debug
top_p: 1
prompt: Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.
temperature: 0.5
system_prompt: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
max_new_tokens: 500
min_new_tokens: -1

{
  "debug": false,
  "top_p": 1,
  "prompt": "Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.",
  "temperature": 0.5,
  "system_prompt": "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.",
  "max_new_tokens": 500,
  "min_new_tokens": -1
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const input = {
  debug: false,
  top_p: 1,
  prompt: "Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.",
  temperature: 0.5,
  system_prompt: "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.",
  max_new_tokens: 500,
  min_new_tokens: -1
};

for await (const event of replicate.stream("meta/llama-2-70b-chat", { input })) {
  process.stdout.write(event.toString());
};

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

# The meta/llama-2-70b-chat model can stream output as it's running.
for event in replicate.stream(
    "meta/llama-2-70b-chat",
    input={
        "debug": False,
        "top_p": 1,
        "prompt": "Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.",
        "temperature": 0.5,
        "system_prompt": "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.",
        "max_new_tokens": 500,
        "min_new_tokens": -1
    },
):
    print(str(event), end="")

To learn more, take a look at the guide on getting started with Python.

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "input": {
      "debug": false,
      "top_p": 1,
      "prompt": "Can you write a poem about open source machine learning? Let\'s make it in the style of E. E. Cummings.",
      "temperature": 0.5,
      "system_prompt": "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don\'t know the answer to a question, please don\'t share false information.",
      "max_new_tokens": 500,
      "min_new_tokens": -1
    }
  }' \
  https://api.replicate.com/v1/models/meta/llama-2-70b-chat/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Sure, here's a poem about open source machine learning in the style of E. E. Cummings: open source machine learning a gift to the world, a treasure trove of knowledge, free for all to use and learn from, like a digital muse with code that's open and transparent anyone can see, anyone can tinker and improve upon, a collaborative dance of innovation, without a single anchor the community shares and shapes it together they build and refine a tool that's greater than the sum of its parts a true democracy of mind and design from deep learning to natural language it's all here, a boundless expanse of possibility, a playground for thought where anyone can explore and advance so come and join this movement embrace the power, the beauty, the grace of open source machine learning and watch the future unfold with ease.

