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---
language:
- en
license: cc-by-nc-4.0
base_model: Q-bert/MetaMath-Cybertron-Starling
datasets:
- Intel/orca_dpo_pairs
pipeline_tag: text-generation
model-index:
- name: go-bruins
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.11
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.73
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.94
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 58.71
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins
name: Open LLM Leaderboard
---
![image/png](https://cdn-uploads.huggingface.co/production/uploads/63a259d0f30c46422789d38d/vO3iATjO8ulfcakTltE4k.png)
# Go Bruins - A Fine-tuned Language Model
Join my AI Discord: [rwitz](https://discord.gg/qbqjBEfkGw)
## 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_`.
---
## 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__go-bruins)
| 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|
|