Mixtral-8x7B is now available in Amazon SageMaker JumpStart

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

 [INST] Instruction [/INST] Model answer [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
[INST] what is the recipe of mayonnaise? [/INST]

> 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)

You get the following output:

> Input
[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]
> 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 models can provide comprehension as shown in the following output with the math logic:

> Input
[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] 
> 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.

Clean up

After you’re done running the notebook, delete all resources that you created in the process so your billing is stopped. Use the following code:

predictor.delete_model()
predictor.delete_endpoint()

In this post, we showed you how to get started with Mixtral-8x7B in SageMaker Studio and deploy the model for inference. Because foundation models are pre-trained, they can help lower training and infrastructure costs and enable customization for your use case. Visit SageMaker JumpStart in SageMaker Studio now to get started.

Resources

• SageMaker JumpStart documentation
• SageMaker JumpStart foundation models documentation
• SageMaker JumpStart product detail page
• SageMaker JumpStart model catalog

About the authors

Rachna Chadha is a Principal Solution Architect AI/ML in Strategic Accounts at AWS. Rachna is an optimist who believes that ethical and responsible use of AI can improve society in the future and bring economic and social prosperity. In her spare time, Rachna likes spending time with her family, hiking, and listening to music.

Dr. Kyle Ulrich is an Applied Scientist with the Amazon SageMaker built-in algorithms team. His research interests include scalable machine learning algorithms, computer vision, time series, Bayesian non-parametrics, and Gaussian processes. His PhD is from Duke University and he has published papers in NeurIPS, Cell, and Neuron.

Christopher Whitten is a software developer on the JumpStart team. He helps scale model selection and integrate models with other SageMaker services. Chris is passionate about accelerating the ubiquity of AI across a variety of business domains.

Dr. Fabio Nonato de Paula is a Senior Manager, Specialist GenAI SA, helping model providers and customers scale generative AI in AWS. Fabio has a passion for democratizing access to generative AI technology. Outside of work, you can find Fabio riding his motorcycle in the hills of Sonoma Valley or reading ComiXology.

Dr. Ashish Khetan is a Senior Applied Scientist with Amazon SageMaker built-in algorithms and helps develop machine learning algorithms. He got his PhD from University of Illinois Urbana-Champaign. He is an active researcher in machine learning and statistical inference, and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.

Karl Albertsen leads product, engineering, and science for Amazon SageMaker Algorithms and JumpStart, SageMaker’s machine learning hub. He is passionate about applying machine learning to unlock business value.