一、前置条件

获得模型访问权，选择Colab运行时，配置训练环境。

先在Kaggle上注册，然后获得Gemma 2B 的访问权；

然后在Google colab 配置环境，主要是GPU的选择，免费的是T4，建议采用付费的A100（为了节省时间，微调训练耗时T4需要30分钟左右，A100只需要2分钟左右）

最后 在Kaggle 上的account上生成令牌文件（主要是usename 和 API Key），并将令牌文件配置到colab环境。

二、微调步骤

三、源码

# -*- coding: utf-8 -*-
"""gemma-lora微调.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1_uEbuYP-vk0tCO0EA7IQ1t85jJ6ne7Qi
"""

from google.colab import files
uploaded = files.upload()

!mkdir ~/.kaggle
!mv kaggle.json ~/.kaggle/

!chmod 600 ~/.kaggle/kaggle.json

# Install Keras 3 last. See https://keras.io/getting_started/ for more details.
!pip install -q -U keras-nlp
!pip install -q -U keras>=3

import os

os.environ["KERAS_BACKEND"] = "jax"  # Or "torch" or "tensorflow".
# Avoid memory fragmentation on JAX backend.
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"

import keras
import keras_nlp

!wget -O databricks-dolly-15k.jsonl https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl

import json
data = []
with open("databricks-dolly-15k.jsonl") as file:
    for line in file:
        features = json.loads(line)
        # Filter out examples with context, to keep it simple.
        if features["context"]:
            continue
        # Format the entire example as a single string.
        template = "Instruction:\n{instruction}\n\nResponse:\n{response}"
        data.append(template.format(**features))

# Only use 1000 training examples, to keep it fast.
data = data[:1000]

gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
gemma_lm.summary()

prompt = template.format(
    instruction="What should I do on a trip to Europe?",
    response="",
)
sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)
gemma_lm.compile(sampler=sampler)
print(gemma_lm.generate(prompt, max_length=256))

prompt = template.format(
    instruction="Explain the process of photosynthesis in a child could understand.",
    response="",
)
print(gemma_lm.generate(prompt,max_length=256))

# Enable LoRA for the model and set the LoRA rank to 4.
gemma_lm.backbone.enable_lora(rank=4)
gemma_lm.summary()

# Limit the input sequence length to 512 (to control memory usage).
gemma_lm.preprocessor.sequence_length = 512
# Use AdamW (a common optimizer for transformer models).
optimizer = keras.optimizers.AdamW(
    learning_rate=5e-5,
    weight_decay=0.01,
)
# Exclude layernorm and bias terms from decay.
optimizer.exclude_from_weight_decay(var_names=["bias", "scale"])

gemma_lm.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=optimizer,
    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
gemma_lm.fit(data, epochs=1, batch_size=1)

prompt = template.format(
    instruction="What should I do on a trip to Europe?",
    response="",
)
sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)
gemma_lm.compile(sampler=sampler)
print(gemma_lm.generate(prompt, max_length=256))

prompt = template.format(
    instruction="Explain the process of photosynthesis in a way that a child could understand.",
    response="",
)
print(gemma_lm.generate(prompt, max_length=256))

微调关键代码--A100