Mixtral-8x7B тепер доступний в Amazon SageMaker JumpStart | Веб-сервіси Amazon

from sagemaker.jumpstart.model import JumpStartModel model = JumpStartModel(model_id="huggingface-llm-mixtral-8x7b")
predictor = model.deploy()

payload = {"inputs": "Hello!"} predictor.predict(payload)

# Code generation
payload = { "inputs": "Write a program to compute factorial in python:", "parameters": { "max_new_tokens": 200, },
}
predictor.predict(payload)

Input Text: Write a program to compute factorial in python:
Generated Text:
Factorial of a number is the product of all the integers from 1 to that number. For example, factorial of 5 is 1*2*3*4*5 = 120. Factorial of 0 is 1. Factorial of a negative number is not defined. The factorial of a number can be written as n!. For example, 5! = 120. ## Write a program to compute factorial in python ```
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n-1) print(factorial(5))
``` Output: ```
120
``` ## Explanation: In the above program, we have defined a function called factorial which takes a single argument n. If n is equal to 0, then we return 1. Otherwise, we return n multiplied by the factorial of n-1. We then call the factorial function with the argument 5 and print the result. The output of the program is 120, which is the factorial of 5. ## How to compute factorial in python In the above program, we have used a recursive function to compute the factorial of a number. A recursive function is a function that calls itself. In this case, the factorial function calls itself with the argument n-1. This process continues until n is equal to 0, at which point the function returns 1. The factorial of a number can also be computed using a loop. For example, the following program uses a for loop to compute the factorial of a number: ```
def factorial(n):
result = 1
for i in range(1, n+1):
result *= i
return result

payload = { "inputs": """
Tweet: "I hate it when my phone battery dies."
Sentiment: Negative Tweet: "My day has been :+1:"
Sentiment: Positive Tweet: "This is the link to the article"
Sentiment: Neutral Tweet: "This new music video was incredibile"
Sentiment:""", "parameters": { "max_new_tokens": 2,
},
}
predictor.predict(payload)

Input Text: Tweet: "I hate it when my phone battery dies."
Sentiment: Negative
###
Tweet: "My day has been :+1:"
Sentiment: Positive
###
Tweet: "This is the link to the article"
Sentiment: Neutral
###
Tweet: "This new music video was incredibile"
Sentiment:
Generated Text:  Positive

# Question answering
payload = { "inputs": "Could you remind me when was the C programming language invented?", "parameters": { "max_new_tokens": 100, },
}
query_endpoint(payload)

Input Text: Could you remind me when was the C programming language invented?
Generated Text: C was invented in 1972 by Dennis Ritchie at Bell Labs. C is a general-purpose programming language. It was invented to write the UNIX operating system. C is a structured programming language. It is a middle-level language. It is a procedural language. C is a compiled language. It is a portable language. C is a case-sensitive language. It is a free-form language

<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]]

instructions = [{"role": "user", "content": "what is the recipe of mayonnaise?"}]
prompt = format_instructions(instructions)
payload = { "inputs": prompt, "parameters": {"max_new_tokens": 315, "do_sample": True}
}
response=predictor.predict(payload)
print_instructions(prompt, response)

> Input
<s>[INST] what is the recipe of mayonnaise? [/INST]</s> > Output
Here is a simple recipe for homemade mayonnaise: Ingredients: * 1 egg yolk
* 1 tablespoon of mustard (Dijon is a good choice)
* 1 cup of oil (canola or vegetable oil work well)
* 1-2 tablespoons of white vinegar or lemon juice
* Salt to taste Instructions: 1. In a medium bowl, whisk together the egg yolk and mustard. It is important that these ingredients are at room temperature.
2. Start adding the oil drop by drop, whisking continuously. This step is crucial to ensure that the mayonnaise emulsifies and does not separate.
3. Once you have added about a quarter of the oil and the mixture has thickened, you can start adding the oil in a thin stream, still whisking constantly.
4. When all the oil has been incorporated and the mayonnaise is thick, whisk in the vinegar or lemon juice and salt to taste.
5. If the mayonnaise is too thick, you can thin it with a little water.
6. Store the mayonnaise in the refrigerator and use within a few days. Note: It is important to use pasteurized eggs or egg yolks when making homemade mayonnaise to reduce the risk of foodborne illness.

instructions = [
{ "role": "user", "content": "In Bash, how do I list all text files in the current directory (excluding subdirectories) that have been modified in the last month?",
}
]
prompt = format_instructions(instructions)
payload = { "inputs": prompt, "parameters": {"max_new_tokens": 256, "do_sample": True, "temperature": 0.2}
}
response=predictor.predict(payload)
print_instructions(prompt, response)

Ви отримуєте наступний результат:

