Inferensi dengan Gemma menggunakan JAX dan Flax

Lihat di ai.google.dev Berjalan di Google Colab Buka di Vertex AI Lihat sumber di GitHub

Ringkasan

Gemma adalah sekumpulan model bahasa besar terbuka yang ringan dan canggih, berdasarkan riset dan teknologi Gemini dari Google DeepMind. Tutorial ini menunjukkan cara melakukan pengambilan sampel/inferensi dasar dengan model Instruct Gemma 2B menggunakan library gemma Google DeepMind yang ditulis dengan JAX (library komputasi numerik berperforma tinggi), Flax (library jaringan neural berbasis JAX), Orbax (library berbasis JAX untuk utilitas pelatihan seperti library checkpointing), dan SentencePiece Meskipun Flax tidak digunakan secara langsung di notebook ini, Flax digunakan untuk membuat Gemma.

Notebook ini dapat berjalan di Google Colab dengan GPU T4 gratis (buka Edit > Setelan notebook > Di bagian Akselerator hardware, pilih T4 GPU).

Penyiapan

1. Menyiapkan akses Kaggle untuk Gemma

Untuk menyelesaikan tutorial ini, pertama-tama Anda harus mengikuti petunjuk penyiapan di penyiapan Gemma, yang menunjukkan cara melakukan hal berikut:

  • Dapatkan akses ke Gemma di kaggle.com.
  • Pilih runtime Colab dengan resource yang memadai untuk menjalankan model Gemma.
  • Membuat dan mengkonfigurasi nama pengguna dan kunci API Kaggle.

Setelah Anda menyelesaikan penyiapan Gemma, lanjutkan ke bagian berikutnya, untuk menetapkan variabel lingkungan untuk lingkungan Colab Anda.

2. Menetapkan variabel lingkungan

Menetapkan variabel lingkungan untuk KAGGLE_USERNAME dan KAGGLE_KEY. Saat melihat dialog "Berikan akses?", pesan, setuju untuk memberikan akses rahasia.

import os
from google.colab import userdata # `userdata` is a Colab API.

os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

3. Menginstal library gemma

Notebook ini berfokus pada penggunaan GPU Colab gratis. Untuk mengaktifkan akselerasi hardware, klik Edit > Setelan notebook > Pilih T4 GPU > Simpan.

Selanjutnya, Anda perlu menginstal library gemma Google DeepMind dari github.com/google-deepmind/gemma. Jika mendapatkan error tentang "resolver dependensi pip", Anda biasanya bisa mengabaikannya.

pip install -q git+https://github.com/google-deepmind/gemma.git

Memuat dan menyiapkan model Gemma

  1. Muat model Gemma dengan kagglehub.model_download, yang menggunakan tiga argumen:
  • handle: Handle model dari Kaggle
  • path: (String opsional) Jalur lokal
  • force_download: (Boolean opsional) Memaksa untuk mendownload ulang model
GEMMA_VARIANT = 'gemma2-2b-it' # @param ['gemma2-2b', 'gemma2-2b-it'] {type:"string"}

import kagglehub

GEMMA_PATH = kagglehub.model_download(f'google/gemma-2/flax/{GEMMA_VARIANT}')

Downloading 11 files:   0%|          | 0/11 [00:00<?, ?it/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/manifest.ocdbt...
100%|██████████| 180/180 [00:00<00:00, 101kB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/d/b5a4695f4be0a2f41ec1e25616ebd7e7...
100%|██████████| 2.66k/2.66k [00:00<00:00, 5.36MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/descriptor/descriptor.pbtxt...
100%|██████████| 45.0/45.0 [00:00<00:00, 90.0kB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/_METADATA...
100%|██████████| 55.3k/55.3k [00:00<00:00, 29.5MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/_CHECKPOINT_METADATA...
100%|██████████| 92.0/92.0 [00:00<00:00, 234kB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/bf69258061ae5f35eb7a5669fe6877d4...
0%|          | 0.00/2.12G [00:00<?, ?B/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/fc20151969d7ca91ea9d8275bda0e219...
100%|██████████| 2.64k/2.64k [00:00<00:00, 5.58MB/s]

  0%|          | 2.00M/2.12G [00:00<01:48, 20.8MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/834bb4bf1e3854eb09f6208c95c071b2...
0%|          | 0.00/1.70G [00:00<?, ?B/s]
  0%|          | 9.00M/2.12G [00:00<00:46, 48.2MB/s]

  0%|          | 3.00M/1.70G [00:00<01:06, 27.6MB/s]
  1%|          | 14.0M/2.12G [00:00<00:46, 48.6MB/s]

  1%|          | 9.00M/1.70G [00:00<00:40, 44.5MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/manifest.ocdbt...
100%|██████████| 118/118 [00:00<00:00, 303kB/s]

  1%|          | 21.0M/2.12G [00:00<00:41, 53.7MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/checkpoint...
0%|          | 0.00/22.5k [00:00<?, ?B/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/tokenizer.model...
100%|██████████| 22.5k/22.5k [00:00<00:00, 24.7MB/s]


  1%|          | 17.0M/1.70G [00:00<00:36, 49.5MB/s]


  0%|          | 0.00/4.04M [00:00<?, ?B/s]
100%|██████████| 4.04M/4.04M [00:00<00:00, 64.6MB/s]


  1%|▏         | 24.0M/1.70G [00:00<00:34, 52.7MB/s]
  2%|▏         | 40.0M/2.12G [00:00<00:34, 64.6MB/s]

  2%|▏         | 33.0M/1.70G [00:00<00:27, 64.4MB/s]
  2%|▏         | 49.0M/2.12G [00:00<00:34, 64.8MB/s]

  3%|▎         | 47.0M/1.70G [00:00<00:20, 86.9MB/s]
  3%|▎         | 59.0M/2.12G [00:00<00:29, 74.4MB/s]

