tomasmcm/loyal-piano-m7 – Run with an API on Replicate

tomasmcm / loyal-piano-m7

Source: chargoddard/loyal-piano-m7 ✦ Quant: TheBloke/loyal-piano-m7-AWQ ✦ Intended to be a roleplay-focused model with some smarts and good long-context recall

Cold

Public
42 runs
L40S
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

<s>[INST] What is your favourite condiment? [/INST]<s>[INST] What is your favourite condiment? [/INST]

Text prompt to send to the model.

max_tokens

integer

Maximum number of tokens to generate per output sequence.

Default: 128

presence_penalty

number

(minimum: -5, maximum: 5)

Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

Default: 0

frequency_penalty

number

(minimum: -5, maximum: 5)

Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

Default: 0

temperature

number

(minimum: 0.01, maximum: 5)

Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.

Default: 0.8

top_p

number

(minimum: 0.01, maximum: 1)

Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.

Default: 0.95

top_k

integer

Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens.

Default: -1

stop

string

Shift + Return to add a new line

List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.

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

npx create-replicate --model=tomasmcm/loyal-piano-m7

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 tomasmcm/loyal-piano-m7 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tomasmcm/loyal-piano-m7:9b8a8331a9acf321e137c87e8378c3f1839ab6ee11c3bd338ea326c2c5a69e85",
  {
    input: {
      stop: "[INST]",
      top_k: -1,
      top_p: 0.95,
      prompt: "<s>[INST] What is your favourite condiment? [/INST]",
      max_tokens: 128,
      temperature: 0.8,
      presence_penalty: 0,
      frequency_penalty: 0
    }
  }
);

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 tomasmcm/loyal-piano-m7 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tomasmcm/loyal-piano-m7:9b8a8331a9acf321e137c87e8378c3f1839ab6ee11c3bd338ea326c2c5a69e85",
    input={
        "stop": "[INST]",
        "top_k": -1,
        "top_p": 0.95,
        "prompt": "<s>[INST] What is your favourite condiment? [/INST]",
        "max_tokens": 128,
        "temperature": 0.8,
        "presence_penalty": 0,
        "frequency_penalty": 0
    }
)
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 tomasmcm/loyal-piano-m7 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": "tomasmcm/loyal-piano-m7:9b8a8331a9acf321e137c87e8378c3f1839ab6ee11c3bd338ea326c2c5a69e85",
    "input": {
      "stop": "[INST]",
      "top_k": -1,
      "top_p": 0.95,
      "prompt": "<s>[INST] What is your favourite condiment? [/INST]",
      "max_tokens": 128,
      "temperature": 0.8,
      "presence_penalty": 0,
      "frequency_penalty": 0
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

My favorite condiment is mayonnaise. I love how it adds a creamy texture and rich flavor to sandwiches, salads, and other dishes. It's versatile and can be used in both sweet and savory recipes. Plus, it's just delicious on its own!

Generated in

1.6 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.0036 to run on Replicate, or 277 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 4 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Experimenting with dataset ratios. Intended to be a roleplay-focused model with some smarts and good long-context recall.

Not sure if I’ve succeeded on the roleplay front, but something sure went right! Currently the #4 7B model on the leaderboard as of 11/30/2023. Going to riff on this and see where it goes.

model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K	DROP
fblgit/juanako-7b-UNA	59.91	68.17	85.34	62.47	65.13	78.85	20.7	38.74
Intel/neural-chat-7b-v3-1	59.06	66.21	83.64	62.37	59.65	78.14	19.56	43.84
Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B	58.6	66.55	84.47	63.34	61.22	78.37	23.58	32.66
chargoddard/loyal-piano-m7	58.42	66.72	85.03	64.43	60.03	79.08	25.7	27.92
Gryphe/MythoMist7b	58.26	65.87	83.55	62.32	59.98	78.06	20.24	37.82

Dataset composition: | dataset | rows used | percent of total | | — | — | — | | PIPPA | 14.6k | 43% | | summarize_from_feedback | 9k | 26% | | orca_mini_v1_dataset | 5.6k | 17% | | rpguild | 2.86k | 8% | | LimaRP | 2k | 6% |