kcaverly/neuralbeagle14-7b-gguf:4c5eaa79 | Run with an API on Replicate

Version

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kcaverly /neuralbeagle14-7b-gguf:4c5eaa79

Input

prompt

*string

Shift + Return to add a new line

How much wood would a woodchuck chuck if a woodchuck could chuck wood?How much wood would a woodchuck chuck if a woodchuck could chuck wood?

Instruction for model

system_prompt

string

Shift + Return to add a new line

You are 'Neural-Beagle', an AI assistant and your purpose and drive is to assist the user with any request they have.You are 'Neural-Beagle', an AI assistant and your purpose and drive is to assist the user with any request they have.

System prompt for the model, helps guides model behaviour.

Default: "You are 'Neural-Beagle', an AI assistant and your purpose and drive is to assist the user with any request they have."

prompt_template

string

Shift + Return to add a new line

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant: <|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant: 

Template to pass to model. Override if you are providing multi-turn instructions.

Default: "<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant: "

max_new_tokens

integer

Maximum new tokens to generate.

Default: 512

repeat_penalty

number

This parameter plays a role in controlling the behavior of an AI language model during conversation or text generation. Its purpose is to discourage the model from repeating itself too often by increasing the likelihood of following up with different content after each response. By adjusting this parameter, users can influence the model's tendency to either stay within familiar topics (lower penalty) or explore new ones (higher penalty). For instance, setting a high repeat penalty might result in more varied and dynamic conversations, whereas a low penalty could be suitable for scenarios where consistency and predictability are preferred.

Default: 1.1

temperature

number

This parameter used to control the 'warmth' or responsiveness of an AI model based on the LLaMA architecture. It adjusts how likely the model is to generate new, unexpected information versus sticking closely to what it has been trained on. A higher value for this parameter can lead to more creative and diverse responses, while a lower value results in safer, more conservative answers that are closer to those found in its training data. This parameter is particularly useful when fine-tuning models for specific tasks where you want to balance between generating novel insights and maintaining accuracy and coherence.

Default: 0.5

Run this model in Node.js with one line of code:

npx create-replicate --model=kcaverly/neuralbeagle14-7b-gguf

or set up a project from scratch

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 kcaverly/neuralbeagle14-7b-gguf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "kcaverly/neuralbeagle14-7b-gguf:4c5eaa791be134c9711ea3a93e62f699540f733fd83c3a2d3df21be42dbdda25",
  {
    input: {
      prompt: "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
      temperature: 0.1,
      system_prompt: "You are 'Neural-Beagle', an AI assistant and your purpose and drive is to assist the user with any request they have.",
      max_new_tokens: 512,
      repeat_penalty: 1.1,
      prompt_template: "<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant: "
    }
  }
);

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 kcaverly/neuralbeagle14-7b-gguf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "kcaverly/neuralbeagle14-7b-gguf:4c5eaa791be134c9711ea3a93e62f699540f733fd83c3a2d3df21be42dbdda25",
    input={
        "prompt": "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
        "temperature": 0.1,
        "system_prompt": "You are 'Neural-Beagle', an AI assistant and your purpose and drive is to assist the user with any request they have.",
        "max_new_tokens": 512,
        "repeat_penalty": 1.1,
        "prompt_template": "<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant: "
    }
)

# The kcaverly/neuralbeagle14-7b-gguf 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/kcaverly/neuralbeagle14-7b-gguf/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=<paste-your-token-here>

Find your API token in your account settings.

Run kcaverly/neuralbeagle14-7b-gguf 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": "kcaverly/neuralbeagle14-7b-gguf:4c5eaa791be134c9711ea3a93e62f699540f733fd83c3a2d3df21be42dbdda25",
    "input": {
      "prompt": "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
      "temperature": 0.1,
      "system_prompt": "You are \'Neural-Beagle\', an AI assistant and your purpose and drive is to assist the user with any request they have.",
      "max_new_tokens": 512,
      "repeat_penalty": 1.1,
      "prompt_template": "<|im_start|>system\\n{system_prompt}<|im_end|>\\n<|im_start|>user\\n{prompt}<|im_end|>\\n<|im_start|>assistant: "
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

This is an amusing riddle or phrase that doesn't have a literal answer. It's often used to illustrate the absurdity of asking how much something would do if it couldn't naturally perform that action. In this case, woodchucks (also known as groundhogs) are not known for chucking wood, as their diet consists mainly of plants and they dig burrows. So, there is no specific amount of wood a woodchuck could or would chuck if it were possible for them to do so.

{
  "completed_at": "2024-01-19T22:18:59.798760Z",
  "created_at": "2024-01-19T22:14:32.568492Z",
  "data_removed": false,
  "error": null,
  "id": "azutjsbbrm7sfnix2w2w2qkxri",
  "input": {
    "prompt": "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
    "temperature": 0.1,
    "system_prompt": "You are 'Neural-Beagle', an AI assistant and your purpose and drive is to assist the user with any request they have.",
    "max_new_tokens": 512,
    "repeat_penalty": 1.1,
    "prompt_template": "<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant: "
  },
  "logs": "llama_print_timings:        load time =     856.31 ms\nllama_print_timings:      sample time =      82.09 ms /   118 runs   (    0.70 ms per token,  1437.53 tokens per second)\nllama_print_timings: prompt eval time =     856.09 ms /    91 tokens (    9.41 ms per token,   106.30 tokens per second)\nllama_print_timings:        eval time =    6084.68 ms /   117 runs   (   52.01 ms per token,    19.23 tokens per second)\nllama_print_timings:       total time =    7623.67 ms",
  "metrics": {
    "predict_time": 7.647223,
    "total_time": 267.230268
  },
  "output": [
    "\n",
    "This",
    " is",
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    " am",
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    " r",
    "iddle",
    " or",
    " phrase",
    " that",
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    " a",
    " literal",
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    " It",
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    " In",
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    " So",
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    ".",
    "\n",
    "",
    ""
  ],
  "started_at": "2024-01-19T22:18:52.151537Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://streaming-api.svc.us-central1.g.replicate.net/v1/predictions/azutjsbbrm7sfnix2w2w2qkxri",
    "get": "https://api.replicate.com/v1/predictions/azutjsbbrm7sfnix2w2w2qkxri",
    "cancel": "https://api.replicate.com/v1/predictions/azutjsbbrm7sfnix2w2w2qkxri/cancel"
  },
  "version": "4c5eaa791be134c9711ea3a93e62f699540f733fd83c3a2d3df21be42dbdda25"
}

Generated in

7.7 seconds

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