  3%|▎         | 56.0M/1.70G [00:00<00:24, 73.1MB/s]
  3%|▎         | 67.0M/2.12G [00:01<00:31, 70.1MB/s]

  4%|▎         | 64.0M/1.70G [00:01<00:25, 69.4MB/s]
  3%|▎         | 74.0M/2.12G [00:01<00:32, 67.4MB/s]

  4%|▍         | 73.0M/1.70G [00:01<00:23, 75.7MB/s]
  4%|▍         | 84.0M/2.12G [00:01<00:28, 75.5MB/s]

  5%|▍         | 81.0M/1.70G [00:01<00:22, 77.7MB/s]

  5%|▌         | 95.0M/1.70G [00:01<00:17, 96.5MB/s]
  4%|▍         | 92.0M/2.12G [00:01<00:38, 56.7MB/s]

  6%|▌         | 106M/1.70G [00:01<00:17, 101MB/s]  
  5%|▍         | 102M/2.12G [00:01<00:32, 67.2MB/s] 

  7%|▋         | 117M/1.70G [00:01<00:16, 102MB/s]
  5%|▌         | 110M/2.12G [00:01<00:30, 70.5MB/s]

  7%|▋         | 128M/1.70G [00:01<00:16, 105MB/s]
  5%|▌         | 119M/2.12G [00:01<00:28, 75.6MB/s]

  8%|▊         | 142M/1.70G [00:01<00:14, 117MB/s]
  6%|▌         | 129M/2.12G [00:02<00:30, 70.1MB/s]

  9%|▉         | 154M/1.70G [00:01<00:17, 92.5MB/s]
  6%|▋         | 138M/2.12G [00:02<00:28, 73.9MB/s]

  9%|▉         | 164M/1.70G [00:02<00:18, 87.7MB/s]
  7%|▋         | 146M/2.12G [00:02<00:30, 70.2MB/s]

 10%|▉         | 173M/1.70G [00:02<00:20, 81.7MB/s]
  7%|▋         | 153M/2.12G [00:02<00:33, 63.0MB/s]

 10%|█         | 182M/1.70G [00:02<00:19, 82.8MB/s]
  8%|▊         | 164M/2.12G [00:02<00:27, 75.3MB/s]

 11%|█         | 195M/1.70G [00:02<00:17, 90.8MB/s]
  8%|▊         | 174M/2.12G [00:02<00:25, 82.0MB/s]

 12%|█▏        | 207M/1.70G [00:02<00:16, 99.0MB/s]
  9%|▊         | 186M/2.12G [00:02<00:22, 92.9MB/s]

 13%|█▎        | 218M/1.70G [00:02<00:16, 99.7MB/s]
  9%|▉         | 196M/2.12G [00:02<00:22, 92.4MB/s]

 13%|█▎        | 229M/1.70G [00:02<00:15, 103MB/s] 
 10%|▉         | 206M/2.12G [00:02<00:22, 92.1MB/s]

 14%|█▎        | 239M/1.70G [00:02<00:15, 99.4MB/s]
 10%|▉         | 215M/2.12G [00:03<00:22, 91.3MB/s]

 14%|█▍        | 250M/1.70G [00:03<00:15, 101MB/s] 
 10%|█         | 226M/2.12G [00:03<00:21, 96.5MB/s]

 15%|█▌        | 263M/1.70G [00:03<00:14, 108MB/s]
 11%|█         | 238M/2.12G [00:03<00:19, 105MB/s] 
 11%|█▏        | 249M/2.12G [00:03<00:19, 103MB/s]

 16%|█▌        | 274M/1.70G [00:03<00:16, 91.6MB/s]
 12%|█▏        | 259M/2.12G [00:03<00:21, 93.3MB/s]

 16%|█▋        | 284M/1.70G [00:03<00:20, 76.0MB/s]
 12%|█▏        | 269M/2.12G [00:03<00:21, 94.3MB/s]

 17%|█▋        | 295M/1.70G [00:03<00:18, 84.0MB/s]
 13%|█▎        | 279M/2.12G [00:03<00:20, 94.2MB/s]

 17%|█▋        | 304M/1.70G [00:03<00:17, 84.4MB/s]
 13%|█▎        | 289M/2.12G [00:03<00:20, 94.9MB/s]

 18%|█▊        | 313M/1.70G [00:03<00:18, 81.9MB/s]
 14%|█▍        | 299M/2.12G [00:03<00:21, 91.4MB/s]
 14%|█▍        | 308M/2.12G [00:04<00:21, 89.4MB/s]

 18%|█▊        | 322M/1.70G [00:03<00:20, 73.5MB/s]

 19%|█▉        | 330M/1.70G [00:04<00:19, 74.9MB/s]
 15%|█▍        | 317M/2.12G [00:04<00:23, 81.1MB/s]
 15%|█▌        | 326M/2.12G [00:04<00:23, 83.6MB/s]

 19%|█▉        | 338M/1.70G [00:04<00:20, 72.0MB/s]

 20%|█▉        | 346M/1.70G [00:04<00:19, 74.6MB/s]
 15%|█▌        | 335M/2.12G [00:04<00:24, 79.2MB/s]

 20%|██        | 354M/1.70G [00:04<00:19, 75.0MB/s]
 16%|█▌        | 344M/2.12G [00:04<00:23, 81.4MB/s]
 16%|█▋        | 352M/2.12G [00:04<00:28, 67.3MB/s]

 21%|██        | 362M/1.70G [00:04<00:26, 54.3MB/s]
 17%|█▋        | 359M/2.12G [00:04<00:31, 59.6MB/s]

 21%|██        | 369M/1.70G [00:04<00:26, 53.4MB/s]
 17%|█▋        | 366M/2.12G [00:05<00:31, 59.0MB/s]

