119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
from datasets import load_dataset
|
|
from unsloth import FastLanguageModel
|
|
import torch
|
|
# import os
|
|
# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
|
|
|
|
# 加载 jsonl 文件
|
|
dataset = load_dataset("json", data_files="dataset/test_dataset.jsonl", split="train")
|
|
|
|
# 转换成 ChatML 格式的字符串字段
|
|
# example 相当于jsonl中的每一行
|
|
def to_chatml(example):
|
|
messages = example["messages"]
|
|
chat = ""
|
|
for m in messages:
|
|
# 将原始内容封装为一句话
|
|
chat += f"<|im_start|>{m['role']}\n{m['content']}<|im_end|>\n"
|
|
return {"text": chat.strip()}
|
|
|
|
# 添加 `text` 字段
|
|
dataset = dataset.map(to_chatml)
|
|
|
|
# print("\n", dataset[0])
|
|
|
|
|
|
|
|
|
|
# 加载预训练模型
|
|
model, tokenizer = FastLanguageModel.from_pretrained(
|
|
# model_name = "unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
|
|
model_name = "unsloth/Qwen3-8B-unsloth-bnb-4bit",
|
|
# model_name = "deepseek-ai/DeepSeek-V2-Lite",
|
|
# model_name = "unsloth/Qwen3-4B-unsloth-bnb-4bit",
|
|
trust_remote_code=True, # ✅ 允許遠端自訂程式碼, deepseek 系列模型使用
|
|
max_seq_length = 2048, # Context length - can be longer, but uses more memory
|
|
# max_seq_length = 512, # Context length - can be longer, but uses more memory
|
|
load_in_4bit = True, # 4bit uses much less memory , 启用QLoRA
|
|
load_in_8bit = False, # A bit more accurate, uses 2x memory
|
|
full_finetuning = False, # We have full finetuning now!
|
|
# token = "hf_...", # use one if using gated models
|
|
|
|
|
|
)
|
|
|
|
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, # Best to choose alpha = rank or rank*2
|
|
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, SFTConfig
|
|
trainer = SFTTrainer(
|
|
model = model,
|
|
tokenizer = tokenizer,
|
|
train_dataset = dataset,
|
|
eval_dataset = None, # Can set up evaluation!
|
|
args = SFTConfig(
|
|
dataset_text_field = "text", # 要和 dataset中定义的字段统一
|
|
per_device_train_batch_size = 2,
|
|
gradient_accumulation_steps = 4, # Use GA to mimic batch size!
|
|
warmup_steps = 5,
|
|
# num_train_epochs = 1, # Set this for 1 full training run.
|
|
max_steps = 30,
|
|
learning_rate = 2e-4, # Reduce to 2e-5 for long training runs
|
|
logging_steps = 1,
|
|
optim = "adamw_8bit",
|
|
weight_decay = 0.01,
|
|
lr_scheduler_type = "linear",
|
|
seed = 3407,
|
|
report_to = "none", # Use this for WandB etc
|
|
),
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
|
|
|
|
|
|
messages = [
|
|
{"role" : "user", "content" : "请介绍一下昊天"}
|
|
]
|
|
text = tokenizer.apply_chat_template(
|
|
messages,
|
|
tokenize = False,
|
|
add_generation_prompt = True, # Must add for generation
|
|
enable_thinking = False, # Disable thinking
|
|
)
|
|
|
|
from transformers import TextStreamer
|
|
_ = model.generate(
|
|
**tokenizer(text, return_tensors = "pt").to("cuda"),
|
|
max_new_tokens = 256, # Increase for longer outputs!
|
|
temperature = 0.7, top_p = 0.8, top_k = 20, # For non thinking
|
|
streamer = TextStreamer(tokenizer, skip_prompt = True),
|
|
)
|
|
|
|
# model.cpu()
|
|
|
|
model.save_pretrained_gguf(
|
|
"Qwen3-8B",
|
|
tokenizer,
|
|
# quantization_method="q4_k_m", # 或 "q8_0" # 量化模式--默认 q8_0, 可选f16, "q4_k_m", "q8_0", "q5_k_m",
|
|
maximum_memory_usage=0.7 # 限制使用 GPU 显存为总容量的 50%
|
|
)
|
|
|
|
# print("*"*150, " Modefile")
|
|
# print(tokenizer._ollama_modelfile)
|
|
# print("*"*150)
|