जैक्स और फ़्लैक्स का इस्तेमाल करके जेम्मा के साथ अनुमान लगाना

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खास जानकारी

Gemma एक लाइटवेट और बेहतरीन ओपन लार्ज लैंग्वेज मॉडल है. यह Google DeepMind Gemini की रिसर्च और टेक्नोलॉजी पर आधारित है. इस ट्यूटोरियल में Google DeepMind की gemma लाइब्रेरी, Flax (JAX-आधारित न्यूरल नेटवर्क लाइब्रेरी), Orbax (JAX-आधारित लाइब्रेरी जैसे चेकपॉइंटिंग (JAX) पर आधारित लाइब्रेरी) की मदद से लिखा गया है. Google DeepMind की gemma लाइब्रेरी का इस्तेमाल करके, बेसिक सैंपलिंग/इन्ट्रक्शन मॉडल की मदद से बुनियादी सैंपलिंग/अनुमान लगाने का तरीका बताया गया है. जैसे, चेकपॉइंटिंग (चेकपॉइंटिंग की सुविधा) के लिए{/1JAXSentencePiece इस नोटबुक में सीधे तौर पर Flax का इस्तेमाल नहीं किया जाता है, लेकिन Gemma को बनाने के लिए Flax का इस्तेमाल किया गया था.

इस notebook को Google Colab पर बिना किसी शुल्क के T4 जीपीयू के साथ चलाया जा सकता है (बदलाव करें > नोटबुक की सेटिंग पर जाएं > हार्डवेयर ऐक्सेलरेटर में जाकर, T4 जीपीयू चुनें.

सेटअप

1. Gemma के लिए Kaggle का ऐक्सेस सेट अप करें

इस ट्यूटोरियल को पूरा करने के लिए, आपको सबसे पहले Gemma सेटअप में दिए गए सेटअप के निर्देशों का पालन करना होगा. इसमें बताया गया है कि ये काम कैसे किए जा सकते हैं:

• kaggle.com पर Gemma का ऐक्सेस पाएं.
• Gemma मॉडल चलाने के लिए, ज़रूरी संसाधनों वाला Colab रनटाइम चुनें.
• Kaggle उपयोगकर्ता नाम और एपीआई पासकोड को जनरेट और कॉन्फ़िगर करें.

Gemma का सेटअप पूरा करने के बाद, अगले सेक्शन पर जाएं. यहां अपने Colab एनवायरमेंट के लिए, एनवायरमेंट वैरिएबल सेट किए जा सकते हैं.

2. एनवायरमेंट वैरिएबल सेट करना

KAGGLE_USERNAME और KAGGLE_KEY के लिए, एनवायरमेंट वैरिएबल सेट करें. जब "ऐक्सेस दें?" के साथ प्रॉम्प्ट किया जाए मैसेज, सीक्रेट ऐक्सेस देने के लिए सहमत हों.

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. gemma लाइब्रेरी इंस्टॉल करें

इस notebook में, बिना किसी शुल्क के Colab जीपीयू के इस्तेमाल पर फ़ोकस किया गया है. हार्डवेयर की मदद से तेज़ी लाने के लिए, बदलाव करें > पर क्लिक करें नोटबुक की सेटिंग > T4 जीपीयू चुनें > सेव करें पर टैप करें.

इसके बाद, आपको github.com/google-deepmind/gemma से Google DeepMind gemma लाइब्रेरी इंस्टॉल करनी होगी. अगर आपको "पीआईपी की डिपेंडेंसी रिज़ॉल्वर" से जुड़ी कोई गड़बड़ी मिलती है, तो आम तौर पर उसे अनदेखा किया जा सकता है.