 22%|██▏       | 375M/1.70G [00:04<00:26, 54.9MB/s]
 17%|█▋        | 372M/2.12G [00:05<00:31, 59.2MB/s]

 22%|██▏       | 381M/1.70G [00:05<00:25, 56.2MB/s]
 17%|█▋        | 379M/2.12G [00:05<00:30, 62.3MB/s]

 22%|██▏       | 388M/1.70G [00:05<00:24, 56.8MB/s]
 18%|█▊        | 386M/2.12G [00:05<00:29, 63.8MB/s]

 23%|██▎       | 395M/1.70G [00:05<00:23, 60.2MB/s]
 18%|█▊        | 394M/2.12G [00:05<00:27, 68.5MB/s]

 23%|██▎       | 402M/1.70G [00:05<00:22, 62.7MB/s]
 19%|█▊        | 401M/2.12G [00:05<00:27, 66.3MB/s]

 23%|██▎       | 409M/1.70G [00:05<00:21, 64.4MB/s]
 19%|█▉        | 408M/2.12G [00:05<00:28, 65.4MB/s]

 24%|██▍       | 416M/1.70G [00:05<00:21, 65.4MB/s]

 24%|██▍       | 423M/1.70G [00:05<00:26, 51.4MB/s]
 19%|█▉        | 415M/2.12G [00:07<03:02, 10.1MB/s]

 25%|██▍       | 429M/1.70G [00:08<02:56, 7.79MB/s]
 19%|█▉        | 420M/2.12G [00:08<03:17, 9.28MB/s]

 25%|██▌       | 439M/1.70G [00:08<01:52, 12.2MB/s]
 20%|█▉        | 432M/2.12G [00:08<01:56, 15.7MB/s]

 26%|██▌       | 447M/1.70G [00:08<01:22, 16.5MB/s]
 20%|██        | 441M/2.12G [00:08<01:25, 21.1MB/s]

 26%|██▌       | 454M/1.70G [00:08<01:05, 20.5MB/s]
 21%|██        | 448M/2.12G [00:09<01:14, 24.0MB/s]

 26%|██▋       | 460M/1.70G [00:08<00:54, 24.4MB/s]

 27%|██▋       | 468M/1.70G [00:09<00:42, 31.6MB/s]
 21%|██        | 454M/2.12G [00:09<01:07, 26.4MB/s]
 21%|██▏       | 464M/2.12G [00:09<00:49, 36.4MB/s]

 27%|██▋       | 476M/1.70G [00:09<00:38, 34.4MB/s]

 28%|██▊       | 487M/1.70G [00:09<00:28, 47.0MB/s]
 22%|██▏       | 471M/2.12G [00:09<00:53, 33.2MB/s]

 28%|██▊       | 495M/1.70G [00:09<00:28, 46.0MB/s]
 22%|██▏       | 477M/2.12G [00:09<00:49, 36.0MB/s]

 29%|██▉       | 502M/1.70G [00:09<00:27, 47.6MB/s]

 29%|██▉       | 510M/1.70G [00:09<00:23, 54.1MB/s]
 22%|██▏       | 483M/2.12G [00:09<00:52, 33.9MB/s]

 30%|██▉       | 519M/1.70G [00:09<00:20, 62.0MB/s]
 23%|██▎       | 491M/2.12G [00:10<00:41, 41.9MB/s]

 30%|███       | 527M/1.70G [00:09<00:19, 65.6MB/s]
 23%|██▎       | 497M/2.12G [00:10<00:47, 37.1MB/s]
 23%|██▎       | 506M/2.12G [00:10<00:36, 47.1MB/s]

 31%|███       | 535M/1.70G [00:10<00:26, 47.1MB/s]
 24%|██▎       | 513M/2.12G [00:10<00:35, 49.5MB/s]

 31%|███       | 541M/1.70G [00:10<00:25, 49.2MB/s]
 24%|██▍       | 523M/2.12G [00:10<00:28, 60.8MB/s]

 32%|███▏      | 551M/1.70G [00:10<00:20, 60.3MB/s]
 24%|██▍       | 530M/2.12G [00:10<00:30, 56.8MB/s]

 32%|███▏      | 561M/1.70G [00:10<00:18, 65.7MB/s]
 25%|██▍       | 537M/2.12G [00:10<00:29, 58.3MB/s]

 33%|███▎      | 569M/1.70G [00:10<00:18, 67.2MB/s]
 25%|██▌       | 547M/2.12G [00:10<00:24, 68.2MB/s]

 33%|███▎      | 578M/1.70G [00:10<00:16, 73.2MB/s]
 26%|██▌       | 557M/2.12G [00:11<00:21, 77.0MB/s]

 34%|███▎      | 586M/1.70G [00:10<00:17, 71.0MB/s]

 34%|███▍      | 595M/1.70G [00:11<00:15, 76.1MB/s]
 26%|██▌       | 565M/2.12G [00:11<00:24, 69.2MB/s]

 35%|███▍      | 609M/1.70G [00:11<00:13, 88.9MB/s]
 26%|██▋       | 573M/2.12G [00:11<00:26, 63.3MB/s]

 35%|███▌      | 618M/1.70G [00:11<00:13, 90.0MB/s]
 27%|██▋       | 583M/2.12G [00:11<00:23, 71.8MB/s]

 36%|███▌      | 630M/1.70G [00:11<00:11, 99.5MB/s]

 37%|███▋      | 640M/1.70G [00:12<00:37, 31.0MB/s]
 27%|██▋       | 591M/2.12G [00:12<01:09, 23.8MB/s]

 37%|███▋      | 650M/1.70G [00:12<00:29, 38.7MB/s]
 28%|██▊       | 602M/2.12G [00:12<00:49, 32.9MB/s]
 28%|██▊       | 611M/2.12G [00:12<00:40, 40.3MB/s]

