adirik / mamba-2.8b-slimpj

Base version of Mamba 2.8B Slim Pyjama, a 2.8 billion parameter state space language model

Cold

Public
73 runs
L40S
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Text prompt to send to the model.

max_length

integer

(minimum: 1, maximum: 5000)

Maximum number of tokens to generate. A word is generally 2-3 tokens.

Default: 100

temperature

number

(minimum: 0.1, maximum: 5)

Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.

Default: 1

top_p

number

(minimum: 0.01, maximum: 1)

When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens.

Default: 1

top_k

integer

When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens.

Default: 1

repetition_penalty

number

(minimum: 0.01, maximum: 10)

Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.

Default: 1.2

seed

integer

The seed for the random number generator

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

npx create-replicate --model=adirik/mamba-2.8b-slimpj

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 adirik/mamba-2.8b-slimpj using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "adirik/mamba-2.8b-slimpj:e663586c1922943ad155a146b4c843855e61241d9ac80db6103215927e5134ed",
  {
    input: {
      top_k: 1,
      top_p: 1,
      prompt: "How are you doing?",
      max_length: 100,
      temperature: 1,
      repetition_penalty: 1.2
    }
  }
);

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 adirik/mamba-2.8b-slimpj using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "adirik/mamba-2.8b-slimpj:e663586c1922943ad155a146b4c843855e61241d9ac80db6103215927e5134ed",
    input={
        "top_k": 1,
        "top_p": 1,
        "prompt": "How are you doing?",
        "max_length": 100,
        "temperature": 1,
        "repetition_penalty": 1.2
    }
)

# The adirik/mamba-2.8b-slimpj 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/adirik/mamba-2.8b-slimpj/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 adirik/mamba-2.8b-slimpj 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": "adirik/mamba-2.8b-slimpj:e663586c1922943ad155a146b4c843855e61241d9ac80db6103215927e5134ed",
    "input": {
      "top_k": 1,
      "top_p": 1,
      "prompt": "How are you doing?",
      "max_length": 100,
      "temperature": 1,
      "repetition_penalty": 1.2
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

I'm fine. I am very well, thank-you! How about yourself?" "Fine." (1) The man said that he was feeling good and asked how the woman felt herself to be at this moment in time; she replied by saying: 'Well' or 'Very Well'. This is a polite way of asking someone if they feel OK – it's not rude but just an expression used when we want people around us know what our feelings towards them may have been like recently as

{
  "completed_at": "2024-02-05T18:34:46.854902Z",
  "created_at": "2024-02-05T18:22:47.660154Z",
  "data_removed": false,
  "error": null,
  "id": "o6jwpptbnaycvo25ypknewfc4i",
  "input": {
    "top_k": 1,
    "top_p": 1,
    "prompt": "How are you doing?",
    "max_length": 100,
    "temperature": 1,
    "repetition_penalty": 1.2
  },
  "logs": null,
  "metrics": {
    "predict_time": 6.788395,
    "total_time": 719.194748
  },
  "output": [
    " ",
    "",
    "I'm ",
    "",
    "fine.\n",
    "",
    "I ",
    "am ",
    "very ",
    "",
    "well, ",
    "",
    "",
    "",
    "thank-you! ",
    "How ",
    "about ",
    "",
    "yourself?\" ",
    "",
    "",
    "\"Fine.\" ",
    "",
    "",
    "(1) ",
    "The ",
    "man ",
    "said ",
    "that ",
    "he ",
    "was ",
    "feeling ",
    "good ",
    "and ",
    "asked ",
    "how ",
    "the ",
    "woman ",
    "felt ",
    "herself ",
    "to ",
    "be ",
    "at ",
    "this ",
    "moment ",
    "in ",
    "",
    "time; ",
    "she ",
    "replied ",
    "by ",
    "",
    "saying: ",
    "",
    "",
    "'Well' ",
    "or ",
    "",
    "'Very ",
    "",
    "Well'. ",
    "This ",
    "is ",
    "a ",
    "polite ",
    "way ",
    "of ",
    "asking ",
    "someone ",
    "if ",
    "they ",
    "feel ",
    "OK ",
    "– ",
    "",
    "it's ",
    "not ",
    "rude ",
    "but ",
    "just ",
    "an ",
    "expression ",
    "used ",
    "when ",
    "we ",
    "want ",
    "people ",
    "around ",
    "us ",
    "know ",
    "what ",
    "our ",
    "feelings ",
    "towards ",
    "them ",
    "may ",
    "have ",
    "been ",
    "like ",
    "recently ",
    "as"
  ],
  "started_at": "2024-02-05T18:34:40.066507Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://streaming-api.svc.us.c.replicate.net/v1/streams/akxjq4qylt5el3254n3bc5h22xsrxpxeief3vgthlzapgodx7owq",
    "get": "https://api.replicate.com/v1/predictions/o6jwpptbnaycvo25ypknewfc4i",
    "cancel": "https://api.replicate.com/v1/predictions/o6jwpptbnaycvo25ypknewfc4i/cancel"
  },
  "version": "e663586c1922943ad155a146b4c843855e61241d9ac80db6103215927e5134ed"
}

Generated in

6.8 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.14 to run on Replicate, or 7 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 144 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Mamba

Mamba is a large language model with state space model architecture showing promising performance on information-dense data such as language modeling. See the original repo and paper for details.

Basic Usage

The API input arguments are as follows:

prompt: The text prompt for Mamba.
max_length: Maximum number of tokens to generate. A word is generally 2-3 tokens.
temperature: Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.
top_p: Samples from the top p percentage of most likely tokens during text decoding, lower to ignore less likely tokens.
top_k: Samples from the top k most likely tokens during text decoding, lower to ignore less likely tokens.
repetition_penalty: Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.
seed: The seed parameter for deterministic text generation. A specific seed can be used to reproduce results or left blank for random generation.

References

@article{mamba,
  title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
  author={Gu, Albert and Dao, Tri},
  journal={arXiv preprint arXiv:2312.00752},
  year={2023}
}