UnslothใฎGoogle Colabใใผใใใใฏใงๆ่ปฝใซLLMใฎ่ธ็ใใงใใ
DeepSeek-R1ใฎ1.58ใใใ้ๅญๅใง้็ซฏใซๆณจ็ฎใฎ็ใซใชใฃใUnslothใ
ๅฝผใใฏๅ
ใ
ใLLMใฎGGUFใชใฉใไฝใฃใฆใใใ่ฆชๅใชใใใซใผใจใใใคใกใผใธใ ใฃใใ
ใใใใไปๅใฎๅ็้ๅญๅใชใฉใๅฐใฃใๆ่กใใฝใใใฆใงใขใใใฑใผใธใซใใฆใใฆใPhi-4ใCommande-Rใชใฉใใใใใใชใชใผใใณใฆใงใคใใขใใซใฎ่ธ็ใ้ๅญๅใ็ฐกๅใซใงใใGoogle Colab Notebookใ้ ใฃใฆใใใ
้ๅญๅใใใใค
่ธ็ใใใใค
ใใใใใใ็ฐกๅใชใฎใงใ่ธ็ใใๆ่ปฝใซ่ฉฆใใใใจใใไบบใซใใฃใฆใคใใ ใจๆใใไฝใใใใใใฎใฏใๅฝผใใๅฎ่ฃ ใใๅ็้ๅญๅใฎใใใใงใ4ใใใLoRA(QLoRA)ใฎๆง่ฝๅฃๅใๆใใใใ็กๆใขใซใฆใณใใงใ7Bใขใใซใใใใชใๅญฆ็ฟใงใใฆใใพใใจใใใใจใ
ๅญฆ็ฟใใฎใใฎใๆฐๅใใๆฐๅๅใง็ตใใฃใฆใใพใใฎใงใใใใพใงใซใใฃใใใใชใ่ธ็/ใใกใคใณใใฅใผใใณใฐใฏๅใๅคงๅคใใจใใใคใกใผใธใงใใชใใ็ฎใใ้ฑใจใใๆใใ
ใใใใใชLLMใ็ฐกๅใซ้ซ้ๅใงใใใฎใง่ฉฆใใฆใฟใใฎใ้ข็ฝใใใใใใชใใ
Unslothใ่ชๅใง็จๆใใ้ๅญๅใขใใซใฎไธ่ฆงใฏใใกใ
่ชๅใงใPhi-4ใฎ่ธ็ใ่ฉฆใใฆใฟใใใๅฐใใใใใฉใใใใใใ
ใพใใxformerใฎใใผใธใงใณใใไฝใใใๆใใชใใจใใใชใใ
Pythonใฏ3.10ใงใCUDA12.1ใงใใฃใใGPUใฏA100 80GBx1ใไฝฟ็จใ
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 unslothใพใใ้จใใใใจๆใฃใฆไธใฎใณใใณใใ้ ใซๅฎ่กใใฆใใ
ใใฎๅพใColabใใผใใใใฏใใๆใๅบใใไปฅไธใฎใณใผใใ้ฉๅฝใชๅๅใงใปใผใใใฆใใ
from unsloth import FastLanguageModel # FastVisionModel for LLMs
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
"unsloth/Meta-Llama-3.1-8B-bnb-4bit", # Llama-3.1 2x faster
"unsloth/Mistral-Small-Instruct-2409", # Mistral 22b 2x faster!
"unsloth/Phi-4", # Phi-4 2x faster!
"unsloth/Phi-4-unsloth-bnb-4bit", # Phi-4 Unsloth Dynamic 4-bit Quant
"unsloth/gemma-2-9b-bnb-4bit", # Gemma 2x faster!
"unsloth/Qwen2.5-7B-Instruct-bnb-4bit" # Qwen 2.5 2x faster!
"unsloth/Llama-3.2-1B-bnb-4bit", # NEW! Llama 3.2 models
"unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
"unsloth/Llama-3.2-3B-bnb-4bit",
"unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
] # More models at https://docs.unsloth.ai/get-started/all-our-models
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/Phi-4",
max_seq_length = max_seq_length,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
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
)
from unsloth.chat_templates import get_chat_template
tokenizer = get_chat_template(
tokenizer,
chat_template = "phi-4",
)
def formatting_prompts_func(examples):
convos = examples["conversations"]
texts = [
tokenizer.apply_chat_template(
convo, tokenize = False, add_generation_prompt = False
)
for convo in convos
]
return { "text" : texts, }
from datasets import load_dataset
dataset = load_dataset("mlabonne/FineTome-100k", split = "train")
pass
from datasets import load_dataset
dataset = load_dataset("mlabonne/FineTome-100k", split = "train")
from datasets import load_dataset
dataset = load_dataset("mlabonne/FineTome-100k", split = "train")
from unsloth.chat_templates import standardize_sharegpt
dataset = standardize_sharegpt(dataset)
dataset = dataset.map(
formatting_prompts_func,
batched=True,
)
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 = 1, # Set this for 1 full training run.
max_steps = 30,
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
),
)
from unsloth.chat_templates import train_on_responses_only
trainer = train_on_responses_only(
trainer,
instruction_part="<|im_start|>user<|im_sep|>",
response_part="<|im_start|>assistant<|im_sep|>",
)
trainer_stats = trainer.train()
ใใใๅฎ่กใใใจใๆฐๅใง่ธ็ใๅฎไบใใใ
๐ฆฅ Unsloth: Will patch your computer to enable 2x faster free finetuning.
๐ฆฅ Unsloth Zoo will now patch everything to make training faster!
==((====))== Unsloth 2025.1.8: Fast Llama patching. Transformers: 4.47.1.
\\ /| GPU: NVIDIA A100 80GB PCIe. Max memory: 79.256 GB. Platform: Linux.
O^O/ \_/ \ Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0
\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]
"-____-" Free Apache license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Loading checkpoint shards: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 3/3 [00:03<00:00, 1.15s/it]
Unsloth 2025.1.8 patched 40 layers with 40 QKV layers, 40 O layers and 40 MLP layers.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1
\\ /| Num examples = 100,000 | Num Epochs = 1
O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4
\ / Total batch size = 8 | Total steps = 30
"-____-" Number of trainable parameters = 65,536,000
0%| | 0/30 [00:00<?, ?it/s]
{'loss': 0.7482, 'grad_norm': 0.17042014002799988, 'learning_rate': 4e-05, 'epoch': 0.0}
{'loss': 0.6629, 'grad_norm': 0.14523565769195557, 'learning_rate': 8e-05, 'epoch': 0.0}
{'loss': 1.0388, 'grad_norm': 0.3025570511817932, 'learning_rate': 0.00012, 'epoch': 0.0}
{'loss': 0.7812, 'grad_norm': 0.22732926905155182, 'learning_rate': 0.00016, 'epoch': 0.0}
{'loss': 0.7112, 'grad_norm': 0.2056725025177002, 'learning_rate': 0.0002, 'epoch': 0.0}
{'loss': 0.8405, 'grad_norm': 0.24817104637622833, 'learning_rate': 0.000192, 'epoch': 0.0}
{'loss': 0.473, 'grad_norm': 0.12781551480293274, 'learning_rate': 0.00018400000000000003, 'epoch': 0.0}
{'loss': 0.8386, 'grad_norm': 0.1284501999616623, 'learning_rate': 0.00017600000000000002, 'epoch': 0.0}
{'loss': 0.6032, 'grad_norm': 0.09723766893148422, 'learning_rate': 0.000168, 'epoch': 0.0}
{'loss': 0.528, 'grad_norm': 0.12025056034326553, 'learning_rate': 0.00016, 'epoch': 0.0}
{'loss': 0.7025, 'grad_norm': 0.10142025351524353, 'learning_rate': 0.000152, 'epoch': 0.0}
{'loss': 0.7531, 'grad_norm': 0.6547413468360901, 'learning_rate': 0.000144, 'epoch': 0.0}
{'loss': 0.7773, 'grad_norm': 0.13310837745666504, 'learning_rate': 0.00013600000000000003, 'epoch': 0.0}
{'loss': 0.4542, 'grad_norm': 0.08028502762317657, 'learning_rate': 0.00012800000000000002, 'epoch': 0.0}
{'loss': 0.6358, 'grad_norm': 0.11321067065000534, 'learning_rate': 0.00012, 'epoch': 0.0}
{'loss': 0.3977, 'grad_norm': 0.09965520352125168, 'learning_rate': 0.00011200000000000001, 'epoch': 0.0}
{'loss': 0.7854, 'grad_norm': 0.11347711086273193, 'learning_rate': 0.00010400000000000001, 'epoch': 0.0}
{'loss': 0.6585, 'grad_norm': 0.1086692288517952, 'learning_rate': 9.6e-05, 'epoch': 0.0}
{'loss': 0.6072, 'grad_norm': 0.1224820464849472, 'learning_rate': 8.800000000000001e-05, 'epoch': 0.0}
{'loss': 0.7727, 'grad_norm': 0.1163286566734314, 'learning_rate': 8e-05, 'epoch': 0.0}
{'loss': 0.734, 'grad_norm': 0.07792412489652634, 'learning_rate': 7.2e-05, 'epoch': 0.0}
{'loss': 0.5374, 'grad_norm': 0.07801351696252823, 'learning_rate': 6.400000000000001e-05, 'epoch': 0.0}
{'loss': 0.9119, 'grad_norm': 0.14749355614185333, 'learning_rate': 5.6000000000000006e-05, 'epoch': 0.0}
{'loss': 0.7244, 'grad_norm': 0.17585600912570953, 'learning_rate': 4.8e-05, 'epoch': 0.0}
{'loss': 0.5003, 'grad_norm': 0.10795021057128906, 'learning_rate': 4e-05, 'epoch': 0.0}
{'loss': 0.5919, 'grad_norm': 0.08922930806875229, 'learning_rate': 3.2000000000000005e-05, 'epoch': 0.0}
{'loss': 0.6601, 'grad_norm': 0.10317248851060867, 'learning_rate': 2.4e-05, 'epoch': 0.0}
{'loss': 0.6584, 'grad_norm': 0.17016059160232544, 'learning_rate': 1.6000000000000003e-05, 'epoch': 0.0}
{'loss': 0.6996, 'grad_norm': 0.10287228971719742, 'learning_rate': 8.000000000000001e-06, 'epoch': 0.0}
{'loss': 0.7713, 'grad_norm': 0.08957896381616592, 'learning_rate': 0.0, 'epoch': 0.0}
{'train_runtime': 138.8609, 'train_samples_per_second': 1.728, 'train_steps_per_second': 0.216, 'train_loss': 0.6853088637193044, 'epoch': 0.0}
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 30/30 [02:18<00:00, 4.63s/it]
ๆ่ปฝใใใฆใณใฃใใใใใใพใใงใlossใใใใชใซไธใใฃใฆใชใใฎใงใใใงใฏๅฎ็จๆงใฏใชใใฎใใใใใชใใใ
