README.md

metadata

language:
  - en
license: apache-2.0
datasets:
  - Intel/orca_dpo_pairs
  - athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
pipeline_tag: text-generation
model-index:
  - name: go-bruins-v2
    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: 69.8
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins-v2
          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: 87.05
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins-v2
          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: 64.75
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins-v2
          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: 59.7
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins-v2
          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-v2
          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: 69.67
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rwitz/go-bruins-v2
          name: Open LLM Leaderboard

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Go Bruins V2 - A Fine-tuned Language Model

Updates

Overview

Go Bruins-V2 is a language model fine-tuned on the rwitz/go-bruins 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: rwitz/go-bruins
• Fine-tuning Method: Direct Preference Optimization (DPO)
• Training Steps: 642
• 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-v2"
inference_pipeline = pipeline('text-generation', model=model_name)

input_text = "Your input text goes here"
output = inference_pipeline(input_text)

print(output)

Not Recommended For:

• Illegal activities
• Harassment
• Professional advice or crisis situations

Training and Evaluation

Trained on a dataset from athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW, Go Bruins V2 has shown promising improvements over its predecessor, Go Bruins.

Evaluations

Metric	Average	Arc Challenge	Hella Swag	MMLU	Truthful Q&A	Winogrande	GSM8k
Score	72.07	69.8	87.05	64.75	59.7	81.45	69.67

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.	72.07
AI2 Reasoning Challenge (25-Shot)	69.80
HellaSwag (10-Shot)	87.05
MMLU (5-Shot)	64.75
TruthfulQA (0-shot)	59.70
Winogrande (5-shot)	81.45
GSM8k (5-shot)	69.67