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RichardErkhov/
rwitz_-_go-bruins-gguf

Name: rwitz_-_go-bruins-gguf
Availability: InStock

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gguf

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go-bruins - GGUF

Model creator: https://huggingface.co/rwitz/
Original model: https://huggingface.co/rwitz/go-bruins/

Name	Quant method	Size
go-bruins.Q2_K.gguf	Q2_K	2.53GB
go-bruins.IQ3_XS.gguf	IQ3_XS	2.81GB
go-bruins.IQ3_S.gguf	IQ3_S	2.96GB
go-bruins.Q3_K_S.gguf	Q3_K_S	2.95GB
go-bruins.IQ3_M.gguf	IQ3_M	3.06GB
go-bruins.Q3_K.gguf	Q3_K	3.28GB
go-bruins.Q3_K_M.gguf	Q3_K_M	3.28GB
go-bruins.Q3_K_L.gguf	Q3_K_L	3.56GB
go-bruins.IQ4_XS.gguf	IQ4_XS	3.67GB
go-bruins.Q4_0.gguf	Q4_0	3.83GB
go-bruins.IQ4_NL.gguf	IQ4_NL	3.87GB
go-bruins.Q4_K_S.gguf	Q4_K_S	3.86GB
go-bruins.Q4_K.gguf	Q4_K	4.07GB
go-bruins.Q4_K_M.gguf	Q4_K_M	4.07GB
go-bruins.Q4_1.gguf	Q4_1	4.24GB
go-bruins.Q5_0.gguf	Q5_0	4.65GB
go-bruins.Q5_K_S.gguf	Q5_K_S	4.65GB
go-bruins.Q5_K.gguf	Q5_K	4.78GB
go-bruins.Q5_K_M.gguf	Q5_K_M	4.78GB
go-bruins.Q5_1.gguf	Q5_1	5.07GB
go-bruins.Q6_K.gguf	Q6_K	5.53GB
go-bruins.Q8_0.gguf	Q8_0	7.17GB

Original model description:

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

Go Bruins - A Fine-tuned Language Model

Join my AI Discord: rwitz

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:

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

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

RichardErkhov/rwitz_-_go-bruins-gguf