Mixtral-8x7B er nu tilgængelig i Amazon SageMaker JumpStart | Amazon Web Services

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)

Du får følgende output:

> 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)

Mixtral-modeller kan give forståelse som vist i følgende output med den matematiske logik:

> 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.

Ryd op

Når du er færdig med at køre notesbogen, skal du slette alle ressourcer, du oprettede i processen, så din fakturering stoppes. Brug følgende kode:

predictor.delete_model()
predictor.delete_endpoint()

I dette indlæg viste vi dig, hvordan du kommer i gang med Mixtral-8x7B i SageMaker Studio og implementerer modellen til inferens. Fordi fundamentmodeller er foruddannede, kan de hjælpe med at sænke uddannelses- og infrastrukturomkostninger og muliggøre tilpasning til din brugssituation. Besøg SageMaker JumpStart i SageMaker Studio nu for at komme i gang.

Ressourcer

Om forfatterne

Rachna Chadha er Principal Solution Architect AI/ML i Strategic Accounts hos AWS. Rachna er en optimist, der mener, at etisk og ansvarlig brug af kunstig intelligens kan forbedre samfundet i fremtiden og bringe økonomisk og social velstand. I sin fritid kan Rachna godt lide at bruge tid med sin familie, vandreture og lytte til musik.

Dr. Kyle Ulrich er en anvendt videnskabsmand med Amazon SageMaker indbyggede algoritmer hold. Hans forskningsinteresser omfatter skalerbare maskinlæringsalgoritmer, computervision, tidsserier, Bayesianske ikke-parametriske og Gaussiske processer. Hans ph.d. er fra Duke University, og han har udgivet artikler i NeurIPS, Cell og Neuron.

Christopher Whitten er softwareudvikler på JumpStart-teamet. Han hjælper med at skalere modelvalg og integrere modeller med andre SageMaker-tjenester. Chris brænder for at accelerere AI's allestedsnærværelse på tværs af en række forretningsdomæner.

Dr. Fabio Nonato de Paula er en Senior Manager, Specialist GenAI SA, der hjælper modeludbydere og kunder med at skalere generativ AI i AWS. Fabio har en passion for at demokratisere adgangen til generativ AI-teknologi. Uden for arbejdet kan du finde Fabio kørende på sin motorcykel i bakkerne i Sonoma Valley eller læse ComiXology.

Dr. Ashish Khetan er en Senior Applied Scientist med Amazon SageMaker indbyggede algoritmer og hjælper med at udvikle machine learning algoritmer. Han fik sin ph.d. fra University of Illinois Urbana-Champaign. Han er en aktiv forsker i maskinlæring og statistisk inferens og har publiceret mange artikler i NeurIPS, ICML, ICLR, JMLR, ACL og EMNLP konferencer.

Karl Albertsen fører produkt, teknik og videnskab for Amazon SageMaker Algorithms og JumpStart, SageMakers maskinlæringshub. Han brænder for at anvende maskinlæring til at frigøre forretningsværdi.