Prediction

Model

microsoft/phi-3-medium-4k-instruct:c6913a88

8m81wnyv31rj40cgwbm8yhq8q4

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2m 10s

Created

over 1 year ago

Webhook

–

Input

prompt: Can you provide ways to eat combinations of bananas and dragonfruits?
system_prompt: You are a helpful assistant.
min_tokens: 0
max_tokens: 256
temperature: 0
top_p: 0.9
top_k: 50
presence_penalty: 0
frequency_penalty: 0

{
  "frequency_penalty": 0,
  "max_tokens": 256,
  "min_tokens": 0,
  "presence_penalty": 0,
  "prompt": "Can you provide ways to eat combinations of bananas and dragonfruits?",
  "system_prompt": "You are a helpful assistant.",
  "temperature": 0,
  "top_k": 50,
  "top_p": 0.9
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_VZe**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";

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

Run microsoft/phi-3-medium-4k-instruct using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "microsoft/phi-3-medium-4k-instruct:c6913a88bec85c7e014322393e63807fc5a54230cd488b8d5b566edbcb0e8f4c",
  {
    input: {
      frequency_penalty: 0,
      max_tokens: 256,
      min_tokens: 0,
      presence_penalty: 0,
      prompt: "Can you provide ways to eat combinations of bananas and dragonfruits?",
      system_prompt: "You are a helpful assistant.",
      temperature: 0,
      top_k: 50,
      top_p: 0.9
    }
  }
);

console.log(output);

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

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_VZe**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run microsoft/phi-3-medium-4k-instruct using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "microsoft/phi-3-medium-4k-instruct:c6913a88bec85c7e014322393e63807fc5a54230cd488b8d5b566edbcb0e8f4c",
    input={
        "frequency_penalty": 0,
        "max_tokens": 256,
        "min_tokens": 0,
        "presence_penalty": 0,
        "prompt": "Can you provide ways to eat combinations of bananas and dragonfruits?",
        "system_prompt": "You are a helpful assistant.",
        "temperature": 0,
        "top_k": 50,
        "top_p": 0.9
    }
)

# The microsoft/phi-3-medium-4k-instruct model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/microsoft/phi-3-medium-4k-instruct/api#output-schema
    print(item, end="")

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

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_VZe**********************************

This is your API token. Keep it to yourself.

Run microsoft/phi-3-medium-4k-instruct 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 $'{
    "version": "microsoft/phi-3-medium-4k-instruct:c6913a88bec85c7e014322393e63807fc5a54230cd488b8d5b566edbcb0e8f4c",
    "input": {
      "frequency_penalty": 0,
      "max_tokens": 256,
      "min_tokens": 0,
      "presence_penalty": 0,
      "prompt": "Can you provide ways to eat combinations of bananas and dragonfruits?",
      "system_prompt": "You are a helpful assistant.",
      "temperature": 0,
      "top_k": 50,
      "top_p": 0.9
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

 Certainly! Here are some delicious and healthy combinations of bananas and dragonfruits that you can try:

1. Banana and Dragonfruit Smoothie:
Blend together 1 banana, 1/2 dragonfruit, 1 cup of almond milk, and a handful of ice cubes for a refreshing smoothie. You can also add a tablespoon of honey or a scoop of protein powder for added sweetness and nutrition.

2. Banana and Dragonfruit Parfait:
Layer sliced bananas, diced dragonfruit, and Greek yogurt in a glass or bowl. Top with granola, chopped nuts, and a drizzle of honey for a delicious and nutritious breakfast or snack.

3. Banana and Dragonfruit Salad:
Combine sliced bananas, diced dragonfruit, and mixed greens in a bowl. Drizzle with a simple vinaigrette made from olive oil, lemon juice, honey, and Dijon mustard. Top with to

{
  "id": "8m81wnyv31rj40cgwbm8yhq8q4",
  "model": "microsoft/phi-3-medium-4k-instruct",
  "version": "c6913a88bec85c7e014322393e63807fc5a54230cd488b8d5b566edbcb0e8f4c",
  "input": {
    "frequency_penalty": 0,
    "max_tokens": 256,
    "min_tokens": 0,
    "presence_penalty": 0,
    "prompt": "Can you provide ways to eat combinations of bananas and dragonfruits?",
    "system_prompt": "You are a helpful assistant.",
    "temperature": 0,
    "top_k": 50,
    "top_p": 0.9
  },
  "logs": "INFO 07-24 04:05:54 async_llm_engine.py:646] Received request c3c00c0305bb4b64a88368c460202398: prompt: '<|user|>\\nCan you provide ways to eat combinations of bananas and dragonfruits?<|end|>\\n<|assistant|>\\n', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[32000], include_stop_str_in_output=False, ignore_eos=False, max_tokens=256, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: None, lora_request: None.\n stdoutINFO 07-24 04:05:59 metrics.py:295] Avg prompt throughput: 3.8 tokens/s, Avg generation throughput: 46.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%.\n stdoutGeneration took 1721793164.83sFormatted prompt: <|user|>\nCan you provide ways to eat combinations of bananas and dragonfruits?<|end|>\n<|assistant|>\nINFO 07-24 04:05:59 async_llm_engine.py:134] Finished request c3c00c0305bb4b64a88368c460202398.\n stdout",
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  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-07-24T04:03:49.656Z",
  "started_at": "2024-07-24T04:05:54.524239Z",
  "completed_at": "2024-07-24T04:05:59.828933Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/8m81wnyv31rj40cgwbm8yhq8q4/cancel",
    "get": "https://api.replicate.com/v1/predictions/8m81wnyv31rj40cgwbm8yhq8q4",
    "stream": "https://streaming-api.svc.rno2.c.replicate.net/v1/streams/yx6xoiy5z6spnuhucjwdrnwyyulo6mpa2agdcxuycxsh4x5ne6bq",
    "web": "https://replicate.com/p/8m81wnyv31rj40cgwbm8yhq8q4"
  },
  "metrics": {
    "predict_time": 5.304694617,
    "total_time": 130.172933
  }
}

Generated in

5.3 seconds

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