kcaverly/openchat-3.5-1210-gguf – Run with an API on Replicate

kcaverly / openchat-3.5-1210-gguf

The "Overall Best Performing Open Source 7B Model" for Coding + Generalization or Mathematical Reasoning

Cold

Public
26.2K runs
L40S
GitHub
Paper

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?

Instruction for model

prompt_template

string

Shift + Return to add a new line

GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: 

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

Default: "GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: "

max_new_tokens

integer

Maximum new tokens to generate.

Default: -1

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: 0.7

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

npx create-replicate --model=kcaverly/openchat-3.5-1210-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/openchat-3.5-1210-gguf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "kcaverly/openchat-3.5-1210-gguf:0d1426400ae23540eef130c0cd6cbd7184ac47cffee9dfd16fdf02d065df123b",
  {
    input: {
      prompt: "Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?",
      temperature: 0.7,
      max_new_tokens: -1,
      repeat_penalty: 1.1,
      prompt_template: "GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct 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/openchat-3.5-1210-gguf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "kcaverly/openchat-3.5-1210-gguf:0d1426400ae23540eef130c0cd6cbd7184ac47cffee9dfd16fdf02d065df123b",
    input={
        "prompt": "Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?",
        "temperature": 0.7,
        "max_new_tokens": -1,
        "repeat_penalty": 1.1,
        "prompt_template": "GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: "
    }
)

# The kcaverly/openchat-3.5-1210-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/openchat-3.5-1210-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/openchat-3.5-1210-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": "0d1426400ae23540eef130c0cd6cbd7184ac47cffee9dfd16fdf02d065df123b",
    "input": {
      "prompt": "Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?",
      "temperature": 0.7,
      "max_new_tokens": -1,
      "repeat_penalty": 1.1,
      "prompt_template": "GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: "
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run kcaverly/openchat-3.5-1210-gguf using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/kcaverly/openchat-3.5-1210-gguf@sha256:0d1426400ae23540eef130c0cd6cbd7184ac47cffee9dfd16fdf02d065df123b \
  -i 'prompt="Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?"' \
  -i 'temperature=0.7' \
  -i 'max_new_tokens=-1' \
  -i 'repeat_penalty=1.1' \
  -i 'prompt_template="GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: "'

To learn more, take a look at the Cog documentation.

Pull and run kcaverly/openchat-3.5-1210-gguf using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 --gpus=all r8.im/kcaverly/openchat-3.5-1210-gguf@sha256:0d1426400ae23540eef130c0cd6cbd7184ac47cffee9dfd16fdf02d065df123b
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?",
      "temperature": 0.7,
      "max_new_tokens": -1,
      "repeat_penalty": 1.1,
      "prompt_template": "GPT Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: "
    }
  }' \
  http://localhost:5000/predictions

Output

Since Sally is the only girl in her family, she must be considered as one of the "sisters" mentioned. Therefore, Sally has 2 sisters (including herself).

Generated in

0.9 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.016 to run on Replicate, or 62 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 17 seconds. The predict time for this model varies significantly based on the inputs.

Readme

A quantized, version of the OpenChat 3.5 1210 model.

The original model can be found here. The quantized version used is Q5_K_M, and can be found here.

There are two separate ‘modes’ available, which can be chosen via the prompt. This model has no system prompt.

Default Mode (GPT4 Correct): Best for coding, chat and general tasks

Mathematical Reasoning Mode: Tailored for solving math problems

Math Correct User: 10.3 − 7988.8133=<|end_of_turn|>Math Correct Assistant: