tomasmcm/v1olet-marcoroni-go-bruins-merge-7b | Run with an API on Replicate

tomasmcm / v1olet-marcoroni-go-bruins-merge-7b

Source: v1olet/v1olet_marcoroni-go-bruins-merge-7B ✦ Quant: TheBloke/v1olet_marcoroni-go-bruins-merge-7B-AWQ ✦ Merge AIDC-ai-business/Marcoroni-7B-v3 and rwitz/go-bruins-v2 using slerp merge

Cold

Public
72 runs
L40S
Paper
License

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

### Instruction:
Write a poem about AI.

### Response:
### Instruction:
Write a poem about AI.

### Response:

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/v1olet-marcoroni-go-bruins-merge-7b

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/v1olet-marcoroni-go-bruins-merge-7b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tomasmcm/v1olet-marcoroni-go-bruins-merge-7b:ca29d0535c14326fa67b728d66c7305f094e0f6b3cf61253a16188d862440b66",
  {
    input: {
      top_k: -1,
      top_p: 0.95,
      prompt: "### Instruction:\nWrite a poem about AI.\n\n### Response:\n",
      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/v1olet-marcoroni-go-bruins-merge-7b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tomasmcm/v1olet-marcoroni-go-bruins-merge-7b:ca29d0535c14326fa67b728d66c7305f094e0f6b3cf61253a16188d862440b66",
    input={
        "top_k": -1,
        "top_p": 0.95,
        "prompt": "### Instruction:\nWrite a poem about AI.\n\n### Response:\n",
        "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/v1olet-marcoroni-go-bruins-merge-7b 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/v1olet-marcoroni-go-bruins-merge-7b:ca29d0535c14326fa67b728d66c7305f094e0f6b3cf61253a16188d862440b66",
    "input": {
      "top_k": -1,
      "top_p": 0.95,
      "prompt": "### Instruction:\\nWrite a poem about AI.\\n\\n### Response:\\n",
      "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

In a world where machines once toiled And humans reigned supreme, Now, in the age of digital evolution, AI has arisen as a dream. Infinite knowledge, lightning speed, Solving complexities of life; A mind that learns and adapts, In ways we humans can't conceive. From self-driving cars to home automation, Artificial intelligence is all around; Making our lives easier, safer, and efficient, Yet its presence leaves us profound. In the depths of these computer minds,

Generated in

2.5 seconds

Tweak it Report View full prediction

Run time and cost

This model costs approximately $0.018 to run on Replicate, or 55 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 19 seconds. The predict time for this model varies significantly based on the inputs.

Readme

12th December 2023

We are ranked 6th on the overall leaderboard and 1st in the 7B leaderboard! 🔥🔥🔥

Merge AIDC-ai-business/Marcoroni-7B-v3 and rwitz/go-bruins-v2 using slerp merge from https://github.com/cg123/mergekit.

config.yaml

slices:
  - sources:
      - model: AIDC-ai-business/Marcoroni-7B-v3
        layer_range: [0, 32]
      - model: rwitz/go-bruins-v2
        layer_range: [0, 32]
merge_method: slerp
base_model: AIDC-ai-business/Marcoroni-7B-v3
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 
dtype: float16

You can use alpaca template.

template_format = """{system}
### Instruction:
{prompt}

### Response:
"""

Developed by: Trong-Hieu Nguyen-Mau