Gecko-7B-v0.1-DPO / README.md
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metadata
license: apache-2.0
library_name: transformers
datasets:
  - Intel/orca_dpo_pairs
base_model: NeuralNovel/Gecko-7B-v0.1
inference: false
model-index:
  - name: Gecko-7B-v0.1-DPO
    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: 56.74
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1-DPO
          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: 82.38
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1-DPO
          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: 60.42
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1-DPO
          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: 57.42
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1-DPO
          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: 77.35
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1-DPO
          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: 45.03
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1-DPO
          name: Open LLM Leaderboard

Gecko

NeuralNovel/Gecko-7B-v0.1-DPO

Designed to generate instructive and narrative text, with a focus on mathematics & numeracy.

Full-parameter fine-tune (FFT) of Mistral-7B-Instruct-v0.2, with apache-2.0 license.

You may download and use this model for research, training and commercial purposes.

This model is suitable for commercial deployment.

Join our Discord!

Data-set

The model was finetuned using the orca_dpo_pairs dataset

Summary

Fine-tuned with the intention of following all prompt directions, making it more suitable for math questions and problem solving.

Out-of-Scope Use

The model may not perform well in scenarios unrelated to instructive and narrative text generation. Misuse or applications outside its designed scope may result in suboptimal outcomes.

Bias, Risks, and Limitations

This model may not work as intended. As such all users are encouraged to use this model with caution and respect.

This model is for testing and research purposes only, it has reduced levels of alignment and as a result may produce NSFW or harmful content. The user is responsible for their output and must use this model responsibly.

Hardware and Training

Trained on a single 80GB A100 for 2 hours trained using Axolotl

Thank you to h2m for the generous funding.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 63.22
AI2 Reasoning Challenge (25-Shot) 56.74
HellaSwag (10-Shot) 82.38
MMLU (5-Shot) 60.42
TruthfulQA (0-shot) 57.42
Winogrande (5-shot) 77.35
GSM8k (5-shot) 45.03