 38%|███▊      | 660M/1.70G [00:12<00:24, 46.6MB/s]

 39%|███▊      | 673M/1.70G [00:12<00:18, 60.3MB/s]
 29%|██▊       | 619M/2.12G [00:12<00:37, 43.2MB/s]

 39%|███▉      | 684M/1.70G [00:12<00:15, 69.5MB/s]
 29%|██▉       | 626M/2.12G [00:12<00:35, 45.0MB/s]

 40%|████      | 697M/1.70G [00:12<00:13, 79.8MB/s]
 29%|██▉       | 638M/2.12G [00:13<00:27, 59.2MB/s]

 41%|████      | 707M/1.70G [00:12<00:12, 83.7MB/s]
 30%|██▉       | 646M/2.12G [00:13<00:25, 63.4MB/s]

 41%|████      | 717M/1.70G [00:13<00:12, 88.5MB/s]
 30%|███       | 654M/2.12G [00:13<00:23, 67.5MB/s]
 31%|███       | 662M/2.12G [00:13<00:22, 70.8MB/s]

 42%|████▏     | 727M/1.70G [00:13<00:15, 67.7MB/s]

 42%|████▏     | 736M/1.70G [00:13<00:15, 68.8MB/s]
 31%|███       | 670M/2.12G [00:13<00:26, 58.5MB/s]

 43%|████▎     | 744M/1.70G [00:13<00:15, 67.3MB/s]
 31%|███▏      | 677M/2.12G [00:13<00:29, 53.2MB/s]

 43%|████▎     | 755M/1.70G [00:13<00:13, 77.3MB/s]
 32%|███▏      | 683M/2.12G [00:13<00:28, 54.4MB/s]

 44%|████▍     | 765M/1.70G [00:13<00:12, 83.9MB/s]
 32%|███▏      | 690M/2.12G [00:13<00:26, 58.6MB/s]

 44%|████▍     | 774M/1.70G [00:13<00:13, 77.1MB/s]
 32%|███▏      | 703M/2.12G [00:14<00:19, 77.2MB/s]

 45%|████▌     | 786M/1.70G [00:14<00:11, 88.4MB/s]
 33%|███▎      | 712M/2.12G [00:14<00:20, 73.5MB/s]

 46%|████▌     | 797M/1.70G [00:14<00:10, 94.8MB/s]
 33%|███▎      | 722M/2.12G [00:14<00:19, 79.4MB/s]

 46%|████▋     | 807M/1.70G [00:14<00:10, 93.8MB/s]
 34%|███▍      | 731M/2.12G [00:14<00:18, 81.6MB/s]

 47%|████▋     | 817M/1.70G [00:14<00:10, 90.0MB/s]
 34%|███▍      | 740M/2.12G [00:14<00:17, 83.8MB/s]

 48%|████▊     | 829M/1.70G [00:14<00:09, 98.2MB/s]
 35%|███▍      | 749M/2.12G [00:14<00:17, 84.2MB/s]

 48%|████▊     | 839M/1.70G [00:14<00:09, 95.3MB/s]
 35%|███▌      | 759M/2.12G [00:14<00:16, 89.8MB/s]
 36%|███▌      | 769M/2.12G [00:14<00:15, 93.3MB/s]

 49%|████▉     | 849M/1.70G [00:14<00:09, 93.8MB/s]
 36%|███▌      | 780M/2.12G [00:14<00:14, 97.7MB/s]

 49%|████▉     | 859M/1.70G [00:14<00:10, 91.0MB/s]
 37%|███▋      | 793M/2.12G [00:15<00:13, 106MB/s] 

 50%|████▉     | 868M/1.70G [00:14<00:10, 89.9MB/s]
 37%|███▋      | 804M/2.12G [00:15<00:13, 107MB/s]

 50%|█████     | 877M/1.70G [00:15<00:10, 87.0MB/s]

 51%|█████     | 886M/1.70G [00:15<00:10, 85.0MB/s]
 38%|███▊      | 815M/2.12G [00:15<00:16, 84.8MB/s]

 51%|█████▏    | 895M/1.70G [00:15<00:12, 69.5MB/s]
 38%|███▊      | 824M/2.12G [00:15<00:18, 74.1MB/s]

 52%|█████▏    | 904M/1.70G [00:15<00:11, 73.7MB/s]
 38%|███▊      | 832M/2.12G [00:15<00:18, 75.1MB/s]

 52%|█████▏    | 912M/1.70G [00:15<00:11, 75.6MB/s]
 39%|███▉      | 843M/2.12G [00:15<00:16, 83.7MB/s]
 40%|███▉      | 856M/2.12G [00:15<00:14, 95.7MB/s]

 53%|█████▎    | 921M/1.70G [00:15<00:12, 71.1MB/s]
 40%|███▉      | 866M/2.12G [00:15<00:13, 97.5MB/s]

 53%|█████▎    | 931M/1.70G [00:15<00:10, 77.9MB/s]
 41%|████      | 878M/2.12G [00:16<00:12, 104MB/s] 

 54%|█████▍    | 939M/1.70G [00:16<00:11, 70.3MB/s]
 41%|████      | 889M/2.12G [00:16<00:12, 104MB/s]

 55%|█████▍    | 950M/1.70G [00:16<00:10, 80.9MB/s]

 56%|█████▌    | 967M/1.70G [00:16<00:07, 105MB/s] 
 42%|████▏     | 900M/2.12G [00:16<00:17, 73.9MB/s]

 56%|█████▌    | 978M/1.70G [00:16<00:07, 105MB/s]
 42%|████▏     | 909M/2.12G [00:16<00:17, 76.9MB/s]

