measurement.json
Browse files- README.md +174 -0
- measurement.json +0 -0
README.md
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1 |
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---
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license: apache-2.0
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base_model:
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- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
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- Locutusque/Hyperion-1.5-Mistral-7B
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- ibm/merlinite-7b
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library_name: transformers
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tags:
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- mergekit
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- merge
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- code
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model-index:
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- name: Magic-Dolphin-7b
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 65.78
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 85.61
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 64.64
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 58.01
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 79.64
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 51.18
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b
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name: Open LLM Leaderboard
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quantized_by: bartowski
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pipeline_tag: text-generation
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---
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## Exllama v2 Quantizations of Magic-Dolphin-7b
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Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization.
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<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
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Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
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Original model: https://huggingface.co/InferenceIllusionist/Magic-Dolphin-7b
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| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
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| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
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| [8_0](https://huggingface.co/bartowski/Magic-Dolphin-7b-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
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| [6_5](https://huggingface.co/bartowski/Magic-Dolphin-7b-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
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| [5_0](https://huggingface.co/bartowski/Magic-Dolphin-7b-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
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| [4_25](https://huggingface.co/bartowski/Magic-Dolphin-7b-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
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| [3_5](https://huggingface.co/bartowski/Magic-Dolphin-7b-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
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## Download instructions
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With git:
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```shell
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git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Magic-Dolphin-7b-exl2 Magic-Dolphin-7b-exl2-6_5
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```
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With huggingface hub (credit to TheBloke for instructions):
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```shell
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pip3 install huggingface-hub
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```
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To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Magic-Dolphin-7b-exl2`:
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```shell
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mkdir Magic-Dolphin-7b-exl2
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huggingface-cli download bartowski/Magic-Dolphin-7b-exl2 --local-dir Magic-Dolphin-7b-exl2 --local-dir-use-symlinks False
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```
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To download from a different branch, add the `--revision` parameter:
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Linux:
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```shell
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mkdir Magic-Dolphin-7b-exl2-6_5
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huggingface-cli download bartowski/Magic-Dolphin-7b-exl2 --revision 6_5 --local-dir Magic-Dolphin-7b-exl2-6_5 --local-dir-use-symlinks False
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```
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Windows (which apparently doesn't like _ in folders sometimes?):
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```shell
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mkdir Magic-Dolphin-7b-exl2-6.5
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huggingface-cli download bartowski/Magic-Dolphin-7b-exl2 --revision 6_5 --local-dir Magic-Dolphin-7b-exl2-6.5 --local-dir-use-symlinks False
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```
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Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
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measurement.json
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