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license: other |
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language: |
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- en |
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**UPDATE:** There's a Llama 2 sequel now! [Check it out here!](https://huggingface.co/Gryphe/MythoLogic-L2-13b) |
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An experiment with gradient merges using [the following script](https://github.com/TehVenomm/LM_Transformers_BlockMerge), with [Chronos](https://huggingface.co/elinas/chronos-13b) as its primary model, augmented by [Hermes](https://huggingface.co/NousResearch/Nous-Hermes-13b) and [Wizard-Vicuna Uncensored](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-HF). |
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Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoLogic-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoLogic-13B-GPTQ) (You're the best!) |
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## Model details |
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Chronos is a wonderfully verbose model, though it definitely seems to lack in the logic department. Hermes and WizardLM have been merged gradually, primarily in the higher layers (10+) in an attempt to rectify some of this behaviour. |
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The main objective was to create an all-round model with improved story generation and roleplaying capabilities. |
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Below is an illustration to showcase a rough approximation of the gradients I used to create MythoLogic: |
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![](approximation.png) |
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## Prompt Format |
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This model primarily uses Alpaca formatting, so for optimal model performance, use: |
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``` |
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<System prompt/Character Card> |
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### Instruction: |
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Your instruction or question here. |
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For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only. |
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### Response: |
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``` |
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license: other |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoLogic-13b) |
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| Metric | Value | |
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| Avg. | 47.23 | |
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| ARC (25-shot) | 58.45 | |
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| HellaSwag (10-shot) | 81.56 | |
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| MMLU (5-shot) | 49.36 | |
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| TruthfulQA (0-shot) | 49.47 | |
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| Winogrande (5-shot) | 75.61 | |
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| GSM8K (5-shot) | 8.64 | |
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| DROP (3-shot) | 7.53 | |
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