 57%|█████▋    | 989M/1.70G [00:16<00:07, 103MB/s]
 43%|████▎     | 921M/2.12G [00:16<00:14, 87.9MB/s]

 57%|█████▋    | 0.98G/1.70G [00:16<00:07, 105MB/s]

 58%|█████▊    | 0.99G/1.70G [00:16<00:06, 110MB/s]
 43%|████▎     | 931M/2.12G [00:16<00:15, 81.4MB/s]
 43%|████▎     | 940M/2.12G [00:16<00:15, 82.6MB/s]

 59%|█████▊    | 1.00G/1.70G [00:16<00:09, 76.3MB/s]
 44%|████▍     | 949M/2.12G [00:17<00:18, 70.7MB/s]
 44%|████▍     | 957M/2.12G [00:17<00:18, 67.3MB/s]

 59%|█████▉    | 1.01G/1.70G [00:17<00:11, 64.5MB/s]
 45%|████▍     | 964M/2.12G [00:17<00:19, 65.9MB/s]

 60%|█████▉    | 1.02G/1.70G [00:17<00:11, 64.5MB/s]
 45%|████▍     | 971M/2.12G [00:17<00:19, 65.2MB/s]

 60%|██████    | 1.02G/1.70G [00:17<00:11, 63.6MB/s]
 45%|████▌     | 978M/2.12G [00:17<00:19, 63.6MB/s]

 61%|██████    | 1.03G/1.70G [00:17<00:11, 62.9MB/s]
 45%|████▌     | 985M/2.12G [00:17<00:19, 64.7MB/s]

 61%|██████    | 1.04G/1.70G [00:17<00:11, 62.8MB/s]
 46%|████▌     | 992M/2.12G [00:17<00:18, 65.3MB/s]

 61%|██████▏   | 1.04G/1.70G [00:17<00:10, 64.5MB/s]
 46%|████▌     | 0.98G/2.12G [00:17<00:17, 68.9MB/s]

 62%|██████▏   | 1.05G/1.70G [00:17<00:10, 66.5MB/s]
 46%|████▋     | 0.98G/2.12G [00:18<00:17, 69.8MB/s]

 62%|██████▏   | 1.06G/1.70G [00:17<00:10, 67.7MB/s]
 47%|████▋     | 0.99G/2.12G [00:18<00:16, 73.2MB/s]

 63%|██████▎   | 1.07G/1.70G [00:18<00:09, 69.0MB/s]
 47%|████▋     | 1.00G/2.12G [00:18<00:16, 72.2MB/s]

 63%|██████▎   | 1.07G/1.70G [00:18<00:09, 68.5MB/s]
 48%|████▊     | 1.01G/2.12G [00:18<00:16, 73.8MB/s]

 63%|██████▎   | 1.08G/1.70G [00:18<00:09, 69.1MB/s]
 48%|████▊     | 1.01G/2.12G [00:18<00:18, 63.5MB/s]

 64%|██████▍   | 1.09G/1.70G [00:18<00:10, 61.4MB/s]
 48%|████▊     | 1.02G/2.12G [00:18<00:20, 57.1MB/s]

 64%|██████▍   | 1.09G/1.70G [00:18<00:11, 55.9MB/s]
 49%|████▊     | 1.03G/2.12G [00:18<00:20, 57.9MB/s]

 65%|██████▍   | 1.10G/1.70G [00:18<00:11, 56.5MB/s]
 49%|████▉     | 1.04G/2.12G [00:18<00:18, 63.5MB/s]

 65%|██████▍   | 1.10G/1.70G [00:18<00:11, 57.4MB/s]
 49%|████▉     | 1.04G/2.12G [00:19<00:18, 63.4MB/s]

 65%|██████▌   | 1.11G/1.70G [00:18<00:10, 58.5MB/s]
 50%|████▉     | 1.05G/2.12G [00:19<00:17, 64.7MB/s]

 66%|██████▌   | 1.12G/1.70G [00:19<00:10, 61.8MB/s]
 50%|████▉     | 1.06G/2.12G [00:19<00:16, 67.6MB/s]

 66%|██████▌   | 1.12G/1.70G [00:19<00:09, 63.1MB/s]
 50%|█████     | 1.06G/2.12G [00:19<00:15, 70.6MB/s]

 67%|██████▋   | 1.13G/1.70G [00:19<00:08, 69.1MB/s]
 51%|█████     | 1.07G/2.12G [00:19<00:15, 72.6MB/s]

 67%|██████▋   | 1.14G/1.70G [00:19<00:08, 71.4MB/s]
 51%|█████     | 1.08G/2.12G [00:19<00:15, 73.6MB/s]

 68%|██████▊   | 1.15G/1.70G [00:19<00:07, 74.3MB/s]
 51%|█████▏    | 1.09G/2.12G [00:19<00:14, 76.0MB/s]

 68%|██████▊   | 1.16G/1.70G [00:19<00:07, 74.0MB/s]
 52%|█████▏    | 1.10G/2.12G [00:19<00:15, 69.1MB/s]

 69%|██████▊   | 1.17G/1.70G [00:19<00:08, 69.9MB/s]
 52%|█████▏    | 1.10G/2.12G [00:19<00:15, 70.8MB/s]

 69%|██████▉   | 1.17G/1.70G [00:19<00:08, 70.0MB/s]
 52%|█████▏    | 1.11G/2.12G [00:20<00:16, 66.4MB/s]

 69%|██████▉   | 1.18G/1.70G [00:19<00:09, 60.2MB/s]

 70%|██████▉   | 1.19G/1.70G [00:20<00:08, 62.5MB/s]
 53%|█████▎    | 1.12G/2.12G [00:20<00:19, 54.8MB/s]

 70%|███████   | 1.19G/1.70G [00:20<00:08, 63.8MB/s]
 53%|█████▎    | 1.12G/2.12G [00:20<00:18, 57.7MB/s]

 71%|███████   | 1.20G/1.70G [00:20<00:08, 64.8MB/s]
 53%|█████▎    | 1.13G/2.12G [00:20<00:17, 61.3MB/s]

 71%|███████   | 1.21G/1.70G [00:20<00:07, 68.3MB/s]
 54%|█████▍    | 1.14G/2.12G [00:20<00:16, 65.0MB/s]

 71%|███████▏  | 1.21G/1.70G [00:20<00:07, 66.1MB/s]
 54%|█████▍    | 1.15G/2.12G [00:20<00:16, 64.4MB/s]

 72%|███████▏  | 1.22G/1.70G [00:20<00:07, 66.4MB/s]
 54%|█████▍    | 1.15G/2.12G [00:20<00:16, 63.0MB/s]

 72%|███████▏  | 1.23G/1.70G [00:20<00:08, 61.2MB/s]
 55%|█████▍    | 1.16G/2.12G [00:20<00:16, 61.6MB/s]

 73%|███████▎  | 1.23G/1.70G [00:20<00:08, 58.0MB/s]
 55%|█████▌    | 1.17G/2.12G [00:21<00:16, 61.0MB/s]
 55%|█████▌    | 1.17G/2.12G [00:22<01:11, 14.1MB/s]

 73%|███████▎  | 1.24G/1.70G [00:22<00:37, 13.4MB/s]
 56%|█████▌    | 1.18G/2.12G [00:22<00:51, 19.6MB/s]

 73%|███████▎  | 1.25G/1.70G [00:22<00:23, 20.3MB/s]
 56%|█████▌    | 1.19G/2.12G [00:22<00:42, 23.4MB/s]

 74%|███████▍  | 1.25G/1.70G [00:22<00:19, 24.2MB/s]

 74%|███████▍  | 1.26G/1.70G [00:22<00:13, 34.0MB/s]

 75%|███████▌  | 1.28G/1.70G [00:22<00:08, 50.6MB/s]

 76%|███████▌  | 1.29G/1.70G [00:22<00:07, 59.8MB/s]
 57%|█████▋    | 1.20G/2.12G [00:23<00:35, 27.6MB/s]
 57%|█████▋    | 1.21G/2.12G [00:23<00:26, 36.3MB/s]

 76%|███████▋  | 1.30G/1.70G [00:23<00:07, 57.6MB/s]

 77%|███████▋  | 1.31G/1.70G [00:23<00:06, 62.4MB/s]
 57%|█████▋    | 1.21G/2.12G [00:23<00:27, 35.1MB/s]
 58%|█████▊    | 1.22G/2.12G [00:23<00:24, 38.9MB/s]

 77%|███████▋  | 1.31G/1.70G [00:23<00:07, 53.2MB/s]
 58%|█████▊    | 1.23G/2.12G [00:23<00:22, 43.0MB/s]

 78%|███████▊  | 1.32G/1.70G [00:23<00:06, 61.9MB/s]

 78%|███████▊  | 1.33G/1.70G [00:23<00:05, 68.7MB/s]
 58%|█████▊    | 1.24G/2.12G [00:23<00:20, 46.8MB/s]

 79%|███████▉  | 1.34G/1.70G [00:23<00:05, 72.2MB/s]
 59%|█████▉    | 1.24G/2.12G [00:23<00:20, 46.0MB/s]

 80%|███████▉  | 1.35G/1.70G [00:23<00:04, 77.2MB/s]

 80%|███████▉  | 1.36G/1.70G [00:23<00:04, 74.6MB/s]
 59%|█████▉    | 1.25G/2.12G [00:24<00:19, 47.6MB/s]

 81%|████████  | 1.37G/1.70G [00:24<00:04, 79.9MB/s]
 60%|█████▉    | 1.26G/2.12G [00:24<00:16, 56.2MB/s]

 81%|████████  | 1.38G/1.70G [00:24<00:03, 88.6MB/s]
 60%|█████▉    | 1.27G/2.12G [00:24<00:18, 48.6MB/s]

 82%|████████▏ | 1.39G/1.70G [00:24<00:03, 87.9MB/s]
 60%|██████    | 1.27G/2.12G [00:24<00:16, 55.1MB/s]

 82%|████████▏ | 1.40G/1.70G [00:24<00:03, 81.4MB/s]
 61%|██████    | 1.28G/2.12G [00:24<00:14, 62.4MB/s]

 83%|████████▎ | 1.41G/1.70G [00:24<00:03, 82.2MB/s]
 61%|██████    | 1.29G/2.12G [00:24<00:14, 62.8MB/s]

 83%|████████▎ | 1.42G/1.70G [00:24<00:03, 85.9MB/s]
 61%|██████▏   | 1.30G/2.12G [00:24<00:13, 64.0MB/s]

 84%|████████▍ | 1.43G/1.70G [00:24<00:02, 99.6MB/s]

 85%|████████▍ | 1.45G/1.70G [00:24<00:02, 109MB/s] 
 62%|██████▏   | 1.31G/2.12G [00:25<00:15, 57.2MB/s]

 86%|████████▌ | 1.46G/1.70G [00:25<00:02, 110MB/s]
 62%|██████▏   | 1.32G/2.12G [00:25<00:12, 69.9MB/s]

 86%|████████▌ | 1.47G/1.70G [00:25<00:02, 110MB/s]
 63%|██████▎   | 1.33G/2.12G [00:25<00:11, 74.1MB/s]

 87%|████████▋ | 1.48G/1.70G [00:25<00:02, 102MB/s]
 63%|██████▎   | 1.34G/2.12G [00:25<00:10, 82.6MB/s]