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

Gemma मॉडल लोड करें और तैयार करें

1. Gemma मॉडल को kagglehub.model_download से लोड करें. इसमें तीन आर्ग्युमेंट इस्तेमाल किए जाते हैं:

• handle: Kaggle का मॉडल हैंडल
• path: (वैकल्पिक स्ट्रिंग) लोकल पाथ
• force_download: (वैकल्पिक बूलियन) मॉडल को फिर से डाउनलोड करने के लिए मजबूर करता है

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}')

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print('GEMMA_PATH:', GEMMA_PATH)

GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1

1. मॉडल वेट और टोकनाइज़र की जगह की जांच करें. इसके बाद, पाथ वैरिएबल सेट करें. टोकनाइज़र डायरेक्ट्री, उस मुख्य डायरेक्ट्री में होगी जिसमें आपने मॉडल डाउनलोड किया है. वहीं, मॉडल वेट किसी सब-डायरेक्ट्री में होगा. उदाहरण के लिए:

• tokenizer.model फ़ाइल /LOCAL/PATH/TO/gemma/flax/2b-it/2 में होगी).
• मॉडल चेकपॉइंट /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it में होगा).

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

सैंपलिंग/अनुमान लगाना

1. gemma.params.load_and_format_params तरीके का इस्तेमाल करके, Gemma मॉडल चेकपॉइंट लोड और फ़ॉर्मैट करें:

from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)

1. sentencepiece.SentencePieceProcessor का इस्तेमाल करके बनाया गया Gemma टोकनाइज़र लोड करें:

import sentencepiece as spm

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

True

1. Gemma मॉडल चेकपॉइंट से सही कॉन्फ़िगरेशन को अपने-आप लोड करने के लिए, gemma.transformer.TransformerConfig का इस्तेमाल करें. cache_size आर्ग्युमेंट, Gemma Transformer कैश में सेव किए गए समय से जुड़े चरणों की संख्या बताता है. इसके बाद, जेमा मॉडल को transformer के तौर पर gemma.transformer.Transformer (जो flax.linen.Module से इनहेरिट होता है) के रूप में इंस्टैंशिएट करें.

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. Gemma मॉडल चेकपॉइंट/वेट और टोकनाइज़र के ऊपर, gemma.sampler.Sampler वाला sampler बनाएं:

from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)

1. input_batch में प्रॉम्प्ट लिखें और अनुमान लगाएं. total_generation_steps में बदलाव किया जा सकता है. रिस्पॉन्स जनरेट करने के दौरान किए गए चरणों की संख्या में बदलाव किया जा सकता है. इस उदाहरण में, होस्ट की मेमोरी को बनाए रखने के लिए 100 का इस्तेमाल किया गया है.

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. (ज़रूरी नहीं) अगर आपने notebook पूरी कर ली है और किसी दूसरे प्रॉम्प्ट को आज़माना है, तो मेमोरी खाली करने के लिए इस सेल को चलाएं. इसके बाद, तीसरे चरण में sampler को फिर से इंस्टैंशिएट किया जा सकता है. साथ ही, चौथे चरण में प्रॉम्प्ट को पसंद के मुताबिक बनाया जा सकता है और उसे चलाया जा सकता है.

del sampler

ज़्यादा जानें

• GitHub पर Google DeepMind gemma की लाइब्रेरी के बारे में ज़्यादा जानें, जिसमें इस ट्यूटोरियल में इस्तेमाल किए गए मॉड्यूल की docstrings शामिल हैं, जैसे कि gemma.params, gemma.transformer और gemma.sampler.
• इन लाइब्रेरी की अपनी डॉक्यूमेंटेशन साइटें हैं: कोर JAX, Flax, और Orbax.
• sentencepiece टोकनाइज़र/डिटोकेनाइज़र के दस्तावेज़ के लिए, Google का sentencepiece GitHub रेपो देखें.
• kagglehub के दस्तावेज़ों के लिए, Kaggle के kagglehub GitHub रेपो पर README.md देखें.
• Google Cloud Vertex AI की मदद से, Gemma के मॉडल इस्तेमाल करने का तरीका जानें.