miscii-14b-1028 / README.md
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metadata
language:
  - en
  - zh
license: apache-2.0
library_name: transformers
tags:
  - chat
  - conversational
  - custom-research
  - rp
  - roleplay
  - tool-use
base_model:
  - Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
model-index:
  - name: miscii-14b-1028
    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: 82.37
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sthenno-com/miscii-14b-1028
          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: 49.26
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sthenno-com/miscii-14b-1028
          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: 6.34
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sthenno-com/miscii-14b-1028
          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: 14.21
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sthenno-com/miscii-14b-1028
          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: 12
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sthenno-com/miscii-14b-1028
          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: 46.14
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sthenno-com/miscii-14b-1028
          name: Open LLM Leaderboard
datasets:
  - nvidia/HelpSteer2
  - google/Synthetic-Persona-Chat
  - mlabonne/orpo-dpo-mix-40k

miscii-14b-1028

Role-based Instructions

Just parse the following as your system prompt. Note there is NO special-tokens here.

An example system prompt:

system_prompt: str = (
    """<|context_start|>personas<|context_sep|>
<|persona_start|>user<|persona_sep|>
{user_persona}<|persona_end|>
<|persona_start|>assistant<|persona_sep|>
{assistant_persona}<|persona_end|><|context_end|>""".format(
        user_persona="""I am Miscii.
I am the designer of Sthenno.
[Optional: Additional statements]""",
        assistant_persona="""I am Sthenno.
I speak in Chinese.
[Optional: Additional statements]""",
    )
)

Training

See Report for miscii-1020 for more details.


Open LLM Leaderboard Evaluation Results

Detailed results can be found here.

Metric Value
Avg. 35.05
IFEval (0-Shot) 82.37
BBH (3-Shot) 49.26
MATH Lvl 5 (4-Shot) 6.34
GPQA (0-shot) 14.21
MuSR (0-shot) 12.00
MMLU-PRO (5-shot) 46.14

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