2024-07-12
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Gemma 2Building on its predecessor, it offers enhanced performance and efficiency, as well as a host of innovative features that make it particularly attractive for both research and practical applications. What sets Gemma 2 apart is that it is able to deliver performance comparable to larger proprietary models, but with aSoftware PackagesDesigned for wider accessibility and use on more modest hardware setups.
As I delved deeper into the technical specifications and architecture of Gemma 2, I became more and more impressed by the ingenuity of its design. The model uses a variety of advanced technologies, including novelAttention Mechanismand innovative training stability methods, which all contribute to its superior performance.
In this comprehensive guide, you’ll explore Gemma 2 in depth, examining its architecture, key features, and real-world applications. Whether you’re an experienced AI practitioner or a passionate newcomer to the field, this article aims to provide valuable insights into how Gemma 2 works and how you can leverage its capabilities in your own projects.
Gemma 2 is Google's latest open sourceLanguage Model, designed to be compact yet powerful. It is built on the same research and technology used to create the Google Gemini model, delivering state-of-the-art performance in a more accessible package. Gemma 2 is available in two sizes:
Gemma 2 9B: A 9 billion parameter model
Gemma 2 27B: A larger 27 billion parameter model
Each size is available in two styles:
Basic Model: Pre-training on large amounts of text data
Instruction Adjustment (IT) Model: Fine-tuned to achieve better performance on specific tasks
Accessing models in Google AI Studio：Google AI Studio – Gemma 2
Read the paper here: Gemma 2 Technical Report
Gemma 2 introduces several significant improvements over its predecessor:
These models have been trained with more data:
Gemma 2 27B: After training with 13 trillion tokens
Gemma 2 9B: After training with 8 trillion tokens
This expanded dataset consists mainly of web data (mostly in English), code, and mathematics, which helps improve the performance and versatility of the model.
Gemma 2 implements a novel approach to attention mechanisms:
A sliding window attention mechanism is used every other layer, and the local context is 4096 tokens.
The alternating layer uses a fully quadratic global attention mechanism for the entire 8192 token context.
This hybrid approach aims to balance efficiency and the ability to capture long-range dependencies in the input.
In order to improve training stability and performance, Gemma 2 introduces a soft cap mechanism:
def soft_cap(x, cap):
return cap * torch.tanh(x / cap)
# Applied to attention logits
attention_logits = soft_cap(attention_logits, cap=50.0)
# Applied to final layer logits
final_logits = soft_cap(final_logits, cap=30.0)
This technique prevents the logits from being too large without hard truncation, thus stabilizing the training process while retaining more information.
Each size is available in two styles:
For the 9B model, Gemma 2 uses knowledge extraction technology:
This process helps the smaller model capture the functionality of the larger model more effectively.
Gemma 2 uses a new model merging technology called Warp, which combines multiple models in three stages:
This approach aims to create a more robust and powerful final model.
Gemma 2 shows impressive performance in various benchmarks:
Gemma 2 features a redesigned architecture designed for superior performance and inference efficiency
To start using Gemma 2 in your project, you have several options:
Gemma 2 can be accessed through Google AI Studio.Google AI Studio.
Gemma 2 and Hugging Face Transformers library integration. Here is how to use it:
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from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
model_name = "google/gemma-2-27b-it" # or "google/gemma-2-9b-it" for the smaller version
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Prepare input
prompt = "Explain the concept of quantum entanglement in simple terms."
inputs = tokenizer(prompt, return_tensors="pt")
# Generate text
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
For TensorFlow users, Gemma 2 is available through Keras:
import tensorflow as tf
from keras_nlp.models import GemmaCausalLM
# Load the model
model = GemmaCausalLM.from_preset("gemma_2b_en")
# Generate text
prompt = "Explain the concept of quantum entanglement in simple terms."
output = model.generate(prompt, max_length=200)
print(output)
A powerful application of Gemma 2 is building Retrieval-Augmented Generation (RAG) systems. Let’s create a simple, fully native RAG system using Gemma 2 and Nomic embeddings.
First, make sure you have the necessary libraries installed:
pip install langchain ollama nomic chromadb
Create an indexer to process the documents:
import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import DirectoryLoader
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
class Indexer:
def __init__(self, directory_path):
self.directory_path = directory_path
self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
self.embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1")
def load_and_split_documents(self):
loader = DirectoryLoader(self.directory_path, glob="**/*.txt")
documents = loader.load()
return self.text_splitter.split_documents(documents)
def create_vector_store(self, documents):
return Chroma.from_documents(documents, self.embeddings, persist_directory="./chroma_db")
def index(self):
documents = self.load_and_split_documents()
vector_store = self.create_vector_store(documents)
vector_store.persist()
return vector_store
# Usage
indexer = Indexer("path/to/your/documents")
vector_store = indexer.index()
Now, create the RAG system using Gemma 2:
from langchain.llms import Ollama
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
class RAGSystem:
def __init__(self, vector_store):
self.vector_store = vector_store
self.llm = Ollama(model="gemma2:9b")
self.retriever = self.vector_store.as_retriever(search_kwargs={"k": 3})
self.template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Answer: """
self.qa_prompt = PromptTemplate(
template=self.template, input_variables=["context", "question"]
)
self.qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=self.retriever,
return_source_documents=True,
chain_type_kwargs={"prompt": self.qa_prompt}
)
def query(self, question):
return self.qa_chain({"query": question})
# Usage
rag_system = RAGSystem(vector_store)
response = rag_system.query("What is the capital of France?")
print(response["result"])
The RAG system uses Gemma 2 to Ollama as a language model and Nomic embeddings for document retrieval. It allows you to ask questions based on indexed documents and provide contextual answers from relevant sources.
For specific tasks or domains, you may want to fine-tune Gemma 2. Here is a basic example using the Hugging Face Transformers library:
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
from datasets import load_dataset
# Load model and tokenizer
model_name = "google/gemma-2-9b-it"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Prepare dataset
dataset = load_dataset("your_dataset")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Set up training arguments
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
warmup_steps=500,
weight_decay=0.01,
logging_dir="./logs",
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
)
# Start fine-tuning
trainer.train()
# Save the fine-tuned model
model.save_pretrained("./fine_tuned_gemma2")
tokenizer.save_pretrained("./fine_tuned_gemma2")
Adjust the training parameters according to your specific requirements and computing resources.
While Gemma 2 offers impressive capabilities, one must be aware of its limitations and ethical considerations:
Gemma 2's advanced features, such as sliding window attention, soft capping, and novel model merging techniques, make it a powerful tool for a wide range of natural language processing tasks.
By leveraging Gemma 2 in your projects, whether through simple inference, complex RAG systems, or fine-tuned models for specific domains, you can harness the power of SOTA AI while maintaining control over your data and processes.
Original address:https://www.unite.ai/complete-guide-on-gemma-2-googles-new-open-large-language-model/
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