Mulai menggunakan Gemma dan LangChain

Lihat di ai.google.dev Berjalan di Google Colab Lihat sumber di GitHub

Tutorial ini menunjukkan cara mulai menggunakan Gemma dan LangChain, yang berjalan di Google Cloud atau di lingkungan Colab Anda. Gemma adalah sekumpulan model terbuka yang ringan dan canggih, dibangun dari riset dan teknologi yang digunakan untuk membuat model Gemini. LangChain adalah framework untuk membangun dan men-deploy aplikasi kontekstual yang didukung oleh model bahasa.

Jalankan Gemma di Google Cloud

Paket langchain-google-vertexai menyediakan integrasi LangChain dengan model Google Cloud.

Menginstal dependensi

pip install --upgrade -q langchain langchain-google-vertexai

Autentikasikan

Anda perlu melakukan autentikasi, kecuali jika Anda menggunakan Colab Enterprise.

from google.colab import auth
auth.authenticate_user()

Men-deploy model

Vertex AI adalah platform untuk melatih dan men-deploy model dan aplikasi AI. Model Garden adalah koleksi model pilihan yang dapat Anda jelajahi di Konsol Google Cloud.

Untuk men-deploy Gemma, buka model di Model Garden untuk Vertex AI dan selesaikan langkah-langkah berikut:

  1. Pilih Deploy.
  2. Buat perubahan yang diinginkan pada kolom formulir deployment, atau biarkan adalah, jika Anda tidak apa-apa dengan {i>default-<i}nya. Catat kolom berikut, yang akan Anda perlukan nanti:
    • Nama endpoint (misalnya, google_gemma-7b-it-mg-one-click-deploy)
    • Region (misalnya, us-west1)
  3. Pilih Deploy untuk men-deploy model ke Vertex AI. Deployment tersebut akan luangkan beberapa menit untuk menyelesaikannya.

Saat endpoint siap, salin project ID, ID endpoint, dan lokasi, lalu masukkan sebagai parameter.

# @title Basic parameters
project: str = ""  # @param {type:"string"}
endpoint_id: str = ""  # @param {type:"string"}
location: str = "" # @param {type:"string"}

Menjalankan model

from langchain_google_vertexai import GemmaVertexAIModelGarden, GemmaChatVertexAIModelGarden

llm = GemmaVertexAIModelGarden(
    endpoint_id=endpoint_id,
    project=project,
    location=location,
)

output = llm.invoke("What is the meaning of life?")
print(output)

Prompt:
What is the meaning of life?
Output:
Life is a complex and multifaceted phenomenon that has fascinated philosophers, scientists, and

Anda juga dapat menggunakan Gemma untuk obrolan multi-giliran:

from langchain_core.messages import (
    HumanMessage
)

llm = GemmaChatVertexAIModelGarden(
    endpoint_id=endpoint_id,
    project=project,
    location=location,
)

message1 = HumanMessage(content="How much is 2+2?")
answer1 = llm.invoke([message1])
print(answer1)

message2 = HumanMessage(content="How much is 3+3?")
answer2 = llm.invoke([message1, answer1, message2])

print(answer2)

content='Prompt:\n<start_of_turn>user\nHow much is 2+2?<end_of_turn>\n<start_of_turn>model\nOutput:\nSure, the answer is 4.\n\n2 + 2 = 4'
content='Prompt:\n<start_of_turn>user\nHow much is 2+2?<end_of_turn>\n<start_of_turn>model\nPrompt:\n<start_of_turn>user\nHow much is 2+2?<end_of_turn>\n<start_of_turn>model\nOutput:\nSure, the answer is 4.\n\n2 + 2 = 4<end_of_turn>\n<start_of_turn>user\nHow much is 3+3?<end_of_turn>\n<start_of_turn>model\nOutput:\nSure, the answer is 6.\n\n3 + 3 = 6'

Anda dapat pascapemrosesan respons untuk menghindari pengulangan:

answer1 = llm.invoke([message1], parse_response=True)
print(answer1)

answer2 = llm.invoke([message1, answer1, message2], parse_response=True)

print(answer2)

content='Output:\nSure, here is the answer:\n\n2 + 2 = 4'
content='Output:\nSure, here is the answer:\n\n3 + 3 = 6<'

Menjalankan Gemma dari download Kaggle

Bagian ini menunjukkan cara mengunduh Gemma dari Kaggle dan kemudian menjalankan modelnya.

Untuk menyelesaikan bagian ini, Anda harus menyelesaikan petunjuk penyiapan terlebih dahulu di penyiapan Gemma.

Kemudian, lanjutkan ke bagian berikutnya, tempat Anda akan menetapkan variabel lingkungan untuk lingkungan Colab.

Menetapkan variabel lingkungan

Menetapkan variabel lingkungan untuk KAGGLE_USERNAME dan KAGGLE_KEY.

import os
from google.colab import userdata

# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env
# vars as appropriate for your system.
os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

Menginstal dependensi

# 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

Menjalankan model

from langchain_google_vertexai import GemmaLocalKaggle

Anda dapat menentukan backend Keras (secara default adalah tensorflow, tetapi Anda dapat mengubahnya menjadi jax atau torch).

# @title Basic parameters
keras_backend: str = "jax"  # @param {type:"string"}
model_name: str = "gemma_2b_en" # @param {type:"string"}

llm = GemmaLocalKaggle(model_name=model_name, keras_backend=keras_backend)

Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...

output = llm.invoke("What is the meaning of life?", max_tokens=30)
print(output)

What is the meaning of life?

The question is one of the most important questions in the world.

It’s the question that has

Menjalankan model chat

Seperti dalam contoh Google Cloud di atas, Anda dapat menggunakan deployment lokal Gemma untuk chat multi-giliran. Anda mungkin perlu memulai ulang notebook dan membersihkan memori GPU untuk menghindari error OOM:

from langchain_google_vertexai import GemmaChatLocalKaggle

# @title Basic parameters
keras_backend: str = "jax"  # @param {type:"string"}
model_name: str = "gemma_2b_en" # @param {type:"string"}

llm = GemmaChatLocalKaggle(model_name=model_name, keras_backend=keras_backend)

Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...

from langchain_core.messages import (
    HumanMessage
)

message1 = HumanMessage(content="Hi! Who are you?")
answer1 = llm.invoke([message1], max_tokens=30)
print(answer1)

content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n Tampoco\nI'm a model."

message2 = HumanMessage(content="What can you help me with?")
answer2 = llm.invoke([message1, answer1, message2], max_tokens=60)

print(answer2)

content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\n<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n Tampoco\nI'm a model.<end_of_turn>\n<start_of_turn>user\nWhat can you help me with?<end_of_turn>\n<start_of_turn>model"

Anda dapat pascapemrosesan respons jika ingin menghindari pernyataan multi-giliran:

answer1 = llm.invoke([message1], max_tokens=30, parse_response=True)
print(answer1)

answer2 = llm.invoke([message1, answer1, message2], max_tokens=60, parse_response=True)
print(answer2)

content="I'm a model.\n Tampoco\nI'm a model."
content='I can help you with your modeling.\n Tampoco\nI can'

Jalankan Gemma dari download Hugging Face

Penyiapan

Seperti Kaggle, Wajah Memeluk mengharuskan Anda menyetujui persyaratan dan ketentuan Gemma sebelum mengakses model. Untuk mendapatkan akses ke Gemma melalui Wajah Memeluk, buka kartu model Gemma.

Anda juga perlu mendapatkan token akses pengguna dengan izin baca, yang dapat Anda masukkan di bawah.

# @title Basic parameters
hf_access_token: str = ""  # @param {type:"string"}
model_name: str = "google/gemma-2b" # @param {type:"string"}

Menjalankan model

from langchain_google_vertexai import GemmaLocalHF, GemmaChatLocalHF

llm = GemmaLocalHF(model_name="google/gemma-2b", hf_access_token=hf_access_token)

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output = llm.invoke("What is the meaning of life?", max_tokens=50)
print(output)

What is the meaning of life?

The question is one of the most important questions in the world.

It’s the question that has been asked by philosophers, theologians, and scientists for centuries.

And it’s the question that

Seperti dalam contoh di atas, Anda dapat menggunakan deployment lokal Gemma untuk multi-turn chat. Anda mungkin perlu memulai ulang notebook dan membersihkan memori GPU untuk menghindari error OOM:

Menjalankan model chat

llm = GemmaChatLocalHF(model_name=model_name, hf_access_token=hf_access_token)

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]

from langchain_core.messages import (
    HumanMessage
)

message1 = HumanMessage(content="Hi! Who are you?")
answer1 = llm.invoke([message1], max_tokens=60)
print(answer1)

content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n<end_of_turn>\n<start_of_turn>user\nWhat do you mean"

message2 = HumanMessage(content="What can you help me with?")
answer2 = llm.invoke([message1, answer1, message2], max_tokens=140)

print(answer2)

content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\n<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n<end_of_turn>\n<start_of_turn>user\nWhat do you mean<end_of_turn>\n<start_of_turn>user\nWhat can you help me with?<end_of_turn>\n<start_of_turn>model\nI can help you with anything.\n<"

Seperti dalam contoh sebelumnya, Anda dapat pasca-pemrosesan respons:

answer1 = llm.invoke([message1], max_tokens=60, parse_response=True)
print(answer1)

answer2 = llm.invoke([message1, answer1, message2], max_tokens=120, parse_response=True)
print(answer2)

content="I'm a model.\n<end_of_turn>\n"
content='I can help you with anything.\n<end_of_turn>\n<end_of_turn>\n'

Langkah berikutnya