Step 1: Create an API key for free by logging into Google AI Studio
A:
B:
C:
D:
E:
F: Your free API key is created. Copy it and save it somewhere.
Trying a couple of things...
import google.generativeai as genai
API_KEY = 'A...o'
genai.configure(api_key=API_KEY)
model = genai.GenerativeModel()
response = model.generate_content('Teach me about how an LLM works')
print(response.text)
**Understanding Large Language Models (LLMs)**
**Introduction:**
LLMs are advanced machine learning models trained on vast amounts of text data. They can generate human-like text, translate languages, write different types of content, and perform various other text-related tasks.
**Working Principle:**
LLMs are trained on billions of words of text using a technique called transformer neural networks. These networks process sequential data and learn the relationships between words and phrases within the text.
**Training Process:**
1. **Massive Data Collection:** LLMs are trained on enormous datasets of text from various sources, such as books, articles, news, and social media posts.
2. **Tokenization:** The text is broken down into individual words and phrases called "tokens."
3. **Encoding:** Tokens are converted into numerical representations using embedding techniques.
4. **Transformer Architecture:** The transformer neural network processes the encoded tokens, identifying patterns and learning relationships within the text.
5. **Parameter Optimization:** The model adjusts its internal parameters (weights and biases) to minimize a loss function, which measures how well it predicts the next words in the text.
6. **Fine-tuning:** After general training, the LLM can be fine-tuned for specific tasks, such as language translation or content generation.
**Capabilities:**
* **Text Generation:** LLMs can create original, fluent, and grammatically correct text in response to a given prompt.
* **Language Translation:** They can translate text from one language to another with high accuracy.
* **Question Answering:** LLMs can extract information from text and answer questions related to the content.
* **Summarization:** They can condense large amounts of text into concise and informative summaries.
* **Content Creation:** LLMs can generate various types of content, including articles, stories, poems, and website copy.
**Applications:**
* **Natural Language Processing (NLP):** Sentiment analysis, text classification, chatbots
* **Content Generation:** Creative writing, marketing materials, news articles
* **Education:** Language learning, essay writing assistance
* **Research:** Literature analysis, data extraction
* **Customer Service:** Automated response systems, chat support
**Limitations:**
* **Bias:** LLMs can exhibit biases present in their training data.
* **Factual Errors:** They are not immune to factual errors in the sources they are trained on.
* **Limited Contextual Understanding:** They may struggle to understand the full context of complex text.
* **Ethics:** The use of LLMs raises ethical concerns about authorship, misinformation, and deepfakes.
Listing all models
import pprint
for model in genai.list_models():
pprint.pprint(model)
Model(name='models/chat-bison-001',
base_model_id='',
version='001',
display_name='PaLM 2 Chat (Legacy)',
description='A legacy text-only model optimized for chat conversations',
input_token_limit=4096,
output_token_limit=1024,
supported_generation_methods=['generateMessage', 'countMessageTokens'],
temperature=0.25,
top_p=0.95,
top_k=40)
Model(name='models/text-bison-001',
base_model_id='',
version='001',
display_name='PaLM 2 (Legacy)',
description='A legacy model that understands text and generates text as an output',
input_token_limit=8196,
output_token_limit=1024,
supported_generation_methods=['generateText', 'countTextTokens', 'createTunedTextModel'],
temperature=0.7,
top_p=0.95,
top_k=40)
Model(name='models/embedding-gecko-001',
base_model_id='',
version='001',
display_name='Embedding Gecko',
description='Obtain a distributed representation of a text.',
input_token_limit=1024,
output_token_limit=1,
supported_generation_methods=['embedText', 'countTextTokens'],
temperature=None,
top_p=None,
top_k=None)
Model(name='models/gemini-1.0-pro',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro',
description='The best model for scaling across a wide range of tasks',
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-1.0-pro-001',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro 001 (Tuning)',
description=('The best model for scaling across a wide range of tasks. This is a stable '
'model that supports tuning.'),
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens', 'createTunedModel'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-1.0-pro-latest',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Latest',
description=('The best model for scaling across a wide range of tasks. This is the latest '
'model.'),
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-1.0-pro-vision-latest',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Vision',
description='The best image understanding model to handle a broad range of applications',
input_token_limit=12288,
output_token_limit=4096,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.4,
top_p=1.0,
top_k=32)
Model(name='models/gemini-1.5-flash',
base_model_id='',
version='001',
display_name='Gemini 1.5 Flash',
description='Fast and versatile multimodal model for scaling across diverse tasks',
input_token_limit=1048576,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-flash-001',
base_model_id='',
version='001',
display_name='Gemini 1.5 Flash 001',
description='Fast and versatile multimodal model for scaling across diverse tasks',
input_token_limit=1048576,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens', 'createCachedContent'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-flash-latest',
base_model_id='',
version='001',
display_name='Gemini 1.5 Flash Latest',
description='Fast and versatile multimodal model for scaling across diverse tasks',
input_token_limit=1048576,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-pro',
base_model_id='',
version='001',
display_name='Gemini 1.5 Pro',
description='Mid-size multimodal model that supports up to 1 million tokens',
input_token_limit=2097152,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-pro-001',
base_model_id='',
version='001',
display_name='Gemini 1.5 Pro 001',
description='Mid-size multimodal model that supports up to 1 million tokens',
input_token_limit=2097152,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens', 'createCachedContent'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-pro-latest',
base_model_id='',
version='001',
display_name='Gemini 1.5 Pro Latest',
description='Mid-size multimodal model that supports up to 1 million tokens',
input_token_limit=2097152,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-pro',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro',
description='The best model for scaling across a wide range of tasks',
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-pro-vision',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Vision',
description='The best image understanding model to handle a broad range of applications',
input_token_limit=12288,
output_token_limit=4096,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.4,
top_p=1.0,
top_k=32)
Model(name='models/embedding-001',
base_model_id='',
version='001',
display_name='Embedding 001',
description='Obtain a distributed representation of a text.',
input_token_limit=2048,
output_token_limit=1,
supported_generation_methods=['embedContent'],
temperature=None,
top_p=None,
top_k=None)
Model(name='models/text-embedding-004',
base_model_id='',
version='004',
display_name='Text Embedding 004',
description='Obtain a distributed representation of a text.',
input_token_limit=2048,
output_token_limit=1,
supported_generation_methods=['embedContent'],
temperature=None,
top_p=None,
top_k=None)
Model(name='models/aqa',
base_model_id='',
version='001',
display_name='Model that performs Attributed Question Answering.',
description=('Model trained to return answers to questions that are grounded in provided '
'sources, along with estimating answerable probability.'),
input_token_limit=7168,
output_token_limit=1024,
supported_generation_methods=['generateAnswer'],
temperature=0.2,
top_p=1.0,
top_k=40)
No comments:
Post a Comment