README.md

metadata

library_name: transformers
tags:
  - mergekit
  - merge
  - llama-3.1
  - roleplay
  - function calling
base_model:
  - arcee-ai/Llama-3.1-SuperNova-Lite
  - akjindal53244/Llama-3.1-Storm-8B
  - Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
  - unsloth/Meta-Llama-3.1-8B-Instruct
model-index:
  - name: ZEUS-8B-V2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 80.29
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/ZEUS-8B-V2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 31.61
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/ZEUS-8B-V2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 21.15
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/ZEUS-8B-V2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 6.94
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/ZEUS-8B-V2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 8.24
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/ZEUS-8B-V2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 32.18
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/ZEUS-8B-V2
          name: Open LLM Leaderboard

ZEUS 8B 🌩️ V2

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using unsloth/Meta-Llama-3.1-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

• arcee-ai/Llama-3.1-SuperNova-Lite
• akjindal53244/Llama-3.1-Storm-8B
• Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2

Configuration

The following YAML configuration was used to produce this model:

base_model: unsloth/Meta-Llama-3.1-8B-Instruct
dtype: bfloat16
merge_method: dare_ties
parameters:
  int8_mask: 1.0
slices:
- sources:
  - layer_range: [0, 32]
    model: akjindal53244/Llama-3.1-Storm-8B
    parameters:
      density: 0.8
      weight: 0.25
  - layer_range: [0, 32]
    model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      density: 0.8
      weight: 0.33
  - layer_range: [0, 32]
    model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
    parameters:
      density: 0.8
      weight: 0.42
  - layer_range: [0, 32]
    model: unsloth/Meta-Llama-3.1-8B-Instruct
tokenizer_source: base

Open LLM Leaderboard Evaluation Results

Detailed results can be found here! Based on the listed rankings as of 4/12/24, is the top-rank 8B model.

Metric	Value
Avg.	30.07
IFEval (0-Shot)	80.29
BBH (3-Shot)	31.61
MATH Lvl 5 (4-Shot)	21.15
GPQA (0-shot)	6.94
MuSR (0-shot)	8.24
MMLU-PRO (5-shot)	32.18

Inference Settings

Personal recommendations are to use a i1-Q4_K_M quant with these settings:

num_ctx = 4096
repeat_penalty = 1.2
temperature = 0.85
tfs_z = 1.4
top_k = 0 # Change to 40+ if you're roleplaying
top_p = 1 # Change to 0.9 if top_k > 0

Other recommendations can be found on this paper on mobile LLMs, this paper on balancing model parameters, and this Reddit post about tweaking Llama 3.1 parameters.