "formatted_text",
    packing            = False,
    callbacks=[early_stopping_callback],   
    args = TrainingArguments(
        per_device_train_batch_size = config.per_device_train_batch_size,
        gradient_accumulation_steps = config.gradient_accumulation_steps,
        num_train_epochs = config.num_train_epochs,
        warmup_steps     = config.warmup_steps,
        max_steps        = config.max_steps,
        learning_rate    = config.learning_rate,
        seed             = config.seed,
        evaluation_strategy = "steps",       
        eval_steps       = 20,              
        save_strategy    = "steps",          
        save_steps       = 60,               
        save_total_limit = 3,
        load_best_model_at_end = True,        
        metric_for_best_model  = "eval_loss", 
        greater_is_better      = False,      
        output_dir       = "outputs",
        report_to        = "wandb",
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        group_by_length  = True,
        logging_steps    = 10,
    ),
)

# ===============================================================================================
# Trainning
trainer_stats = trainer.train()

# ===============================================================================================
# Load Elyza-100 tasks
import json
eval_datasets = []
elyza_tasks_path = eval_path
with open(elyza_tasks_path, "r") as f:
    item = ""
    for line in f:
      line = line.strip()
      item += line
      if item.endswith("}"):
        eval_datasets.append(json.loads(item))
        item = ""

# Do tasks
from tqdm import tqdm
FastLanguageModel.for_inference(model)
model.eval()
results = []
for dt in tqdm(eval_datasets):
  input = dt["input"]
  prompt = f"""### 指示\n{input}\n### 回答\n"""
  inputs = tokenizer([prompt], return_tensors = "pt")
  outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
  prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
  results.append({"task_id": dt["task_id"], "input": input, "output": prediction})

# ===============================================================================================
# Save result as jsonl
jsonl_result_path = result_path + f"/{new_model_id}_output.jsonl",
with open( jsonl_result_path, 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')

# Send LoRA adaptors into HugginFace modelcard page
HF_TOKEN = "Please replace here by hugging face Access token"
model.push_to_hub_merged(
    new_model_id+"_lora",
    tokenizer=tokenizer,
    save_method="lora",
    token=HF_TOKEN,
    private=False
)