README.md

---
license: cc-by-nc-4.0
language:
- en
inference: false
tags:
- roleplay
- llama3
- sillytavern
- broken
---

> [!CAUTION]
> # #broken
> 
> **[Use version 0.72 instead.](https://huggingface.co/Lewdiculous/Poppy_Porpoise-0.72-L3-8B-GGUF-IQ-Imatrix)**
> 
> **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.**
> 
> ![JmoAAPf.png](https://iili.io/JmoAAPf.png)

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).

"Isn't Poppy the cutest [Porpoise](https://g.co/kgs/5C2zP3r)?"

> [!IMPORTANT]
> **Quantization process:** <br>
> 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>
> 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> -->
> If you noticed any issues let me know in the discussions.

> [!NOTE]
> **General usage:** <br>
> Use the latest version of **KoboldCpp**. <br>
> Remember that you can also use `--flashattention` on KoboldCpp now even with non-RTX cards for reduced VRAM usage. <br>
> For **8GB VRAM** GPUs, I recommend the **Q4_K_M-imat** quant for up to 12288 context sizes. <br>
> For **12GB VRAM** GPUs, the **Q5_K_M-imat** quant will give you a great size/quality balance. <br>
>
> **Resources:** <br>
> 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.
> 
> **Presets:** <br>
> 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>
<!-- > Check [**discussions such as this one**](https://huggingface.co/Virt-io/SillyTavern-Presets/discussions/5#664d6fb87c563d4d95151baa) for other recommendations and samplers.
-->

> [!TIP]
> **Personal-support:** <br>
> I apologize for disrupting your experience. <br>
> Currently I'm working on moving for a better internet provider. <br>
> If you **want** and you are **able to**... <br>
> You can [**spare some change over here (Ko-fi)**](https://ko-fi.com/Lewdiculous). <br>
>
> **Author-support:** <br>
> You can support the author [**at their own page**](https://huggingface.co/Nitral-AI).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/Boje781GkTdYgORTYGI6r.png)

## **Original model text information:**

**"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.**

# Presets in repo folder:

 * https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets

# If you want to use vision functionality:

 * You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp).
 
# 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).
 
 * You can load the **mmproj** file by using the corresponding section in the interface:

 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/UX6Ubss2EPNAT3SKGMLe0.png)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Nitral-AI__Poppy_Porpoise-0.85-L3-8B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.24|
|AI2 Reasoning Challenge (25-Shot)|63.40|
|HellaSwag (10-Shot)              |82.89|
|MMLU (5-Shot)                    |68.04|
|TruthfulQA (0-shot)              |54.12|
|Winogrande (5-shot)              |77.90|
|GSM8k (5-shot)                   |69.07|