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Rookie/Llama-3-8B-Instruct-Chinese · Hugging Face
%%capture
# Installs Unsloth, Xformers (Flash Attention) and all other packages!
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps xformers trl peft accelerate bitsandbytes
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 = "unsloth/llama-3-8b-Instruct-bnb-4bit",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
#3微调前测试
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:
{}"""
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
alpaca_prompt.format(
"请用中文回答", # instruction
"海绵宝宝的书法是不是叫做海绵体？", # input
"", # output
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
# 弱智吧数据集
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("yahma/alpaca-cleaned", split = "train")
dataset = dataset.map(formatting_prompts_func, batched = True,)
# Moss数据集
from datasets import load_dataset
# 定义常量
EOS_TOKEN = tokenizer.eos_token
alpaca_prompt = "{instruction}\\\\n{input}\\\\n{output}" # 确保这个格式化字符串适用于你的任务
# 格式化函数适用于 `YeungNLP/moss-003-sft-data` 数据集
def formatting_moss_func(examples):
conversations = examples["conversation"]
texts = []
for conversation in conversations:
combined_text = ""
for message in conversation:
if "human" in message and "assistant" in message:
instruction = message["human"]
output = message["assistant"]
combined_text += alpaca_prompt.format(instruction=instruction, input="", output=output) + EOS_TOKEN
texts.append(combined_text)
return {"text": texts}
# 加载数据集并格式化
dataset = load_dataset("YeungNLP/moss-003-sft-data", split="train")
dataset = dataset.map(formatting_moss_func, batched=True)
print(f"Dataset Length: {len(dataset)}")
# 检查数据集的条目数量
print(f"Dataset Example: {dataset[0]}")
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 trl import SFTTrainer
from transformers import TrainingArguments
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,
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,
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",
),
)
trainer_stats = trainer.train()
#7测试微调后的模型
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
alpaca_prompt.format(
"只用中文回答问题", # instruction
"火烧赤壁 曹操为何不拨打119求救？", # input
"", # output
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
#8保存LoRA模型
model.save_pretrained("lora_model") # Local saving
# model.push_to_hub("your_name/lora_model", token = "...") # 在线保存到hugging face，需要token
#9合并模型并量化成4位gguf保存
model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
#model.save_pretrained_merged("outputs", tokenizer, save_method = "merged_16bit",) #合并模型，保存为16位hf
#model.push_to_hub_gguf("hf/model", tokenizer, quantization_method = "q4_k_m", token = "") #合并4位gguf，上传到hugging face(需要账号token)
#10挂载google drive
from google.colab import drive
drive.mount('/content/drive')
#11复制模型到google drive
import shutil
source_file = '/content/model-unsloth.Q4_K_M.gguf'
destination_dir = '/content/drive/MyDrive/Llama3'
destination_file = f'{destination_dir}/model-unsloth.Q4_K_M.gguf'
shutil.copy(source_file, destination_file)