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---
license: mit
datasets:
- Intel/orca_dpo_pairs
language:
- en
pipeline_tag: text-generation
---
# 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_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](https://huggingface.co/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:
```python
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](https://huggingface.co/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](https://huggingface.co/datasets/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.* |