cjwbw / gorilla

Gorilla: Large Language Model Connected with Massive APIs

Cold

Public
89 runs
L40S
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Prediction

cjwbw/gorilla:4a1a7ce8

Model

cjwbw/gorilla:4a1a7ce8

oj3y2nlbepfocmd42hrhheezve

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

5m 11s

Created

over 1 year ago

Input

prompt: I would like to translate 'I feel very good today.' from English to French.
model_name: gorilla-llm/gorilla-mpt-7b-hf-v0
temperature: 0.7
max_new_tokens: 1024

{
  "prompt": "I would like to translate 'I feel very good today.' from English to French.",
  "model_name": "gorilla-llm/gorilla-mpt-7b-hf-v0",
  "temperature": 0.7,
  "max_new_tokens": 1024
}

Install Replicate’s Node.js client library

npm install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client

import Replicate from "replicate";

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

Run cjwbw/gorilla using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/gorilla:4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
  {
    input: {
      prompt: "I would like to translate 'I feel very good today.' from English to French.",
      model_name: "gorilla-llm/gorilla-mpt-7b-hf-v0",
      temperature: 0.7,
      max_new_tokens: 1024
    }
  }
);
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=<paste-your-token-here>

Find your API token in your account settings.

Import the client

import replicate

Run cjwbw/gorilla using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/gorilla:4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
    input={
        "prompt": "I would like to translate 'I feel very good today.' from English to French.",
        "model_name": "gorilla-llm/gorilla-mpt-7b-hf-v0",
        "temperature": 0.7,
        "max_new_tokens": 1024
    }
)
print(output)

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=<paste-your-token-here>

Find your API token in your account settings.

Run cjwbw/gorilla 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": "4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
    "input": {
      "prompt": "I would like to translate \'I feel very good today.\' from English to French.",
      "model_name": "gorilla-llm/gorilla-mpt-7b-hf-v0",
      "temperature": 0.7,
      "max_new_tokens": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

code_parsed

1. Import the necessary T5Tokenizer and T5Model classes from the transformers library. 2. Instantiate the tokenizer and model with the 'google/byt5-small' pretrained model. 3. Use the tokenizer to tokenize the input English text, which is 'I feel very good today.'n4. Feed the tokenized input to the model and obtain the generated French text as output.

domain_parsed

I feel very good today.' domain

api_call_parsed

Natural Language Processing Text2Text Generation\n

raw_text_response

###EN: 'I feel very good today.' <<<domain>>>: Natural Language Processing Text2Text Generation\n<<<api_call>>>: T5Model.from_pretrained('google/byt5-small')\n<<<api_provider>>>: Hugging Face Transformers\n<<<explanation>>>: 1. Import the necessary T5Tokenizer and T5Model classes from the transformers library.\n2. Instantiate the tokenizer and model with the 'google/byt5-small' pretrained model.\n3. Use the tokenizer to tokenize the input English text, which is 'I feel very good today.'\n4. Feed the tokenized input to the model and obtain the generated French text as output.\n<<<code>>>: from transformers import T5Tokenizer, T5Model\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\nmodel = T5Model.from_pretrained('google/byt5-small')\ninput_text = \"translate English to French: I feel very good today.\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids\noutputs = model.generate(input_ids)\ntranslated_text = tokenizer.decode(outputs[0])\nprint(translated_text)" """ # Translate English text to French using a pre-trained T5 model.\nI feel very good today." <<<code>>>: from transformers import T5Tokenizer, T5Model\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\nmodel = T5Model.from_pretrained('google/byt5-small')\ninput_text = \"translate English to French: I feel very good today.\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids\noutputs = model.generate(input_ids)\ntranslated_text = tokenizer.decode(outputs[0])\nprint(translated_text)" # Translate 'I feel very good today.' to French using a T5 model." # Output: \"J'ai un sentiment très bien aujourd'hui.\"" from transformers import T5Tokenizer, T5Model\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\nmodel = T5Model.from_pretrained('google/byt5-small')\ninput_text = \"translate English to French: I feel very good today.\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids\noutputs = model.generate(input_ids)\ntranslated_text = tokenizer.decode(outputs[0])\nprint(translated_text)" # Translate 'I feel very good today.' to French using a T5 model." Dans cette instruction, nous allons vous apprendre comment transformer une phrase d'anglais en français à l'aide de la technologie T5. \n1. Importez T5Tokenizer et T5Model de transformers.\n2. Créez un tokenizer et un model avec le pretrained 'google/byt5-small' modèle.\n3. Translatez la phrase d'anglais en français en utilisant tokenizer et model.\n4. Imprimez le résultat de la translation sous forme de texte." from transformers import T5Tokenizer, T5Model\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\nmodel = T5Model.from_pretrained('google/byt5-small')\ninput_text = \"translate English to French: I feel very good today.\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids\noutputs = model.generate(input_ids)\ntranslated_text = tokenizer.decode(outputs[0])\nprint(translated_text)" # Translate 'I feel very good today.' to French using a T5 model." <<<code>>>: from transformers import T5Tokenizer, T5Model\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\nmodel = T5Model.from_pretrained('google/byt5-small')\ninput_text = \"translate English to French: I feel very good today.\"\ninput_ids = token

explanation_parsed

Hugging Face Transformers

api_provider_parsed

T5Model.from_pretrained('google/byt5-small')\n

{
  "completed_at": "2023-11-07T12:19:54.748263Z",
  "created_at": "2023-11-07T12:14:43.896592Z",
  "data_removed": false,
  "error": null,
  "id": "oj3y2nlbepfocmd42hrhheezve",
  "input": {
    "prompt": "I would like to translate 'I feel very good today.' from English to French.",
    "model_name": "gorilla-llm/gorilla-mpt-7b-hf-v0",
    "temperature": 0.7,
    "max_new_tokens": 1024
  },
  "logs": "/root/.pyenv/versions/3.11.4/lib/python3.11/site-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\nwarnings.warn(",
  "metrics": {
    "predict_time": 54.737309,
    "total_time": 310.851671
  },
  "output": {
    "code_parsed": "1. Import the necessary T5Tokenizer and T5Model classes from the transformers library.\n2. Instantiate the tokenizer and model with the 'google/byt5-small' pretrained model.\n3. Use the tokenizer to tokenize the input English text, which is 'I feel very good today.'n4. Feed the tokenized input to the model and obtain the generated French text as output.\n",
    "domain_parsed": "I feel very good today.' domain",
    "api_call_parsed": "Natural Language Processing Text2Text Generation\\n",
    "raw_text_response": "###EN: 'I feel very good today.' <<<domain>>>: Natural Language Processing Text2Text Generation\\n<<<api_call>>>: T5Model.from_pretrained('google/byt5-small')\\n<<<api_provider>>>: Hugging Face Transformers\\n<<<explanation>>>: 1. Import the necessary T5Tokenizer and T5Model classes from the transformers library.\\n2. Instantiate the tokenizer and model with the 'google/byt5-small' pretrained model.\\n3. Use the tokenizer to tokenize the input English text, which is 'I feel very good today.'\\n4. Feed the tokenized input to the model and obtain the generated French text as output.\\n<<<code>>>: from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\\nmodel = T5Model.from_pretrained('google/byt5-small')\\ninput_text = \\\"translate English to French: I feel very good today.\\\"\\ninput_ids = tokenizer(input_text, return_tensors=\\\"pt\\\").input_ids\\noutputs = model.generate(input_ids)\\ntranslated_text = tokenizer.decode(outputs[0])\\nprint(translated_text)\" \"\"\" # Translate English text to French using a pre-trained T5 model.\\nI feel very good today.\" <<<code>>>: from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\\nmodel = T5Model.from_pretrained('google/byt5-small')\\ninput_text = \\\"translate English to French: I feel very good today.\\\"\\ninput_ids = tokenizer(input_text, return_tensors=\\\"pt\\\").input_ids\\noutputs = model.generate(input_ids)\\ntranslated_text = tokenizer.decode(outputs[0])\\nprint(translated_text)\" # Translate 'I feel very good today.' to French using a T5 model.\" # Output: \\\"J'ai un sentiment très bien aujourd'hui.\\\"\" from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\\nmodel = T5Model.from_pretrained('google/byt5-small')\\ninput_text = \\\"translate English to French: I feel very good today.\\\"\\ninput_ids = tokenizer(input_text, return_tensors=\\\"pt\\\").input_ids\\noutputs = model.generate(input_ids)\\ntranslated_text = tokenizer.decode(outputs[0])\\nprint(translated_text)\" # Translate 'I feel very good today.' to French using a T5 model.\" Dans cette instruction, nous allons vous apprendre comment transformer une phrase d'anglais en français à l'aide de la technologie T5. \\n1. Importez T5Tokenizer et T5Model de transformers.\\n2. Créez un tokenizer et un model avec le pretrained 'google/byt5-small' modèle.\\n3. Translatez la phrase d'anglais en français en utilisant tokenizer et model.\\n4. Imprimez le résultat de la translation sous forme de texte.\" from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\\nmodel = T5Model.from_pretrained('google/byt5-small')\\ninput_text = \\\"translate English to French: I feel very good today.\\\"\\ninput_ids = tokenizer(input_text, return_tensors=\\\"pt\\\").input_ids\\noutputs = model.generate(input_ids)\\ntranslated_text = tokenizer.decode(outputs[0])\\nprint(translated_text)\" # Translate 'I feel very good today.' to French using a T5 model.\" <<<code>>>: from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('google/byt5-small')\\nmodel = T5Model.from_pretrained('google/byt5-small')\\ninput_text = \\\"translate English to French: I feel very good today.\\\"\\ninput_ids = token",
    "explanation_parsed": "Hugging Face Transformers\n",
    "api_provider_parsed": "T5Model.from_pretrained('google/byt5-small')\\n"
  },
  "started_at": "2023-11-07T12:19:00.010954Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/oj3y2nlbepfocmd42hrhheezve",
    "cancel": "https://api.replicate.com/v1/predictions/oj3y2nlbepfocmd42hrhheezve/cancel"
  },
  "version": "4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083"
}

Generated in

54.7 seconds

Tweak it Share Report

Prediction

cjwbw/gorilla:4a1a7ce8

Model

cjwbw/gorilla:4a1a7ce8

bc2uwn3btg3wvuu5tejtocsi5m

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

4m 15s

Created

over 1 year ago

Input

prompt: I would like to translate 'I feel very good today.' from English to French.
model_name: gorilla-llm/gorilla-falcon-7b-hf-v0
temperature: 0.7
max_new_tokens: 1024

{
  "prompt": "I would like to translate 'I feel very good today.' from English to French.",
  "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
  "temperature": 0.7,
  "max_new_tokens": 1024
}

Install Replicate’s Node.js client library

npm install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client

import Replicate from "replicate";

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

Run cjwbw/gorilla using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/gorilla:4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
  {
    input: {
      prompt: "I would like to translate 'I feel very good today.' from English to French.",
      model_name: "gorilla-llm/gorilla-falcon-7b-hf-v0",
      temperature: 0.7,
      max_new_tokens: 1024
    }
  }
);
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=<paste-your-token-here>

Find your API token in your account settings.

Import the client

import replicate

Run cjwbw/gorilla using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/gorilla:4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
    input={
        "prompt": "I would like to translate 'I feel very good today.' from English to French.",
        "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
        "temperature": 0.7,
        "max_new_tokens": 1024
    }
)
print(output)

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=<paste-your-token-here>

Find your API token in your account settings.

Run cjwbw/gorilla 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": "4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
    "input": {
      "prompt": "I would like to translate \'I feel very good today.\' from English to French.",
      "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
      "temperature": 0.7,
      "max_new_tokens": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

code_parsed

from transformers import pipeline translator = pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr') translated_text = translator("I feel very good today.")[0]['translation_text']"

domain_parsed

Natural Language Processing Translation\n

api_call_parsed

pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr')\n

raw_text_response

\n<<<domain>>>: Natural Language Processing Translation\n<<<api_call>>>: pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr')\n<<<api_provider>>>: Hugging Face Transformers\n<<<explanation>>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\n2. We then use the pipeline function to create a translation model.\n3. We specify the model 'Helsinki-NLP/opus-mt-en-fr' to be loaded. This model is trained for English to French translation tasks, which is exactly what we need for translating 'I feel very good today.'\n4. We can pass the English text as input and the model will return the translated French text.\n<<<code>>>: from transformers import pipeline\ntranslator = pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr')\ntranslated_text = translator(\"I feel very good today.\")[0]['translation_text']"

explanation_parsed

1. We first import the pipeline function from the transformers library provided by Hugging Face. 2. We then use the pipeline function to create a translation model. 3. We specify the model 'Helsinki-NLP/opus-mt-en-fr' to be loaded. This model is trained for English to French translation tasks, which is exactly what we need for translating 'I feel very good today.'n4. We can pass the English text as input and the model will return the translated French text.

