2024-07-12
한어Русский языкEnglishFrançaisIndonesianSanskrit日本語DeutschPortuguêsΕλληνικάespañolItalianoSuomalainenLatina
Gemma 2 Aedificans in Decessore suo, amplificationem ac efficaciam praebet, necnon varias lineamenta porttitor, quae maxime delectabiles sunt tum inquisitionis tum applicationis practicae. Quod Gemma 2 seorsum ponit, id est quod exemplis proprietatis maioribus traditum comparandum, sed cum?software sarcinaDisposita ad ampliorem accessibilitatem et usum in modico hardware setups.
Cum in technicas notiones et architecturam Gemmae 2 intromitto, magis magis magisque imprimitur ab eius consilio ruditatis.Exemplar varias technologias provectae, etiam novae, incorporatoperam mechanismet innovat stabilitatem institutionis methodi, quae omnes ad eximiam eius observantiam conferunt.
In hoc comprehensivo duce, Gemma 2 penitus explorabitur, eius architecturam, clavem lineamenta et usus practicas intuens. Utrum sis peritus medicus AI vel alacer newbie in agro, hic articulum intendit praebere pervestigationes pretiosas in quomodo Gemma 2 opera et quomodo facultates suas in tuis inceptis pressionibus potes.
Gemma 2 est Google tardus fons apertus magnae-scalaelingua exemplar praeclare disposito sed potens. Eadem investigationis et technologiae usu aedificatur ad exempla Google Gemini ad creandos, statum-of-artem facientem tradens in sarcina faciliore. Gemma 2 venit in duas magnitudines;
Gemma 2 9B: A 9 sescenti exemplar parametri
Gemma 2 27B: Amplior 27 billion parametri exemplar
Quaelibet magnitudo venit in duobus generibus;
basic exemplum: Pre-exercitatio in magna copia textus data
Instructio Tuning (IT) Model: Subtilem in melius perficientur in munia
Exempla obvius in Google AI Studio：Google AI Studio – Gemma 2
Lege chartam hic: Gemma 2 Report Technical
Gemma 2 aliquot notabiles emendationes inducit super suum Decessorem;
Exempla haec magis in notitia exercitati sunt:
Gemma 2 27BPost disciplina XIII trillion signa
Gemma 2 9BPost disciplina VIII trillion signis
Haec extensa dataset principaliter consistit in retis notitiis (plerumque Anglice), codice, et mathematicis, adiuvans ad emendandum effectum et versatilem exemplorum tuorum.
Gemma 2 instrumenti novi methodi mechanismi attentionem;
Quaelibet alia tabula ad fenestram inclinatam mechanismum attente utitur, et contextus localis 4096 signa sunt.
Alterna stratum plene mechanismum quadraticam globalem adhibet pro totius contextu argumenti 8192.
Hic accessus hybridorum tendit ad efficientiam aequivalendi cum facultate capiendi longitudinis clientelas in input.
Ad stabiliendam et perficiendam institutionem emendandam, Gemma 2 mollis modum superiorem mechanismum inducit;
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)
Haec ars impedit logits ne nimis magnae sine difficili truncatione fiant, ita plura indicia retinens dum processus disciplinae stabilit.
Quaelibet magnitudo venit in duobus generibus;
Ad exemplar 9B, Gemma 2 scientia extractionis technologiae utitur;
Hic processus adiuvat exemplum minorem efficacius ad amplioris exemplaris functionem comprehendendam.
Gemma 2 utitur novo exemplari technologiae mergendi nomine Warp, quae multiplex exempla in tribus gradibus coniungit:
Accessio haec intendit ad exemplar supremum robustiorem et robustiorem creare.
Gemma 2 infigo monstrat observantia per varias benchmarks;
Gemma 2 features a redesigned architectura ad superiorem faciendam et consequentiam efficientiam disposito
Incipere usura Gemma 2 in inceptis tuis, sequentes optiones habes:
Gemma 2 per Google AI Studio accedere potest,Google Artificialis Intelligentia Studio.
Gemma 2 and Hugging Face Optimus bibliothecae integratio. Ecce quomodo ea uti;
<div class="relative flex flex-col rounded-lg">
<div class="text-text-300 absolute pl-3 pt-2.5 text-xs">
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)
Pro TensorFlow utentes, Gemma 2 per Keras praesto est:
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)
Potens applicatio Gemmae 2 aedificat systemata retreevalem-augetationem (RAG). RAG systema simplicia, plene nativa utentes Gemma 2 et Embedding Nomicis faciamus.
Primum fac tibi necessarias bibliothecas instituisse;
pip install langchain ollama nomic chromadb
Create indexer ut computetur in documentis:
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()
Nunc, RAG systema utens Gemma 2 crea.
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"])
Haec ratio RAG utitur Gemma 2 ad Ollama ut exemplar linguae et Nomica emblema pro documento retrievali. Permittit ut interrogationes ex documentis inscriptis interroges et ex fontibus congruentibus contextuas responsiones praebeas.
Pro certis operibus seu ditionibus, fortasse opus est ad cantilenam cantionum Gemma II. Hic est exemplum fundamentale utens bibliothecam Hugging Facie Transformatores:
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")
Parametris aptare institutionem in certis requisitis ac opum computandi ratione.
Dum Gemma 2 offert functionality infigendas, conscius esse debet suas limitationes et considerationes ethicae;
Gemma 2 notae provectae, sicut fenestrae inclinatae attentio, fines superiores molles, et novum exemplar artificiosae mergendi, efficax instrumentum ad amplitudinem linguae naturalium operas processus efficiunt.
Gemmam 2 in inceptis tuis levando, sive per simplicem consequentiam, composita RAG systemata, sive specialia subtilia exempla dominii, potentiam SOTA AI pressare potes, servata rerum tuarum notitiarum et processuum potestate.
Originale electronicum:https://www.unite.ai/complete-guide-on-gemma-2-googles-new-open-large-language-model/
technologiae technologiae plus quam triginta annos se dedit et in variis linguis proficit ut java, linux, javascript, php, css, etc., multasque contributiones in aperto fonte constituit cinematographica statione ad aliquas quaestiones technologias evolutionis ad futuram referentiam communicandam