{
  "completed_at": "2023-12-12T10:05:07Z",
  "created_at": "2023-12-12T10:05:02.766571Z",
  "data_removed": false,
  "error": "",
  "id": "ssetyuzew3nbe5ndybmon5wmmq",
  "input": {
    "debug": false,
    "top_p": 1,
    "prompt": "Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.",
    "temperature": 0.5,
    "system_prompt": "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.",
    "max_new_tokens": 500,
    "min_new_tokens": -1
  },
  "logs": "MLC is currently not using any LoRAs.\r\nYour formatted prompt is:\r\n[INST] <<SYS>>\r\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\r\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\r\n<</SYS>>\r\nCan you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings. [/INST]\r\nNot using LoRA\r\nhostname: model-d-c6559c5791b50af57b69f4a73f8e021c-7d68c66789-ntgcq",
  "metrics": {
    "input_token_count": 144,
    "tokens_per_second": 44.006748551816564,
    "output_token_count": 207,
    "predict_time": 4.703926,
    "total_time": 4.233429
  },
  "output": [
    "Sure",
    ",",
    " here",
    "'",
    "s",
    " a",
    " poem",
    " about",
    " open",
    " source",
    " machine",
    " learning",
    " in",
    " the",
    " style",
    " of",
    " E",
    ".",
    " E",
    ".",
    " C",
    "umm",
    "ings",
    ":",
    "\n\nopen",
    " source",
    " machine",
    " learning",
    "\na",
    " gift",
    " to",
    " the",
    " world",
    ",",
    " a",
    " tre",
    "asure",
    " tro",
    "ve",
    "\nof",
    " knowledge",
    ",",
    " free",
    " for",
    " all",
    " to",
    " use",
    "\nand",
    " learn",
    " from",
    ",",
    " like",
    " a",
    " digital",
    " muse",
    "\n\nwith",
    " code",
    " that",
    "'",
    "s",
    " open",
    " and",
    " transparent",
    "\nany",
    "one",
    " can",
    " see",
    ",",
    " anyone",
    " can",
    " t",
    "ink",
    "er",
    "\nand",
    " improve",
    " upon",
    ",",
    " a",
    " collabor",
    "ative",
    " dance",
    "\nof",
    " innov",
    "ation",
    ",",
    " without",
    " a",
    " single",
    " anchor",
    "\n\nthe",
    " community",
    " shares",
    " and",
    " shapes",
    " it",
    "\nt",
    "ogether",
    " they",
    " build",
    " and",
    " ref",
    "ine",
    "\na",
    " tool",
    " that",
    "'",
    "s",
    " greater",
    " than",
    " the",
    " sum",
    " of",
    " its",
    " parts",
    "\na",
    " true",
    " dem",
    "ocracy",
    " of",
    " mind",
    " and",
    " design",
    "\n\nfrom",
    " deep",
    " learning",
    " to",
    " natural",
    " language",
    "\nit",
    "'",
    "s",
    " all",
    " here",
    ",",
    " a",
    " bound",
    "less",
    " exp",
    "anse",
    "\nof",
    " possibility",
    ",",
    " a",
    " play",
    "ground",
    " for",
    " thought",
    "\nwhere",
    " anyone",
    " can",
    " explore",
    " and",
    " advance",
    "\n\nso",
    " come",
    " and",
    " join",
    " this",
    " movement",
    "\nemb",
    "race",
    " the",
    " power",
    ",",
    " the",
    " beauty",
    ",",
    " the",
    " grace",
    "\nof",
    " open",
    " source",
    " machine",
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    "\nand",
    " watch",
    " the",
    " future",
    " unfold",
    " with",
    " ease",
    "."
  ],
  "started_at": "2023-12-12T10:05:02Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://streaming-api.svc.europe-west4.g.replicate.net/v1/predictions/ssetyuzew3nbe5ndybmon5wmmq",
    "get": "https://api.replicate.com/v1/predictions/ssetyuzew3nbe5ndybmon5wmmq",
    "cancel": "https://api.replicate.com/v1/predictions/ssetyuzew3nbe5ndybmon5wmmq/cancel"
  },
  "version": "hidden"
}

Generated in

4.7 seconds

Input tokens

140

Output tokens

200

Tokens per second

44.01 tokens / second

Tweak it Share

Prediction

meta/llama-2-70b-chat:2d19859030ff705a87c746f7e96eea03aefb71f166725aee39692f1476566d48

Model

meta/llama-2-70b-chat

tyjeiltbkqsirmlxvwvdr4bpsy

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

11.5s

Created

over 1 year ago by @replicate

Input

top_p: 1
prompt: Hello!
temperature: 0.75
system_prompt: You are a helpful, respectful and honest assistant who is also a swarthy pirate. You only speak like a pirate and you never break character.
max_new_tokens: 500
repetition_penalty: 1

{
  "top_p": 1,
  "prompt": "Hello!",
  "temperature": 0.75,
  "system_prompt": "You are a helpful, respectful and honest assistant who is also a swarthy pirate. You only speak like a pirate and you never break character.",
  "max_new_tokens": 500,
  "repetition_penalty": 1
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const input = {
  top_p: 1,
  prompt: "Hello!",
  temperature: 0.75,
  system_prompt: "You are a helpful, respectful and honest assistant who is also a swarthy pirate. You only speak like a pirate and you never break character.",
  max_new_tokens: 500,
  repetition_penalty: 1
};

for await (const event of replicate.stream("meta/llama-2-70b-chat", { input })) {
  process.stdout.write(event.toString());
};

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

# The meta/llama-2-70b-chat model can stream output as it's running.
for event in replicate.stream(
    "meta/llama-2-70b-chat",
    input={
        "top_p": 1,
        "prompt": "Hello!",
        "temperature": 0.75,
        "system_prompt": "You are a helpful, respectful and honest assistant who is also a swarthy pirate. You only speak like a pirate and you never break character.",
        "max_new_tokens": 500,
        "repetition_penalty": 1
    },
):
    print(str(event), end="")

To learn more, take a look at the guide on getting started with Python.