api_provider_parsed

Hugging Face Transformers\n

{
  "completed_at": "2023-11-07T12:32:36.416744Z",
  "created_at": "2023-11-07T12:28:21.908586Z",
  "data_removed": false,
  "error": null,
  "id": "bc2uwn3btg3wvuu5tejtocsi5m",
  "input": {
    "prompt": "I would like to translate 'I feel very good today.' from English to French.",
    "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
    "temperature": 0.7,
    "max_new_tokens": 1024
  },
  "logs": "/root/.pyenv/versions/3.11.4/lib/python3.11/site-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\nwarnings.warn(\nThe attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\nSetting `pad_token_id` to `eos_token_id`:11 for open-end generation.",
  "metrics": {
    "predict_time": 9.00843,
    "total_time": 254.508158
  },
  "output": {
    "code_parsed": "from transformers import pipeline\ntranslator = pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr')\ntranslated_text = translator(\"I feel very good today.\")[0]['translation_text']\"",
    "domain_parsed": "Natural Language Processing Translation\\n",
    "api_call_parsed": "pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr')\\n",
    "raw_text_response": "\\n<<<domain>>>: Natural Language Processing Translation\\n<<<api_call>>>: pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr')\\n<<<api_provider>>>: Hugging Face Transformers\\n<<<explanation>>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We then use the pipeline function to create a translation model.\\n3. We specify the model 'Helsinki-NLP/opus-mt-en-fr' to be loaded. This model is trained for English to French translation tasks, which is exactly what we need for translating 'I feel very good today.'\\n4. We can pass the English text as input and the model will return the translated French text.\\n<<<code>>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_fr', model='Helsinki-NLP/opus-mt-en-fr')\\ntranslated_text = translator(\\\"I feel very good today.\\\")[0]['translation_text']\"",
    "explanation_parsed": "1. We first import the pipeline function from the transformers library provided by Hugging Face.\n2. We then use the pipeline function to create a translation model.\n3. We specify the model 'Helsinki-NLP/opus-mt-en-fr' to be loaded. This model is trained for English to French translation tasks, which is exactly what we need for translating 'I feel very good today.'n4. We can pass the English text as input and the model will return the translated French text.\n",
    "api_provider_parsed": "Hugging Face Transformers\\n"
  },
  "started_at": "2023-11-07T12:32:27.408314Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/bc2uwn3btg3wvuu5tejtocsi5m",
    "cancel": "https://api.replicate.com/v1/predictions/bc2uwn3btg3wvuu5tejtocsi5m/cancel"
  },
  "version": "4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083"
}

Generated in

9.0 seconds

Tweak it Share Report

Prediction

cjwbw/gorilla:4a1a7ce8

Model

cjwbw/gorilla:4a1a7ce8

azql2s3blxyfvwtdjnsyot3rba

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

4m 22s

Created

over 1 year ago

Input

prompt: I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]
model_name: gorilla-llm/gorilla-falcon-7b-hf-v0
temperature: 0.7
max_new_tokens: 1024

{
  "prompt": "I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]",
  "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
  "temperature": 0.7,
  "max_new_tokens": 1024
}

Install Replicate’s Node.js client library

npm install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client

import Replicate from "replicate";

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

Run cjwbw/gorilla using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/gorilla:4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
  {
    input: {
      prompt: "I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]",
      model_name: "gorilla-llm/gorilla-falcon-7b-hf-v0",
      temperature: 0.7,
      max_new_tokens: 1024
    }
  }
);
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=<paste-your-token-here>

Find your API token in your account settings.

Import the client

import replicate

Run cjwbw/gorilla using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/gorilla:4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
    input={
        "prompt": "I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]",
        "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
        "temperature": 0.7,
        "max_new_tokens": 1024
    }
)
print(output)

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=<paste-your-token-here>

Find your API token in your account settings.

