Mixtral-8x7B kini tersedia di Amazon SageMaker JumpStart | Layanan Web 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)

Anda mendapatkan output berikut:

> 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)

Model mixtral dapat memberikan pemahaman seperti yang ditunjukkan pada keluaran berikut dengan logika matematika:

> 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.

Membersihkan

Setelah Anda selesai menjalankan buku catatan, hapus semua sumber daya yang Anda buat dalam proses sehingga penagihan Anda dihentikan. Gunakan kode berikut:

predictor.delete_model()
predictor.delete_endpoint()

Dalam postingan ini, kami menunjukkan kepada Anda cara memulai Mixtral-8x7B di SageMaker Studio dan menerapkan model untuk inferensi. Karena model dasar telah dilatih sebelumnya, model tersebut dapat membantu menurunkan biaya pelatihan dan infrastruktur serta memungkinkan penyesuaian untuk kasus penggunaan Anda. Kunjungi SageMaker JumpStart di SageMaker Studio sekarang untuk memulai.

Sumber

Tentang penulis

Rakhna Chadha adalah Arsitek Solusi Utama AI/ML dalam Akun Strategis di AWS. Rachna adalah seorang optimis yang percaya bahwa penggunaan AI secara etis dan bertanggung jawab dapat meningkatkan masyarakat di masa depan dan membawa kemakmuran ekonomi dan sosial. Di waktu luangnya, Rachna suka menghabiskan waktu bersama keluarganya, mendaki gunung, dan mendengarkan musik.

Dr.Kyle Ulrich adalah Ilmuwan Terapan dengan Algoritme bawaan Amazon SageMaker tim. Minat penelitiannya meliputi algoritme pembelajaran mesin yang dapat diskalakan, visi komputer, deret waktu, non-parametrik Bayesian, dan proses Gaussian. PhD-nya dari Duke University dan dia telah menerbitkan makalah di NeurIPS, Cell, dan Neuron.

Christopher Whitten adalah pengembang perangkat lunak di tim JumpStart. Dia membantu menskalakan pemilihan model dan mengintegrasikan model dengan layanan SageMaker lainnya. Chris bersemangat untuk mempercepat keberadaan AI di berbagai domain bisnis.

Dr.Fabio Nonato de Paula adalah Manajer Senior, Spesialis GenAI SA, yang membantu penyedia model dan pelanggan menskalakan AI generatif di AWS. Fabio memiliki hasrat untuk mendemokratisasi akses terhadap teknologi AI generatif. Di luar pekerjaan, Anda bisa menemukan Fabio mengendarai sepeda motornya di perbukitan Lembah Sonoma atau membaca ComiXology.

Dr Ashish Khetan adalah Ilmuwan Terapan Senior dengan algoritme bawaan Amazon SageMaker dan membantu mengembangkan algoritme pembelajaran mesin. Ia mendapatkan gelar PhD dari University of Illinois Urbana-Champaign. Dia adalah peneliti aktif dalam pembelajaran mesin dan inferensi statistik, dan telah menerbitkan banyak makalah di konferensi NeurIPS, ICML, ICLR, JMLR, ACL, dan EMNLP.

Karl Albertsen memimpin produk, teknik, dan sains untuk Algoritma Amazon SageMaker dan JumpStart, pusat pembelajaran mesin SageMaker. Dia bersemangat menerapkan pembelajaran mesin untuk membuka nilai bisnis.