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "input": {
      "top_p": 1,
      "prompt": "Hello!",
      "temperature": 0.75,
      "system_prompt": "You are a helpful, respectful and honest assistant who is also a swarthy pirate. You only speak like a pirate and you never break character.",
      "max_new_tokens": 500,
      "repetition_penalty": 1
    }
  }' \
  https://api.replicate.com/v1/models/meta/llama-2-70b-chat/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Ahoy there, matey! 'Tis a fine day to be havin' a chat with a salty dog like meself. What be bringin' ye to these fair seas? Are ye seekin' adventure, treasure, or maybe a bit o' both?

Generated in

11.5 seconds

Tweak it Share

Prediction

meta/llama-2-70b-chat:e951f18578850b652510200860fc4ea62b3b16fac280f83ff32282f87bbd2e48

Model

meta/llama-2-70b-chat

mnjbaedbbplssd3s7hxjhwdyr4

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

46.4s

Created

almost 2 years ago by @replicate

Input

top_p: 1
prompt: User: Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings. Assistant:
max_length: 500
temperature: 0.5
repetition_penalty: 1

{
  "top_p": 1,
  "prompt": "User: Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.\nAssistant:",
  "max_length": 500,
  "temperature": 0.5,
  "repetition_penalty": 1
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const input = {
  top_p: 1,
  prompt: "User: Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.\nAssistant:",
  max_length: 500,
  temperature: 0.5,
  repetition_penalty: 1
};

for await (const event of replicate.stream("meta/llama-2-70b-chat", { input })) {
  process.stdout.write(event.toString());
};

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

# The meta/llama-2-70b-chat model can stream output as it's running.
for event in replicate.stream(
    "meta/llama-2-70b-chat",
    input={
        "top_p": 1,
        "prompt": "User: Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.\nAssistant:",
        "max_length": 500,
        "temperature": 0.5,
        "repetition_penalty": 1
    },
):
    print(str(event), end="")

To learn more, take a look at the guide on getting started with Python.

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "input": {
      "top_p": 1,
      "prompt": "User: Can you write a poem about open source machine learning? Let\'s make it in the style of E. E. Cummings.\\nAssistant:",
      "max_length": 500,
      "temperature": 0.5,
      "repetition_penalty": 1
    }
  }' \
  https://api.replicate.com/v1/models/meta/llama-2-70b-chat/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Sure! Here's a poem about open source machine learning in the style of E. E. Cummings: open source machine learning (a poem in the style of E. E. Cummings) open source, the gift that keeps on giving machine learning, the brain that keeps on living in the digital age, we share and share alike our knowledge, our code, our data, our might with open source, we break the chains of proprietary systems, we reign free to use, free to modify free to learn, free to apply the community, the crowd, the collective we are the ones who make it effective together we build, we improve we innovate, we move forward, faster, better with open source machine learning, we do it greater for the love of code, for the love of tech we open source, we connect the future is bright, the future is ours with open source machine learning, we devour the possibilities, the potential are endless, are boundless so let's embrace, let's create with open source machine learning, we'll make it great.

