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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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inference: false |
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tags: |
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- roleplay |
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- llama3 |
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- sillytavern |
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- broken |
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--- |
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> [!CAUTION] |
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> # #broken |
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> |
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> **[Use version 0.72 instead.](https://huggingface.co/Lewdiculous/Poppy_Porpoise-0.72-L3-8B-GGUF-IQ-Imatrix)** |
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> |
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> **This model is now **deprecated** since the [author has identified significant issues with it](https://huggingface.co/LWDCLS/LLM-Discussions/discussions/12#665d0331325b11b6fe29835f), this is considered #broken and is only still kept here for archiving.** |
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> |
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>  |
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My GGUF-IQ-Imatrix quants for [**Nitral-AI/Poppy_Porpoise-1.0-L3-8B**](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B). |
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"Isn't Poppy the cutest [Porpoise](https://g.co/kgs/5C2zP3r)?" |
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> [!IMPORTANT] |
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> **Quantization process:** <br> |
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> For future reference, these quants have been done after the fixes from [**#6920**](https://github.com/ggerganov/llama.cpp/pull/6920) have been merged. <br> |
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> Since the original model was already an FP16, imatrix data was generated from the FP16-GGUF and the conversions as well. <br> <!-- This was a bit more disk and compute intensive but hopefully avoided any losses during conversion. <br> --> |
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> If you noticed any issues let me know in the discussions. |
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> [!NOTE] |
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> **General usage:** <br> |
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> Use the latest version of **KoboldCpp**. <br> |
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> Remember that you can also use `--flashattention` on KoboldCpp now even with non-RTX cards for reduced VRAM usage. <br> |
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> For **8GB VRAM** GPUs, I recommend the **Q4_K_M-imat** quant for up to 12288 context sizes. <br> |
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> For **12GB VRAM** GPUs, the **Q5_K_M-imat** quant will give you a great size/quality balance. <br> |
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> |
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> **Resources:** <br> |
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> You can find out more about how each quant stacks up against each other and their types [**here**](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [**here**](https://rentry.org/llama-cpp-quants-or-fine-ill-do-it-myself-then-pt-2), respectively. |
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> |
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> **Presets:** <br> |
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> Some compatible SillyTavern presets can be found [**here (New Poppy-1.0 Presets)**](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets) or [**here (Virt's Roleplay Presets)**](https://huggingface.co/Virt-io/SillyTavern-Presets). <br> |
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<!-- > Check [**discussions such as this one**](https://huggingface.co/Virt-io/SillyTavern-Presets/discussions/5#664d6fb87c563d4d95151baa) for other recommendations and samplers. |
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--> |
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> [!TIP] |
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> **Personal-support:** <br> |
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> I apologize for disrupting your experience. <br> |
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> Currently I'm working on moving for a better internet provider. <br> |
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> If you **want** and you are **able to**... <br> |
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> You can [**spare some change over here (Ko-fi)**](https://ko-fi.com/Lewdiculous). <br> |
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> |
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> **Author-support:** <br> |
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> You can support the author [**at their own page**](https://huggingface.co/Nitral-AI). |
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## **Original model text information:** |
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**"Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences.** |
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# Presets in repo folder: |
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* https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets |
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# If you want to use vision functionality: |
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* You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). |
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# To use the multimodal capabilities of this model and use **vision** you need to load the specified **mmproj** file, this can be found here: [Llava-MMProj file](https://huggingface.co/Nitral-AI/Llama-3-Update-2.0-mmproj-model-f16). |
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* You can load the **mmproj** file by using the corresponding section in the interface: |
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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_Nitral-AI__Poppy_Porpoise-0.85-L3-8B) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |69.24| |
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|AI2 Reasoning Challenge (25-Shot)|63.40| |
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|HellaSwag (10-Shot) |82.89| |
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|MMLU (5-Shot) |68.04| |
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|TruthfulQA (0-shot) |54.12| |
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|Winogrande (5-shot) |77.90| |
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|GSM8k (5-shot) |69.07| |
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