tomasmcm / go-bruins-v2

Source: rwitz/go-bruins-v2 ✦ Quant: TheBloke/go-bruins-v2-AWQ ✦ Designed to push the boundaries of NLP applications, offering unparalleled performance in generating human-like text

Cold

Public
219 runs
L40S
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

<|im_start|>system
- You are a helpful assistant chatbot.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
<|im_start|>user
Explain photosynthesis<|im_end|>
<|im_start|>assistant<|im_start|>system
- You are a helpful assistant chatbot.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
<|im_start|>user
Explain photosynthesis<|im_end|>
<|im_start|>assistant

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/go-bruins-v2

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/go-bruins-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tomasmcm/go-bruins-v2:a201caf3e3847babfb7ebfe8d01fe8e4a3a5bce04e044ff793cc05fceed1b198",
  {
    input: {
      top_k: -1,
      top_p: 0.95,
      prompt: "<|im_start|>system\n- You are a helpful assistant chatbot.\n- You answer questions.\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>\n<|im_start|>user\nExplain photosynthesis<|im_end|>\n<|im_start|>assistant",
      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/go-bruins-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tomasmcm/go-bruins-v2:a201caf3e3847babfb7ebfe8d01fe8e4a3a5bce04e044ff793cc05fceed1b198",
    input={
        "top_k": -1,
        "top_p": 0.95,
        "prompt": "<|im_start|>system\n- You are a helpful assistant chatbot.\n- You answer questions.\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>\n<|im_start|>user\nExplain photosynthesis<|im_end|>\n<|im_start|>assistant",
        "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/go-bruins-v2 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": "a201caf3e3847babfb7ebfe8d01fe8e4a3a5bce04e044ff793cc05fceed1b198",
    "input": {
      "top_k": -1,
      "top_p": 0.95,
      "prompt": "<|im_start|>system\\n- You are a helpful assistant chatbot.\\n- You answer questions.\\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>\\n<|im_start|>user\\nExplain photosynthesis<|im_end|>\\n<|im_start|>assistant",
      "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

During photosynthesis, green plants and certain other organisms convert sunlight into chemical energy through a process that uses water and carbon dioxide from the environment. This chemical energy is stored in the form of glucose, which can be used as a source of fuel by the plant. The overall equation for photosynthesis is: 6CO₂ + 6H₂O + light energy → C₆H₁₂O₆ + 6O₂ In this equation, 6 molecules of carbon dioxide (CO₂) combine with 6 molecules of water (

Generated in

4.0 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.0079 to run on Replicate, or 126 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 9 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Go Bruins V2 - A Fine-tuned Language Model

Updates

Overview

Go Bruins-V2 is a language model fine-tuned on the rwitz/go-bruins architecture. It’s designed to push the boundaries of NLP applications, offering unparalleled performance in generating human-like text.

Model Details

Developer: Ryan Witzman
Base Model: rwitz/go-bruins
Fine-tuning Method: Direct Preference Optimization (DPO)
Training Steps: 642
Language: English
License: MIT

Capabilities

Go Bruins excels in a variety of NLP tasks, including but not limited to: - Text generation - Language understanding - Sentiment analysis

Usage

Warning: This model may output NSFW or illegal content. Use with caution and at your own risk.

For Direct Use:

from transformers import pipeline

model_name = "rwitz/go-bruins-v2"
inference_pipeline = pipeline('text-generation', model=model_name)

input_text = "Your input text goes here"
output = inference_pipeline(input_text)

print(output)

Not Recommended For:

Illegal activities
Harassment
Professional advice or crisis situations

Training and Evaluation

Trained on a dataset from athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW, Go Bruins V2 has shown promising improvements over its predecessor, Go Bruins.

Evaluations

Metric	Average	Arc Challenge	Hella Swag	MMLU	Truthful Q&A	Winogrande	GSM8k
Score	72.07	69.8	87.05	64.75	59.7	81.45	69.67

Note: The original MMLU evaluation has been corrected to include 5-shot data rather than 1-shot data.

Contact

For any inquiries or feedback, reach out to Ryan Witzman on Discord: rwitz_.

This model card was created with care by Ryan Witzman. rewrite this model card for new version called go-bruins-v2 that is finetuned on dpo on the original go-bruins model on athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW