Unsloth - 大模型微调

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文章目录

一、关于 Unsloth

Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory

主要特点⭐

  • 所有内核用OpenAI的Triton语言编写。手动反推引擎。
  • 0%的精度损失-没有近似方法-全部精确。
  • 硬件不变。自2018年以来支持NVIDIA GPU。
    最低CUDA能力7.0（V100、T4、Titan V、RTX 20、30、40x、A100、H100、L40等）检查您的GPU！
    GTX 1070、1080工作正常，但速度很慢。
  • 通过WSL在Linux和Windows上工作。
  • 通过bitsandbytes支持4bit和16bit QLoRA/LoRA微调。
  • 开源训练速度快5倍 – 请参阅Unslth Pro，训练速度快30倍！
  • 如果你训练了一个模型与🦥树懒，你可以使用这个很酷的贴纸！

二、安装说明 💾

1、使用 Conda

选择pytorch-cuda=11.8用于CUDA 11.8 或 pytorch-cuda=12.1用于CUDA 12.1。如果您有mamba，请使用mamba而不是conda以更快地解决。有关调试Conda安装的帮助，请参阅此Github问题。

conda create --name unsloth_env \
    python=3.10 \
    pytorch-cuda=<11.8/12.1> \
    pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
    -y
conda activate unsloth_env

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

pip install --no-deps "trl<0.9.0" peft accelerate bitsandbytes

2、使用 Pip安装

如果你有蟒蛇，不要使用这个。你必须使用Conda安装方法，否则东西会坏掉。

1、通过以下方式查找您的CUDA版本

import torch; torch.version.cuda

2、对于Pytorch2.1.0：您可以通过Pip 更新 Pytorch（交换cu121/cu118）。转到https://pytorch.org/了解更多信息。

为CUDA 11.8选择cu118 或 为CUDA 12.1选择cu121。

如果您有RTX 3060或更高版本（A100、H100等），请使用"ampere"路径。

对于 Pytorch 2.1.1：转到步骤3。

对于Pytorch2.2.0：转到步骤4。

pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.0 triton \
  --index-url https://download.pytorch.org/whl/cu121

pip install "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere] @ git+https://github.com/unslothai/unsloth.git"

3、对于Pytorch2.1.1：将"ampere"路径用于较新的RTX 30xx GPU或更高版本。

pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.1 triton \
  --index-url https://download.pytorch.org/whl/cu121

pip install "unsloth[cu118-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch211] @ git+https://github.com/unslothai/unsloth.git"

4、对于Pytorch2.2.0：将"ampere"路径用于较新的RTX 30xx GPU或更高版本。

pip install --upgrade --force-reinstall --no-cache-dir torch==2.2.0 triton \
  --index-url https://download.pytorch.org/whl/cu121

pip install "unsloth[cu118-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch220] @ git+https://github.com/unslothai/unsloth.git"

5、如果出现错误，请先尝试以下操作，然后返回步骤1：

pip install --upgrade pip

6、对于Pytorch2.2.1：

# RTX 3090, 4090 Ampere GPUs:
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes

# Pre Ampere RTX 2080, T4, GTX 1080 GPUs:
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes

7、对于Pytorch2.3.0：将"ampere"路径用于较新的RTX 30xx GPU或更高版本。

pip install "unsloth[cu118-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"

8、要对安装进行故障排除，请尝试以下操作（所有操作都必须成功）。

nvcc
python -m xformers.info
python -m bitsandbytes

三、文档📜

  • 转到我们的Wiki页面保存到GGUF，检查点，评估和更多！
  • 我们支持Huggingface的TRL、Trainer、Seq2SeqTrainer甚至Pytorch代码！
  • 我们在🤗拥抱脸的官方文档中！查看SFT文档和DPO文档！
from unsloth import FastLanguageModel 
from unsloth import is_bfloat16_supported
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset
max_seq_length = 2048 # Supports RoPE Scaling interally, so choose any!
# Get LAION dataset
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
dataset = load_dataset("json", data_files = {"train" : url}, split = "train")

# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
    "unsloth/mistral-7b-v0.3-bnb-4bit",      # New Mistral v3 2x faster!
    "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
    "unsloth/llama-3-8b-bnb-4bit",           # Llama-3 15 trillion tokens model 2x faster!
    "unsloth/llama-3-8b-Instruct-bnb-4bit",
    "unsloth/llama-3-70b-bnb-4bit",
    "unsloth/Phi-3-mini-4k-instruct",        # Phi-3 2x faster!
    "unsloth/Phi-3-medium-4k-instruct",
    "unsloth/mistral-7b-bnb-4bit",
    "unsloth/gemma-7b-bnb-4bit",             # Gemma 2.2x faster!
] # More models at https://huggingface.co/unsloth

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
    model,
    r = 16,
    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,
    max_seq_length = max_seq_length,
    use_rslora = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

trainer = SFTTrainer(
    model = model,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    tokenizer = tokenizer,
    args = TrainingArguments(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 10,
        max_steps = 60,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        output_dir = "outputs",
        optim = "adamw_8bit",
        seed = 3407,
    ),
)
trainer.train()

# Go to https://github.com/unslothai/unsloth/wiki for advanced tips like
# (1) Saving to GGUF / merging to 16bit for vLLM
# (2) Continued training from a saved LoRA adapter
# (3) Adding an evaluation loop / OOMs
# (4) Customized chat templates

四、DPO支持

DPO（直接偏好优化）、PPO、奖励建模似乎都符合骆驼工厂的第三方独立测试。我们有一个初步的谷歌Colab笔记本，用于在特斯拉T4上复制Zephy r：笔记本。

我们在🤗拥抱脸的官方文档里！我们在SFT文档和DPO文档里！

from unsloth import FastLanguageModel, PatchDPOTrainer
from unsloth import is_bfloat16_supported
PatchDPOTrainer()
import torch
from transformers import TrainingArguments
from trl import DPOTrainer

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/zephyr-sft-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
    model,
    r = 64,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 64,
    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,
    max_seq_length = max_seq_length,
)

dpo_trainer = DPOTrainer(
    model = model,
    ref_model = None,
    args = TrainingArguments(
        per_device_train_batch_size = 4,
        gradient_accumulation_steps = 8,
        warmup_ratio = 0.1,
        num_train_epochs = 3,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit",
        seed = 42,
        output_dir = "outputs",
    ),
    beta = 0.1,
    train_dataset = YOUR_DATASET_HERE,
    # eval_dataset = YOUR_DATASET_HERE,
    tokenizer = tokenizer,
    max_length = 1024,
    max_prompt_length = 512,
)
dpo_trainer.train()

伊织 + NMT
2024-07-12(五)

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