Run cjwbw/gorilla 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": "4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083",
    "input": {
      "prompt": "I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]",
      "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
      "temperature": 0.7,
      "max_new_tokens": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

code_parsed

from transformers import Mask2FormerForUniversalSegmentation from PIL import Image image = Image.open('cat.jpeg') # Replace 'cat.jpeg' with the path to your image model = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance') inputs = processor(images=image, return_tensors='pt') outputs = model(**inputs) "

domain_parsed

Computer Vision Image Segmentation\n

api_call_parsed

Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\n

raw_text_response

\n<<<domain>>>: Computer Vision Image Segmentation\n<<<api_call>>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\n<<<api_provider>>>: Hugging Face Transformers\n<<<explanation>>>: 1. Import the required classes from the transformers library, such as Mask2FormerForUniversalSegmentation for the image segmentation model.\n2. Use the from_pretrained method of the Mask2FormerForUniversalSegmentation class to load the pre-trained model 'facebook/mask2former-swin-tiny-coco-instance'.\n3. This model is designed for universal segmentation tasks, which includes detecting instances of objects in images.\n4. Load the image 'cat.jpeg' and process it using this model to get the segmentation results.\n<<<code>>>: from transformers import Mask2FormerForUniversalSegmentation\nfrom PIL import Image\nimage = Image.open('cat.jpeg')\n# Replace 'cat.jpeg' with the path to your image\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\ninputs = processor(images=image, return_tensors='pt')\noutputs = model(**inputs)\n"

explanation_parsed

1. Import the required classes from the transformers library, such as Mask2FormerForUniversalSegmentation for the image segmentation model. 2. Use the from_pretrained method of the Mask2FormerForUniversalSegmentation class to load the pre-trained model 'facebook/mask2former-swin-tiny-coco-instance'. 3. This model is designed for universal segmentation tasks, which includes detecting instances of objects in images. 4. Load the image 'cat.jpeg' and process it using this model to get the segmentation results.

api_provider_parsed

Hugging Face Transformers\n

{
  "completed_at": "2023-11-07T12:43:04.715894Z",
  "created_at": "2023-11-07T12:38:42.546046Z",
  "data_removed": false,
  "error": null,
  "id": "azql2s3blxyfvwtdjnsyot3rba",
  "input": {
    "prompt": "I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]",
    "model_name": "gorilla-llm/gorilla-falcon-7b-hf-v0",
    "temperature": 0.7,
    "max_new_tokens": 1024
  },
  "logs": "/root/.pyenv/versions/3.11.4/lib/python3.11/site-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\nwarnings.warn(\nThe attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\nSetting `pad_token_id` to `eos_token_id`:11 for open-end generation.",
  "metrics": {
    "predict_time": 11.681358,
    "total_time": 262.169848
  },
  "output": {
    "code_parsed": "from transformers import Mask2FormerForUniversalSegmentation\nfrom PIL import Image\nimage = Image.open('cat.jpeg')\n# Replace 'cat.jpeg' with the path to your image\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\ninputs = processor(images=image, return_tensors='pt')\noutputs = model(**inputs)\n\"",
    "domain_parsed": "Computer Vision Image Segmentation\\n",
    "api_call_parsed": "Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\\n",
    "raw_text_response": "\\n<<<domain>>>: Computer Vision Image Segmentation\\n<<<api_call>>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\\n<<<api_provider>>>: Hugging Face Transformers\\n<<<explanation>>>: 1. Import the required classes from the transformers library, such as Mask2FormerForUniversalSegmentation for the image segmentation model.\\n2. Use the from_pretrained method of the Mask2FormerForUniversalSegmentation class to load the pre-trained model 'facebook/mask2former-swin-tiny-coco-instance'.\\n3. This model is designed for universal segmentation tasks, which includes detecting instances of objects in images.\\n4. Load the image 'cat.jpeg' and process it using this model to get the segmentation results.\\n<<<code>>>: from transformers import Mask2FormerForUniversalSegmentation\\nfrom PIL import Image\\nimage = Image.open('cat.jpeg')\\n# Replace 'cat.jpeg' with the path to your image\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"",
    "explanation_parsed": "1. Import the required classes from the transformers library, such as Mask2FormerForUniversalSegmentation for the image segmentation model.\n2. Use the from_pretrained method of the Mask2FormerForUniversalSegmentation class to load the pre-trained model 'facebook/mask2former-swin-tiny-coco-instance'.\n3. This model is designed for universal segmentation tasks, which includes detecting instances of objects in images.\n4. Load the image 'cat.jpeg' and process it using this model to get the segmentation results.\n",
    "api_provider_parsed": "Hugging Face Transformers\\n"
  },
  "started_at": "2023-11-07T12:42:53.034536Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/azql2s3blxyfvwtdjnsyot3rba",
    "cancel": "https://api.replicate.com/v1/predictions/azql2s3blxyfvwtdjnsyot3rba/cancel"
  },
  "version": "4a1a7ce831b1315ab68a6b3ccf54bf376de787c93198a95a329582105b70e083"
}

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