Go Bruins - A Fine-tuned Language Model
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_.
This model card was created with care by Ryan Witzman.
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