 87%|████████▋ | 1.49G/1.70G [00:25<00:02, 96.1MB/s]
 64%|██████▎   | 1.34G/2.12G [00:25<00:09, 85.4MB/s]

 88%|████████▊ | 1.50G/1.70G [00:25<00:02, 106MB/s] 
 64%|██████▍   | 1.35G/2.12G [00:25<00:11, 71.7MB/s]

 89%|████████▉ | 1.52G/1.70G [00:25<00:01, 119MB/s]
 65%|██████▍   | 1.37G/2.12G [00:25<00:09, 84.5MB/s]

 90%|████████▉ | 1.53G/1.70G [00:25<00:01, 117MB/s]
 65%|██████▍   | 1.37G/2.12G [00:25<00:11, 69.7MB/s]

 91%|█████████ | 1.54G/1.70G [00:25<00:01, 99.4MB/s]
 65%|██████▌   | 1.38G/2.12G [00:26<00:10, 74.7MB/s]

 91%|█████████ | 1.55G/1.70G [00:25<00:01, 99.8MB/s]
 66%|██████▌   | 1.39G/2.12G [00:26<00:10, 74.7MB/s]

 92%|█████████▏| 1.56G/1.70G [00:26<00:01, 102MB/s] 
 66%|██████▋   | 1.40G/2.12G [00:26<00:09, 83.9MB/s]

 93%|█████████▎| 1.57G/1.70G [00:26<00:01, 108MB/s]
 67%|██████▋   | 1.41G/2.12G [00:26<00:08, 91.4MB/s]

 93%|█████████▎| 1.58G/1.70G [00:26<00:01, 100MB/s]
 67%|██████▋   | 1.42G/2.12G [00:26<00:08, 90.9MB/s]

 94%|█████████▎| 1.59G/1.70G [00:26<00:01, 87.1MB/s]
 68%|██████▊   | 1.43G/2.12G [00:26<00:08, 86.6MB/s]

 94%|█████████▍| 1.60G/1.70G [00:26<00:01, 82.8MB/s]
 68%|██████▊   | 1.44G/2.12G [00:26<00:10, 70.8MB/s]

 95%|█████████▍| 1.61G/1.70G [00:26<00:01, 78.2MB/s]
 68%|██████▊   | 1.45G/2.12G [00:26<00:09, 73.6MB/s]

 95%|█████████▌| 1.62G/1.70G [00:26<00:01, 83.3MB/s]
 69%|██████▉   | 1.46G/2.12G [00:27<00:08, 80.8MB/s]

 96%|█████████▌| 1.63G/1.70G [00:26<00:00, 85.3MB/s]
 69%|██████▉   | 1.47G/2.12G [00:27<00:08, 81.4MB/s]

 96%|█████████▋| 1.64G/1.70G [00:27<00:00, 87.5MB/s]
 70%|██████▉   | 1.48G/2.12G [00:27<00:07, 86.9MB/s]

 97%|█████████▋| 1.65G/1.70G [00:27<00:00, 70.2MB/s]
 70%|███████   | 1.48G/2.12G [00:27<00:10, 67.4MB/s]

 97%|█████████▋| 1.66G/1.70G [00:27<00:00, 75.5MB/s]
 71%|███████   | 1.49G/2.12G [00:27<00:09, 71.1MB/s]

 98%|█████████▊| 1.67G/1.70G [00:27<00:00, 82.3MB/s]
 71%|███████   | 1.50G/2.12G [00:27<00:08, 74.9MB/s]

 99%|█████████▊| 1.68G/1.70G [00:27<00:00, 90.1MB/s]
 71%|███████▏  | 1.51G/2.12G [00:27<00:08, 75.7MB/s]

 99%|█████████▉| 1.69G/1.70G [00:27<00:00, 89.7MB/s]
 72%|███████▏  | 1.52G/2.12G [00:27<00:08, 79.7MB/s]

100%|██████████| 1.70G/1.70G [00:27<00:00, 65.5MB/s]

