README.md

metadata

license: cc-by-nc-4.0
library_name: transformers
tags:
  - llama-3
model-index:
  - name: badger-l3-instruct-32k
    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: 63.65
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/badger-l3-instruct-32k
          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: 81.4
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/badger-l3-instruct-32k
          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: 67.13
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/badger-l3-instruct-32k
          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: 55.02
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/badger-l3-instruct-32k
          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=maldv/badger-l3-instruct-32k
          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: 72.4
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/badger-l3-instruct-32k
          name: Open LLM Leaderboard

updated with fixed tokenizer config

Badger/δ Llama 3 Instruct 32k

I haven't been releasing my base merges so far, but this one seems worthy.

Badger is a recursive maximally disjoint pairwise normalized fourier interpolation of the following models:

models = [
 'Einstein-v6.1-Llama3-8B',
 'L3-TheSpice-8b-v0.8.3',
 'dolphin-2.9-llama3-8b',
 'Configurable-Hermes-2-Pro-Llama-3-8B',
 'MAmmoTH2-8B-Plus',
 'Pantheon-RP-1.0-8b-Llama-3',
 'Tiamat-8b-1.2-Llama-3-DPO',
 'Buzz-8b-Large-v0.5',
 'Kei_Llama3_8B',
 'Llama-3-Lumimaid-8B-v0.1',
 'llama-3-cat-8b-instruct-pytorch',
 'Llama-3SOME-8B-v1',
 'Roleplay-Llama-3-8B',
 'Llama-3-LewdPlay-8B-evo',
 'opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5',
 'meta-llama-3-8b-instruct-hf-ortho-baukit-5fail-3000total-bf16',
 'Poppy_Porpoise-0.72-L3-8B',
 'Llama-3-8B-Instruct-norefusal',
 'Meta-Llama-3-8B-Instruct-DPO',
 'badger',
 'Llama-3-Refueled',
 'Llama-3-8B-Instruct-DPO-v0.4',
 'Llama-3-8B-Instruct-Gradient-1048k',
 'Mahou-1.0-llama3-8B',
 'Llama-3-SauerkrautLM-8b-Instruct',
 'Llama-3-Soliloquy-8B-v2'
]

I have included the notebook code I used to generate the model, for any that are curious. I have adjusted the config for rope scale 4, and 16k-32k context both seem coherent.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.49
AI2 Reasoning Challenge (25-Shot)	63.65
HellaSwag (10-Shot)	81.40
MMLU (5-Shot)	67.13
TruthfulQA (0-shot)	55.02
Winogrande (5-shot)	77.35
GSM8k (5-shot)	72.40