Go Bruins - A Fine-tuned Language Model
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Updates
December 9, 2023: Go-Bruins has placed #6 overall and #1 for 7 billion parameter models on the Hugging Face Leaderboard!
Overview
Go Bruins is a state-of-the-art language model fine-tuned on the Q-bert/MetaMath-Cybertron-Starling 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: Q-bert/MetaMath-Cybertron-Starling
- Fine-tuning Method: Direct Preference Optimization (DPO)
- Training Steps: 200
- 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"
inference_pipeline = pipeline('text-generation', model=model_name)
input_text = "Your input text goes here"
output = inference_pipeline(input_text)
print(output)
GGUF Quantized Files are Located at NyxKrage/go-bruins-GGUF
Not Recommended For:
- Illegal activities
- Harassment
- Professional advice or crisis situations
Training and Evaluation
Trained on a dataset from Intel/orca_dpo_pairs, Go Bruins has shown promising improvements over its predecessor, Q-Bert.
Evaluations
Go-Bruins is the SOTA 7B model.
Metric | Average | Arc Challenge | Hella Swag | MMLU | Truthful Q&A | Winogrande | GSM8k |
---|---|---|---|---|---|---|---|
Score | 71.86 | 69.11 | 86.53 | 65.02 | 59.24 | 81.37 | 69.90 |
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_
.
Citations
@misc{unacybertron7b,
title={Cybertron: Uniform Neural Alignment},
author={Xavier Murias},
year={2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16}},
}
This model card was created with care by Ryan Witzman.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 71.81 |
AI2 Reasoning Challenge (25-Shot) | 69.11 |
HellaSwag (10-Shot) | 86.73 |
MMLU (5-Shot) | 64.94 |
TruthfulQA (0-shot) | 58.71 |
Winogrande (5-shot) | 81.45 |
GSM8k (5-shot) | 69.90 |
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Base model
Q-bert/MetaMath-Cybertron-StarlingDataset used to train rwitz/go-bruins
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.110
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.730
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.940
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard58.710
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.450
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.900