 72%|███████▏  | 1.53G/2.12G [00:28<00:08, 76.6MB/s]
 73%|███████▎  | 1.54G/2.12G [00:28<00:06, 98.0MB/s]
 74%|███████▎  | 1.56G/2.12G [00:28<00:05, 112MB/s] 
 74%|███████▍  | 1.57G/2.12G [00:28<00:05, 105MB/s]
 75%|███████▍  | 1.58G/2.12G [00:28<00:05, 107MB/s]
 75%|███████▌  | 1.59G/2.12G [00:28<00:06, 89.3MB/s]
 76%|███████▌  | 1.61G/2.12G [00:28<00:05, 101MB/s] 
 77%|███████▋  | 1.62G/2.12G [00:28<00:04, 112MB/s]
 77%|███████▋  | 1.63G/2.12G [00:29<00:05, 92.3MB/s]
 78%|███████▊  | 1.64G/2.12G [00:29<00:06, 78.4MB/s]
 78%|███████▊  | 1.66G/2.12G [00:29<00:05, 95.5MB/s]
 79%|███████▉  | 1.67G/2.12G [00:29<00:04, 101MB/s] 
 80%|███████▉  | 1.69G/2.12G [00:29<00:04, 114MB/s]
 80%|████████  | 1.70G/2.12G [00:29<00:03, 124MB/s]
 81%|████████  | 1.71G/2.12G [00:29<00:03, 122MB/s]
 82%|████████▏ | 1.73G/2.12G [00:30<00:04, 95.5MB/s]
 82%|████████▏ | 1.74G/2.12G [00:30<00:03, 104MB/s] 
 83%|████████▎ | 1.75G/2.12G [00:30<00:03, 103MB/s]
 83%|████████▎ | 1.76G/2.12G [00:30<00:04, 76.8MB/s]
 84%|████████▍ | 1.78G/2.12G [00:30<00:03, 96.8MB/s]
 85%|████████▍ | 1.79G/2.12G [00:30<00:03, 102MB/s] 
 85%|████████▌ | 1.80G/2.12G [00:31<00:04, 69.5MB/s]
 86%|████████▌ | 1.82G/2.12G [00:31<00:03, 92.2MB/s]
 87%|████████▋ | 1.83G/2.12G [00:31<00:03, 91.5MB/s]
 87%|████████▋ | 1.84G/2.12G [00:31<00:02, 98.1MB/s]
 88%|████████▊ | 1.85G/2.12G [00:31<00:03, 85.1MB/s]
 88%|████████▊ | 1.86G/2.12G [00:31<00:03, 88.0MB/s]
 89%|████████▊ | 1.87G/2.12G [00:31<00:02, 88.9MB/s]
 89%|████████▉ | 1.88G/2.12G [00:32<00:02, 93.5MB/s]
 90%|████████▉ | 1.90G/2.12G [00:32<00:02, 106MB/s] 
 90%|█████████ | 1.91G/2.12G [00:32<00:01, 111MB/s]
 91%|█████████ | 1.92G/2.12G [00:32<00:02, 98.7MB/s]
 91%|█████████▏| 1.93G/2.12G [00:32<00:01, 107MB/s] 
 92%|█████████▏| 1.95G/2.12G [00:32<00:01, 104MB/s]
 93%|█████████▎| 1.96G/2.12G [00:32<00:01, 117MB/s]
 93%|█████████▎| 1.97G/2.12G [00:32<00:01, 106MB/s]
 94%|█████████▍| 1.98G/2.12G [00:33<00:01, 92.6MB/s]
 94%|█████████▍| 1.99G/2.12G [00:33<00:01, 86.4MB/s]
 95%|█████████▍| 2.00G/2.12G [00:33<00:01, 68.3MB/s]
 95%|█████████▌| 2.02G/2.12G [00:33<00:01, 84.0MB/s]
 96%|█████████▌| 2.03G/2.12G [00:33<00:01, 91.6MB/s]
 96%|█████████▋| 2.04G/2.12G [00:33<00:00, 96.2MB/s]
 97%|█████████▋| 2.05G/2.12G [00:33<00:00, 108MB/s] 
 98%|█████████▊| 2.06G/2.12G [00:33<00:00, 89.9MB/s]
 98%|█████████▊| 2.08G/2.12G [00:34<00:00, 103MB/s] 
 99%|█████████▉| 2.09G/2.12G [00:34<00:00, 115MB/s]
100%|██████████| 2.12G/2.12G [00:34<00:00, 66.0MB/s]

print('GEMMA_PATH:', GEMMA_PATH)

GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1
  1. Periksa lokasi bobot model dan tokenizer, lalu tetapkan variabel jalur. Direktori tokenizer akan berada di direktori utama tempat Anda mendownload model, sedangkan bobot model akan berada di sub-direktori. Contoh:
  • File tokenizer.model akan berada di /LOCAL/PATH/TO/gemma/flax/2b-it/2).
  • Checkpoint model akan berada di /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it).
CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)
TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')
print('CKPT_PATH:', CKPT_PATH)
print('TOKENIZER_PATH:', TOKENIZER_PATH)

CKPT_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/gemma2-2b-it
TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/tokenizer.model

Melakukan sampling/inferensi

  1. Muat dan format checkpoint model Gemma dengan metode gemma.params.load_and_format_params:
from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)
  1. Muat tokenizer Gemma, yang dibuat menggunakan sentencepiece.SentencePieceProcessor:
import sentencepiece as spm

vocab = spm.SentencePieceProcessor()
vocab.Load(TOKENIZER_PATH)

True
  1. Untuk otomatis memuat konfigurasi yang benar dari checkpoint model Gemma, gunakan gemma.transformer.TransformerConfig. Argumen cache_size adalah jumlah langkah waktu dalam cache Transformer Gemma. Setelah itu, buat instance model Gemma sebagai transformer dengan gemma.transformer.Transformer (yang diturunkan dari flax.linen.Module).
from gemma import transformer as transformer_lib

transformer_config = transformer_lib.TransformerConfig.from_params(
    params=params,
    cache_size=1024
)

transformer = transformer_lib.Transformer(transformer_config)
  1. Buat sampler dengan gemma.sampler.Sampler di atas checkpoint/bobot model Gemma dan tokenizer:
from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)
  1. Tulis perintah di input_batch dan lakukan inferensi. Anda dapat menyesuaikan total_generation_steps (jumlah langkah yang dilakukan saat membuat respons — contoh ini menggunakan 100 untuk menghemat memori host).
prompt = [
    "what is JAX in 3 bullet points?",
]

reply = sampler(input_strings=prompt,
                total_generation_steps=128,
                )

for input_string, out_string in zip(prompt, reply.text):
    print(f"Prompt:\n{input_string}\nOutput:\n{out_string}")

Prompt:
what is JAX in 3 bullet points?
Output:


* **High-performance numerical computation:** JAX leverages the power of GPUs and TPUs to accelerate complex mathematical operations, making it ideal for scientific computing, machine learning, and data analysis.
* **Automatic differentiation:** JAX provides automatic differentiation capabilities, allowing you to compute gradients and optimize models efficiently. This simplifies the process of training deep learning models.
* **Functional programming:** JAX embraces functional programming principles, promoting code readability and maintainability. It offers a flexible and expressive syntax for defining and manipulating data. 


<end_of_turn>
  1. (Opsional) Jalankan sel ini untuk mengosongkan memori jika Anda telah menyelesaikan notebook dan ingin mencoba perintah lain. Setelah itu, Anda dapat membuat instance sampler lagi di langkah 3 serta menyesuaikan dan menjalankan perintah di langkah 4.
del sampler

Pelajari lebih lanjut