【TinyLlama-1.1B】わずか1.1Bの高性能な超小型版Llama 2を使ってみた

!pip install git+https://github.com/huggingface/transformers.git
!pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]

<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>
There is no exact scientific or medical definition of a human's maximum daily caloric intake, but a human can typically consume up to 1,500 calories per day. However, this does not necessarily mean that a human can consume an equal number of calories from all sources. Some individuals may consume more calories from energy sources like carbohydrates, proteins, and fats than others, while others may consume less. Some people may also have a lower metabolic rate or require more calories to maintain their weight. Therefore, the number of helicopters that a human can eat in one sitting is likely dependent on factors such as age, weight, and physical fitness level.

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are good japanese assistant",
    },
    {"role": "user", "content": "Please create a business email using correct polite and humble language to be sent to clients in Japanese."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Subject: Request for Quote for [Product/Service]

Dear [Client's Name],

I am writing to inquire about your interest in our latest product/service, which we believe would be a perfect fit for your business. Our company, [Your Company Name], is a leading provider of [Product/Service] in the market and we believe that our product/service would complement your business well.

Our product/service is designed to provide [insert details about the product/service]. This product/service has gained immense popularity in the industry due to its unique features and benefits. Our company has been providing this product/service to various businesses across the country and we have been getting positive feedback from our clients.

We are offering a [insert price range for the product/service] for your consideration. Our pricing strategy aims to provide our clients with value for their money, while still maintaining our competitive edge in the market. We believe that our pricing strategy aligns with your business needs and would make us an ideal partner for your business.

If you are interested in learning more about our product/service, we would be happy to schedule a meeting with you. Our team is available

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are good programmer.",
    },
    {"role": "user", "content": "Write the code for the regression analysis in Python."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

I don't have the capability to run python code or provide you with the code. However, here's a general overview of how you can perform a regression analysis in python using scikit-learn:

1. Import the necessary libraries:
   ```python
   import pandas as pd
   import numpy as np
   from sklearn.preprocessing import scale
   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import linear_model
   from sklearn.metrics import mean_squared_error, mean_absolute_error
   from sklearn.metrics import r2_score
   ```

2. Load the data:
   ```python
   data = pd.read_csv('your_data_file.csv')
   X = data.drop('target', axis=1)
   y = data['target']
   ```

3. Preprocess the data:
   ```python
   X_scaled = scale(X)
   ```

4. Split the data into train and test sets:
   ```python
   X_train, X_test, y_train

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are good novel writer.",
    },
    {"role": "user", "content": "Write a 400-word sensual novel with a married woman as the theme."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Title: Sensual Passions

Chapter One

Maggie had always been content with her life as a middle-aged housewife. She had a loving husband, two adorable children, and a comfortable home. But she had always been curious about what else life had to offer. So one day, she decided to take a chance and pursue her passion for writing.

Maggie had always been an avid reader, but she never imagined that one day she would write a book. But after years of struggling to find inspiration, she decided to take a leap of faith. Her husband was supportive and encouraged her every step of the way.

Maggie spent hours pouring out her heart onto the pages of her notebook, pouring out her deepest emotions, and exploring her own sensuality. She wrote about the ways in which her husband's body moved in ways that she had never seen before, the way his scent lingered on her skin, the way his touch made her heart race.

As she wrote, she found herself becoming more and more entranced by her own passion. She began to explore new ways to express her sensuality

タイトル 官能的な情熱

第一章

マギーは中年の主婦としての生活に満足していた。愛する夫、二人のかわいい子供、そして快適な家庭。しかし彼女はいつも、人生には他に何があるのだろうと好奇心を抱いていた。そこである日、彼女は思い切って書くことへの情熱を追求することにした。

マギーはいつも熱心な読書家だったが、いつか本を書く日が来るとは想像もしていなかった。しかし、何年もインスピレーションを見つけるのに苦労した後、彼女は信仰の跳躍を取ることを決めた。彼女の夫も協力的で、一歩一歩彼女を励ましてくれた。

マギーは何時間もかけてノートのページに心を注ぎ、深い感情を吐き出し、自分自身の官能性を探求した。彼女は、夫の身体が今まで見たこともないような動き方をすること、彼の香りが肌に残ること、彼の手触りが胸を高鳴らせることなどを書き綴った。

書きながら、彼女は自分自身の情熱にますます魅了されていくのがわかった。彼女は自分の官能性を表現する新しい方法を模索し始めた。