> Input
<s>[INST] In Bash, how do I list all text files in the current directory (excluding subdirectories) that have been modified in the last month? [/INST]</s>
> Output
ef{0.15cm} To list all text files in the current directory that have been modified in the last month, you can use a combination of the `find` and `grep` commands in Bash. Here's the command you're looking for: ```bash
find . -maxdepth 1 -type f -name "*.txt" -mtime -30
``` Let's break down this command: - `find .` starts a search in the current directory.
- `-maxdepth 1` limits the search to the current directory only (excluding subdirectories).
- `-type f` specifies that you're looking for files.
- `-name "*.txt"` filters the results to only include files with a `.txt` extension.
- `-mtime -30` filters the results to only include files modified within the last 30 days. This command will output the paths of all text files in the current directory that have been modified in the last month.

instructions = [
{ "role": "user", "content": "I bought an ice cream for 6 kids. Each cone was $1.25 and I paid with a $10 bill. How many dollars did I get back? Explain first before answering.",
}
]
prompt = format_instructions(instructions)
payload = { "inputs": prompt, "parameters": {"max_new_tokens": 600, "do_sample": True, "temperature": 0.2}
}
response=predictor.predict(payload)
print_instructions(prompt, response)

Моделі Mixtral можуть забезпечити розуміння, як показано в наступному виході з математичною логікою:

> Input
<s>[INST] I bought an ice cream for 6 kids. Each cone was $1.25 and I paid with a $10 bill. How many dollars did I get back? Explain first before answering. [/INST] </s>
> Output
First, let's calculate the total cost of the ice cream cones. Since each cone costs $1.25 and you bought 6 cones, the total cost would be: Total cost = Cost per cone * Number of cones
Total cost = $1.25 * 6
Total cost = $7.50 Next, subtract the total cost from the amount you paid with the $10 bill to find out how much change you got back: Change = Amount paid - Total cost
Change = $10 - $7.50
Change = $2.50 So, you got $2.50 back.

Прибирати

Після того, як ви завершите роботу блокнота, видаліть усі ресурси, які ви створили в процесі, щоб виставлення рахунків припинилося. Використовуйте наступний код:

predictor.delete_model()
predictor.delete_endpoint()

У цій публікації ми показали вам, як розпочати роботу з Mixtral-8x7B у SageMaker Studio та розгорнути модель для висновку. Оскільки базові моделі проходять попередню підготовку, вони можуть допомогти знизити витрати на навчання та інфраструктуру, а також уможливити налаштування для вашого випадку використання. Щоб почати, відвідайте SageMaker JumpStart у SageMaker Studio.

ресурси

Про авторів

Рачна Чадха є головним архітектором рішень AI/ML у Strategic Accounts в AWS. Рахна — оптиміст, який вірить, що етичне та відповідальне використання ШІ може покращити суспільство в майбутньому та принести економічне та соціальне процвітання. У вільний час Рачна любить проводити час з сім'єю, гуляти в походах і слухати музику.

Доктор Кайл Ульріх є прикладним науковцем з Вбудовані алгоритми Amazon SageMaker команда. Його дослідницькі інтереси включають масштабовані алгоритми машинного навчання, комп’ютерне бачення, часові ряди, байєсівські непараметричні процеси та процеси Гаусса. Його доктор філософії отримав в Університеті Дьюка, і він опублікував статті в NeurIPS, Cell і Neuron.

Крістофер Віттен є розробником програмного забезпечення в команді JumpStart. Він допомагає масштабувати вибір моделей та інтегрувати моделі з іншими службами SageMaker. Кріс захоплений прискоренням повсюдного поширення ШІ в різних сферах бізнесу.

Доктор Фабіо Нонато де Паула є старшим менеджером спеціаліста GenAI SA, який допомагає постачальникам моделей і клієнтам масштабувати генеративний ШІ в AWS. Фабіо має пристрасть до демократизації доступу до генеративної технології ШІ. Поза роботою ви можете знайти Фабіо, який катається на своєму мотоциклі пагорбами долини Сонома або читає ComiXology.

Доктор Ашиш Хетан є старшим прикладним науковим співробітником із вбудованими алгоритмами Amazon SageMaker і допомагає розробляти алгоритми машинного навчання. Він отримав ступінь доктора філософії в Іллінойському університеті Урбана-Шампейн. Він активно досліджує машинне навчання та статистичні висновки та опублікував багато статей на конференціях NeurIPS, ICML, ICLR, JMLR, ACL та EMNLP.

Карл Альбертсен очолює продукт, розробку та науку для Amazon SageMaker Algorithms і JumpStart, центру машинного навчання SageMaker. Він захоплений застосуванням машинного навчання для розкриття цінності бізнесу.