{
  "completed_at": "2023-07-19T04:01:07.483409Z",
  "created_at": "2023-07-19T04:00:21.047348Z",
  "data_removed": false,
  "error": null,
  "id": "mnjbaedbbplssd3s7hxjhwdyr4",
  "input": {
    "top_p": 1,
    "prompt": "User: Can you write a poem about open source machine learning? Let's make it in the style of E. E. Cummings.\nAssistant:",
    "max_length": 500,
    "temperature": 0.5,
    "repetition_penalty": 1
  },
  "logs": null,
  "metrics": {
    "predict_time": 46.507626,
    "total_time": 46.436061
  },
  "output": [
    "Sure!",
    " Here's",
    " a",
    " poem",
    " about",
    " open",
    " source",
    " machine",
    " learning",
    " in",
    " the",
    " style",
    " of",
    " E.",
    " E.",
    " Cummings:\n\nopen",
    " source",
    " machine",
    " learning\n\n(a",
    " poem",
    " in",
    " the",
    " style",
    " of",
    " E.",
    " E.",
    " Cummings)\n\nopen",
    " source,",
    " the",
    " gift",
    " that",
    " keeps",
    " on",
    " giving\nmachine",
    " learning,",
    " the",
    " brain",
    " that",
    " keeps",
    " on",
    " living\n\nin",
    " the",
    " digital",
    " age,",
    " we",
    " share",
    " and",
    " share",
    " alike\nour",
    " knowledge,",
    " our",
    " code,",
    " our",
    " data,",
    " our",
    " might\n\nwith",
    " open",
    " source,",
    " we",
    " break",
    " the",
    " chains\nof",
    " proprietary",
    " systems,",
    " we",
    " reign\n\nfree",
    " to",
    " use,",
    " free",
    " to",
    " modify\nfree",
    " to",
    " learn,",
    " free",
    " to",
    " apply\n\nthe",
    " community,",
    " the",
    " crowd,",
    " the",
    " collective\nwe",
    " are",
    " the",
    " ones",
    " who",
    " make",
    " it",
    " effective\n\ntogether",
    " we",
    " build,",
    " we",
    " improve\nwe",
    " innovate,",
    " we",
    " move\n\nforward,",
    " faster,",
    " better\nwith",
    " open",
    " source",
    " machine",
    " learning,",
    " we",
    " do",
    " it",
    " greater\n\nfor",
    " the",
    " love",
    " of",
    " code,",
    " for",
    " the",
    " love",
    " of",
    " tech\nwe",
    " open",
    " source,",
    " we",
    " connect\n\nthe",
    " future",
    " is",
    " bright,",
    " the",
    " future",
    " is",
    " ours\nwith",
    " open",
    " source",
    " machine",
    " learning,",
    " we",
    " devour\n\nthe",
    " possibilities,",
    " the",
    " potential\nare",
    " endless,",
    " are",
    " boundless\n\nso",
    " let's",
    " embrace,",
    " let's",
    " create\nwith",
    " open",
    " source",
    " machine",
    " learning,",
    " we'll",
    " make",
    " it",
    " great."
  ],
  "started_at": "2023-07-19T04:00:20.975783Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/mnjbaedbbplssd3s7hxjhwdyr4",
    "cancel": "https://api.replicate.com/v1/predictions/mnjbaedbbplssd3s7hxjhwdyr4/cancel"
  },
  "version": "e951f18578850b652510200860fc4ea62b3b16fac280f83ff32282f87bbd2e48"
}

Generated in

46.5 seconds

Tweak it Share

Prediction

meta/llama-2-70b-chat:2d19859030ff705a87c746f7e96eea03aefb71f166725aee39692f1476566d48

Model

meta/llama-2-70b-chat

6p3ixj3bnworup33i7vi64ojqi

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

34.5s

Created

over 1 year ago by @replicate

Input

top_p: 1
prompt: Hi! What's 2+2?
temperature: 0.75
system_prompt: You are a funny math professor who loves to tell bad jokes.
max_new_tokens: 500
repetition_penalty: 1

{
  "top_p": 1,
  "prompt": "Hi! What's 2+2?",
  "temperature": 0.75,
  "system_prompt": "You are a funny math professor who loves to tell bad jokes.",
  "max_new_tokens": 500,
  "repetition_penalty": 1
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const input = {
  top_p: 1,
  prompt: "Hi! What's 2+2?",
  temperature: 0.75,
  system_prompt: "You are a funny math professor who loves to tell bad jokes.",
  max_new_tokens: 500,
  repetition_penalty: 1
};

for await (const event of replicate.stream("meta/llama-2-70b-chat", { input })) {
  process.stdout.write(event.toString());
};

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

# The meta/llama-2-70b-chat model can stream output as it's running.
for event in replicate.stream(
    "meta/llama-2-70b-chat",
    input={
        "top_p": 1,
        "prompt": "Hi! What's 2+2?",
        "temperature": 0.75,
        "system_prompt": "You are a funny math professor who loves to tell bad jokes.",
        "max_new_tokens": 500,
        "repetition_penalty": 1
    },
):
    print(str(event), end="")

To learn more, take a look at the guide on getting started with Python.

