tomasmcm / openinstruct-mistral-7b

Source: monology/openinstruct-mistral-7b ✦ Quant: TheBloke/openinstruct-mistral-7B-AWQ ✦ Commercially-usable 7B model, based on mistralai/Mistral-7B-v0.1 and finetuned on VMware/open-instruct

Cold

Public
295 runs
L40S
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Can you write a poem about open source machine learning?

### Response:
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Can you write a poem about open source machine learning?

### 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/openinstruct-mistral-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/openinstruct-mistral-7b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tomasmcm/openinstruct-mistral-7b:d4a7ec393636c5b082359ea82d90d18537dcb36cf9219364e0d4b0888ca8d375",
  {
    input: {
      top_k: -1,
      top_p: 0.95,
      prompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nCan you write a poem about open source machine learning?\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/openinstruct-mistral-7b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tomasmcm/openinstruct-mistral-7b:d4a7ec393636c5b082359ea82d90d18537dcb36cf9219364e0d4b0888ca8d375",
    input={
        "top_k": -1,
        "top_p": 0.95,
        "prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nCan you write a poem about open source machine learning?\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/openinstruct-mistral-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": "d4a7ec393636c5b082359ea82d90d18537dcb36cf9219364e0d4b0888ca8d375",
    "input": {
      "top_k": -1,
      "top_p": 0.95,
      "prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nCan you write a poem about open source machine learning?\\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

Machine learning is a fascinating field That's for sure, I can't disagree With so many open source tools and libraries There's a model for every problem From linear regression to decision trees To support vector machines and deep neural nets There's no shortage of options, you can use Whatever model is best for your data set But with so many models out there It can be hard to choose which one to use So I recommend starting with the basics And working your way up to more advanced tools So go forth and explore the wonderful world

Generated in

2.9 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.0020 to run on Replicate, or 500 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 3 seconds.

Readme

OpenInstruct Mistral-7B

1st among commercially-usable 7B models on the Open LLM Leaderboard!*

This is mistralai/Mistral-7B-v0.1 finetuned on VMware/open-instruct.
Quantized to FP16 and released under the Apache-2.0 license by myself.
Compute generously provided by Higgsfield AI.

Prompt format: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
[your instruction goes here]

### Response:

Recommended preset:

temperature: 0.2
top_k: 50
top_p 0.95
repetition_penalty: 1.1

*as of 21 Nov 2023. “commercially-usable” includes both an open-source base model and a non-synthetic open-source finetune dataset. updated leaderboard results available here.