HashTag766/QwenAssistant

Overview

Finetuned Qwen2.5-3B

the training was for increasing the model capabilities on Instruction following and specific data.

Training Time : 14.5h

Datasets

SMART-Goals-Validation------[https://huggingface.co/datasets/HashTag766/SMART-Goals-Validation]

open-ocra-alpaca-cleaned----[https://huggingface.co/datasets/diabolic6045/open-ocra-alpaca-cleaned] only on 120000k examples

Uploaded model

• Developed by: HashTag766
• License: apache-2.0
• Finetuned from model : SaintHoney/PersonalManV1.0

The code used for finetuning

%%capture
!pip install pip3-autoremove
!pip-autoremove torch torchvision torchaudio -y
!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121
!pip install unsloth

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from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient() # from kaggle_secrets import UserSecretsClient
hugging_face_token = user_secrets.get_secret("HF-Token")

# Login to Hugging Face
from huggingface_hub import login  # Lets you login to API
login(hugging_face_token) # from huggingface_hub import login

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from unsloth import FastLanguageModel
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "SaintHoney/PersonalManV1.0",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

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model = FastLanguageModel.get_peft_model(
    model,
    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 16,
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    use_rslora = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}
    
### Response:
{}"""

EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):
    instructions = examples["instruction"]
    inputs       = examples["input"]
    outputs      = examples["output"]
    texts = []
    for instruction, input, output in zip(instructions, inputs, outputs):
        # Must add EOS_TOKEN, otherwise your generation will go on forever!
        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
        texts.append(text)
    return { "text" : texts, }
pass

from datasets import load_dataset
dataset = load_dataset("HashTag766/SMART-Goals-Validation", split = "train") # specify here the number of examples from dataset
dataset = dataset.map(formatting_prompts_func, batched = True,)

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from trl import SFTTrainer
from transformers import TrainingArguments, DataCollatorForSeq2Seq
from unsloth import is_bfloat16_supported

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),
    dataset_num_proc = 2,
    packing = False, # Can make training 5x faster for short sequences.
    args = TrainingArguments(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 5,
        num_train_epochs = 3, # Set this for 1 full training run.
        # max_steps = 60,
        learning_rate = 2e-4,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit",
        weight_decay = 0.01,
        lr_scheduler_type = "linear",
        seed = 3407,
        output_dir = "outputs",
        report_to = "none", # Use this for WandB etc
    ),
)

trainer_stats = trainer.train()
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model.push_to_hub("hf/model...", token = "...") # Online saving
tokenizer.push_to_hub("hf/model...", token = "...") # Online saving

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.