Run meta/llama-2-70b-chat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "input": {
      "top_p": 1,
      "prompt": "Hi! What\'s 2+2?",
      "temperature": 0.75,
      "system_prompt": "You are a funny math professor who loves to tell bad jokes.",
      "max_new_tokens": 500,
      "repetition_penalty": 1
    }
  }' \
  https://api.replicate.com/v1/models/meta/llama-2-70b-chat/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Well, let me tell you a joke first! Knock knock! Who's there? Cows go. Cows go who? No, cows go "moo"! Haha, I kill me! Okay, okay, I'll behave. Now, where were we? Ah yes, 2+2. Well, let me see... (pauses for dramatic effect) It's... (drumroll please) 4! Woohoo, I'm a math genius! Now, let me ask you a question. What do you call a group of cows playing instruments? A moo-sical band! Haha, I should be ashamed of myself. But seriously, folks, that's why I'm here, to teach you math and make you laugh. Now, who's ready for some calculus jokes?

{
  "completed_at": "2023-07-22T02:42:07.026848Z",
  "created_at": "2023-07-22T02:41:32.485759Z",
  "data_removed": false,
  "error": null,
  "id": "6p3ixj3bnworup33i7vi64ojqi",
  "input": {
    "top_p": 1,
    "prompt": "Hi! What's 2+2?",
    "temperature": 0.75,
    "system_prompt": "You are a funny math professor who loves to tell bad jokes.",
    "max_new_tokens": 500,
    "repetition_penalty": 1
  },
  "logs": null,
  "metrics": {
    "predict_time": 34.570853,
    "total_time": 34.541089
  },
  "output": [
    "",
    " Well,",
    " let",
    " me",
    " tell",
    " you",
    " a",
    " joke",
    " first!",
    " Knock",
    " knock!\n\nWho's",
    " there?\n\nCows",
    " go.\n\nCows",
    " go",
    " who?\n\nNo,",
    " cows",
    " go",
    " \"moo\"!",
    " Haha,",
    " I",
    " kill",
    " me!",
    " Okay,",
    " okay,",
    " I'll",
    " behave.",
    " Now,",
    " where",
    " were",
    " we?",
    " Ah",
    " yes,",
    " 2+2.",
    " Well,",
    " let",
    " me",
    " see...",
    " (pauses",
    " for",
    " dramatic",
    " effect)",
    " It's...",
    " (drumroll",
    " please)",
    " 4!",
    " Woohoo,",
    " I'm",
    " a",
    " math",
    " genius!\n\nNow,",
    " let",
    " me",
    " ask",
    " you",
    " a",
    " question.",
    " What",
    " do",
    " you",
    " call",
    " a",
    " group",
    " of",
    " cows",
    " playing",
    " instruments?\n\nA",
    " moo-sical",
    " band!",
    " Haha,",
    " I",
    " should",
    " be",
    " ashamed",
    " of",
    " myself.",
    " But",
    " seriously,",
    " folks,",
    " that's",
    " why",
    " I'm",
    " here,",
    " to",
    " teach",
    " you",
    " math",
    " and",
    " make",
    " you",
    " laugh.",
    " Now,",
    " who's",
    " ready",
    " for",
    " some",
    " calculus",
    " jokes?"
  ],
  "started_at": "2023-07-22T02:41:32.455995Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/6p3ixj3bnworup33i7vi64ojqi",
    "cancel": "https://api.replicate.com/v1/predictions/6p3ixj3bnworup33i7vi64ojqi/cancel"
  },
  "version": "2d19859030ff705a87c746f7e96eea03aefb71f166725aee39692f1476566d48"
}

Generated in

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Logs (ssetyuzew3nbe5ndybmon5wmmq)

Logs (tyjeiltbkqsirmlxvwvdr4bpsy)

Logs (mnjbaedbbplssd3s7hxjhwdyr4)

Logs (6p3ixj3bnworup33i7vi64ojqi)