README.md

metadata

library_name: transformers
tags:
  - merge
  - llama-3.1
  - roleplay
  - function calling
base_model:
  - unsloth/Meta-Llama-3.1-8B-Instruct
  - REILX/Llama-3-8B-Instruct-750Mb-lora
datasets:
  - databricks/databricks-dolly-15k
  - microsoft/orca-math-word-problems-200k
  - LooksJuicy/ruozhiba
base_model_relation: merge
model-index:
  - name: KRONOS-8B-V1-P3
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: wis-k/instruction-following-eval
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 71.37
            name: averaged accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FKRONOS-8B-V1-P3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: SaylorTwift/bbh
          split: test
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 30.27
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FKRONOS-8B-V1-P3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: lighteval/MATH-Hard
          split: test
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 18.35
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FKRONOS-8B-V1-P3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 1.34
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FKRONOS-8B-V1-P3
          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: 5.96
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FKRONOS-8B-V1-P3
          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: 26.72
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FKRONOS-8B-V1-P3
          name: Open LLM Leaderboard

KRONOS V1 P3

This is a merge of Meta Llama 3.1 Instruct and REILIX's "750MB" LORA, created using llm-tools.

The primary purpose of this model is to be merged into other models in the same family using the TIES merge method.

Creating quants for this is entirely unnecessary.

Merge Details

Configuration

The following Bash command was used to produce this model:

python /llm-tools/merge-lora.py -m unsloth/Meta-Llama-3.1-8B-Instruct -l REILX/Llama-3-8B-Instruct-750Mb-lora

Open LLM Leaderboard Evaluation Results

Detailed results can be found here! Summarized results can be found here!

Metric	Value (%)
Average	25.67
IFEval (0-Shot)	71.37
BBH (3-Shot)	30.27
MATH Lvl 5 (4-Shot)	18.35
GPQA (0-shot)	1.34
MuSR (0-shot)	5.96
MMLU-PRO (5-shot)	26.72