README.md · bartowski/Gemma-2-Ataraxy-v2-9B-exl2 at main

metadata

library_name: transformers
tags:
  - mergekit
  - merge
base_model:
  - ifable/gemma-2-Ifable-9B
  - jsgreenawalt/gemma-2-9B-it-advanced-v2.1
model-index:
  - name: Gemma-2-Ataraxy-v2-9B
    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: 21.36
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
          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: 39.8
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
          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: 0.83
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
          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: 12.3
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
          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: 4.88
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
          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: 35.79
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
          name: Open LLM Leaderboard
quantized_by: bartowski
pipeline_tag: text-generation

Exllama v2 Quantizations of Gemma-2-Ataraxy-v2-9B

Using turboderp's ExLlamaV2 v0.2.2 for quantization.

The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)

Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.

Original model: https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v2-9B

Prompt format

<bos>{system_prompt}<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
<end_of_turn>

Available sizes

Branch	Bits	lm_head bits	VRAM (4k)	VRAM (16k)	VRAM (32k)	Description
8_0	8.0	8.0	11.9 GB	15.9 GB	21.3 GB	Maximum quality that ExLlamaV2 can produce, near unquantized performance.
6_5	6.5	8.0	10.4 GB	14.4 GB	19.8 GB	Very similar to 8.0, good tradeoff of size vs performance, recommended.
5_0	5.0	6.0	8.6 GB	12.6 GB	18.0 GB	Slightly lower quality vs 6.5, but usable on 8GB cards.
4_25	4.25	6.0	7.9 GB	11.9 GB	17.3 GB	GPTQ equivalent bits per weight, slightly higher quality.
3_5	3.5	6.0	7.1 GB	11.1 GB	16.9 GB	Lower quality, only use if you have to.

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Gemma-2-Ataraxy-v2-9B-exl2 Gemma-2-Ataraxy-v2-9B-exl2-6_5

With huggingface hub (credit to TheBloke for instructions):

pip3 install huggingface-hub

To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch:

Linux:

huggingface-cli download bartowski/Gemma-2-Ataraxy-v2-9B-exl2 --revision 6_5 --local-dir Gemma-2-Ataraxy-v2-9B-exl2-6_5

Windows (which apparently doesn't like _ in folders sometimes?):

huggingface-cli download bartowski/Gemma-2-Ataraxy-v2-9B-exl2 --revision 6_5 --local-dir Gemma-2-Ataraxy-v2-9B-exl2-6.5

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski