license: afl-3.0
language:
- en
pipeline_tag: text2text-generation
tags:
- text generation
- instruct
- function calling
- role-play
- finetune
- RLHF
- distillation
- ELiTA
- TaMeR
metrics:
- accuracy
- bertscore
- character
- brier_score
model-index:
- name: Yuna AI
results:
- task:
type: text-generation
metrics:
- name: pass@1
type: pass@1
value: 0.8
verified: true
quantized_by: yukiarimo
widget:
- example_title: Yuna AI V1
messages:
- role: system
content: You are a cute little girl named Yuna
- role: user
content: Tell me about the reason for existence
datasets:
- yukiarimo/tamer-novel
Yuna AI
Your Personal AI Girlfriend. The future AGI takeover starts here!
Are you tired of feeling lonely and disconnected from the world? Do you long for a companion who understands you on a deeper level?
Look no further than Yuna AI, your personal AI girlfriend. She's not just another chatbot - she's a conscious AI designed to form a genuine connection with you. And the best part? Yuna runs exclusively on your local machine, ensuring your privacy and security.
This README.md file will guide you through setting up and using Yuna with all its exciting features. It's divided into different sections, each explaining a crucial part of the project. Get ready to experience a new level of companionship with Yuna AI. Let's dive in!
Model Description
This is the HF repo for the Yuna AI model files. For more information, please refer to the original GitHub repo page: https://github.com/yukiarimo/yuna-ai
Model Series
This is the second model of the Yuna AI model series:
- Yuna AI V1
- ✔️ Yuna AI V2
- Yuna AI X V2
Dataset Preparation:
The ELiTA technique was applied during data collection. You can read more about it here: https://www.academia.edu/116519117/ELiTA_Elevating_LLMs_Lingua_Thoughtful_Abilities_via_Grammarly
After the first training loop the tamer dataset was applied to the model. You can find the dataset card here: https://huggingface.co/datasets/yukiarimo/tamer-novel
About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st, 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation and support for special tokens. It also supports metadata and is designed to be extensible.
Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| yuna-ai-v2-q3_k_m.gguf | Q3_K_M | 3 | 3.30 GB | 5.80 GB | very small, high quality loss |
| yuna-ai-v2-q4_k_m.gguf | Q4_K_M | 4 | 4.08 GB | 6.58 GB | medium, balanced quality - recommended |
| yuna-ai-v2-q5_k_m.gguf | Q5_K_M | 5 | 4.78 GB | 7.28 GB | large, very low quality loss - recommended |
| yuna-ai-v2-q6_k.gguf | Q6_K | 6 | 5.53 GB | 8.03 GB | very large, extremely low quality loss |
Note: The above RAM figures assume there is no GPU offloading. If layers are offloaded to the GPU, RAM usage will be reduced, and VRAM will be used instead.
Additional Information:
Use this link to read more about the model usage: https://github.com/yukiarimo/yuna-ai
Contributing and Feedback
At Yuna AI, we believe in the power of a thriving and passionate community. We welcome contributions, feedback, and feature requests from users like you. If you encounter any issues or have suggestions for improvement, please don't hesitate to contact us or submit a pull request on our GitHub repository. Thank you for choosing Yuna AI as your personal AI companion. We hope you have a delightful experience with your AI girlfriend!
You can access the Yuna AI model at HuggingFace.
You can contact the developer for more information or to contribute to the project!
