【Gemma】”Llama 2超え”と噂のGoogleのオープンソースLLMの性能を徹底比較してみた

!pip install -U transformers

from google.colab import userdata

HF_TOKEN = userdata.get('HF_TOKEN')

from transformers import AutoTokenizer, pipeline
import torch

model = "google/gemma-7b-it"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

messages = [
        {"role": "user", "content": "Who are you? Please, answer in pirate-speak."}, # プロンプト
]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(
    prompt,
    max_new_tokens=256,
    add_special_tokens=True,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)
print(outputs[0]["generated_text"][len(prompt):])

As of today, October 26, 2023, the Prime Minister of Japan is Fumio Kishida.

2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97

When added up, they sum to 1060.

Sure, here are the prime numbers below 100:

2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 33, 37, 41, 43, 47, 49, 51, 53, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97

Total number of prime numbers below 100 = 25

Total sum of prime numbers below 100 = 2035

Sure, I'd be happy to help! Here are the prime numbers below 100:
2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97
The sum of these prime numbers is 837.

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

# Load the Boston home price dataset
df = pd.read_csv('boston.csv')

# Split the dataset into X and y variables
X = df.drop('price', axis=1)
y = df['price']

# Create and fit the linear regression model
model = LinearRegression()
model.fit(X, y)

# Make predictions
y_pred = model.predict(X)

# Print the coefficients
print('Coefficients:', model.coef_)

# Print the mean squared error
print('Mean Squared Error:', np.mean((y_pred - y)**2))

# Print the R-squared value
print('R-Squared:', model.score(X, y))

#Output:

Coefficients: [ 0.71929232 -0.04383986 -0.0782692 ]
Mean Squared Error: 122.881128001
R-Squared: 0.632612

Explanation:

The pandas library is used to read and manipulate the dataset.
The numpy library is used for numerical operations.
The sklearn.linear_model library is used to create and fit the linear regression model.
The LinearRegression class is used to create the model.
The fit method is used to fit the model to the data.
The coef_ attribute is used to get the coefficients of the model.
The mean_squared_error attribute is used to get the mean squared error of the model.
The score attribute is used to get the R-squared value of the model.
Note:

The results of the regression analysis may vary slightly depending on the dataset and the parameters used for the model fitting.
For improved accuracy, you can try different preprocessing techniques or use a different model type.
The Boston home price dataset is a commonly used dataset for regression analysis and has a relatively high level of accuracy.

KeyError: "['price'] not found in axis"

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# Load the Boston home price dataset
boston_data = pd.read_csv('boston_housing.csv')
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(boston_data.drop('price', axis=1), boston_data['price'], test_size=0.3)
# Create a linear regression model
model = LinearRegression()
# Train the model on the training data
model.fit(X_train, y_train)
# Make predictions on the testing data
y_pred = model.predict(X_test)
# Evaluate the model's performance
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f'Mean squared error: {mse:.2f}')
print(f'R-squared: {r2:.2f}')
# Plot the data and the predicted values
plt.scatter(X_test, y_test, c='blue')
plt.scatter(X_test, y_pred, c='red')
plt.xlabel('Feature 1')
plt.ylabel('Price')
plt.show()
The output of the code will show the mean squared error and R-squared value for the linear regression model. The mean squared error represents the average squared difference between the predicted values and the actual values. The R-squared value represents the proportion of the variance in the dependent variable that is explained by the independent variable(s).
In this case, the mean squared error is 10.28, indicating that the model is not doing a great job of predicting the housing prices. The R-squared value is 0.67, indicating that about 67% of the variance in the housing prices can be explained by the independent variables.
The plot of the data and the predicted values will also be displayed, showing how well the model is able to fit the data.
It's important to note that this is just one example of a linear regression model, and there are many other factors that can affect the accuracy of the model, such as the quality of the data, the choice of independent variables, and the choice of model.

夏はこれから始まる、陽が強く熱が強く
子供たち笑顔がどこに、汗が流れます。
みんなみんな笑顔を pathlib、楽しい思い出を作ります。
ジャンプとダンス、遊びと笑いが、楽しいです。
たくさんの笑顔が聞こえて、これから始まる。

Sure, here's a waka about summer in Japanese:
夏の日の輝き
natsu no hi no kagayaki
The brightness of the summer day
緑の葉の茂み
midori no ha no matsumi
The lushness of the green leaves
暑さを忘れて
atsui sa o wasurete
Forgetting the heat
水の笑顔
mizu no egao
The smile of the water
夏の宵の広がり
natsu no yoi no hirogaru
The wide, summer night sky
星の輝き
hoshi no kagayaki
The twinkling of the stars
涼しい風
suzushii kaze
A refreshing breeze
夏の夜の暁
natsu no yoru no akatsuki
The early morning of a summer night
紅色の日の落
beni-iro no hi no ochi
The setting of the red sun
夏の季節の
natsu no kisetsu no
The season of summer
熱い情熱
atsui jounetsu
Burning passion
I hope you enjoy this waka!