---
license: other
license_name: xt-aurora-license
license_link: LICENSE
language:
- en
tags:
- conversational
- chat
- rp
- roleplay
- friend
- slm
- small
- slim
- slender
- general
- creative
co2_eq_emissions:
emissions: 5
source: "ML CO2"
training_type: "fine-tuning"
hardware_used: "1 GTX1060-3GB"
---
![image/png](https://cdn-uploads.huggingface.co/production/uploads/65ca8c3c5495933ab066c33c/-fUpi6P30Lmlx-iasdGaX.png)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/65ca8c3c5495933ab066c33c/ZWbL5O4B5WWIwm0CWAmKU.png)
About this model:
This model, XT_AURORA-OpenBeta-V0.4, is by us, XeTute. The model was finetuned ontop of the previos beta-verion[XT_AURORA-OpenBeta-V0.3-GGUF].
This version[Beta V0.4] achieves better performance in being[or only acting] as a helpful friend, logical thinking[its still pretty "dumb"] and grammar without any adapters.
About XT_AURORA:
XT_AURORA is a series of SLMs[Slender Language Models], which all aim to provide a friendly, human-like conversation.
The serie is limited by its size[about 1.1B Params], but we still try to get the best possible output.
The context-length is 2048 tokens, but it can be upscaled using rope, with the cost being slightly less logic.
About this version[V0.4]:
* High quality output[sometimes outperforms 3B models in HumanEval], as long as the context size is under 2049 Tokens.
* We provide a system prompt[Files and Versions --> chat_template]. The SLM was partly trained using that template, so the output is better if you use the prompt at start.
* AURORA expects the chat template to be Vicuna[{{user}}: {some input}\nAURORA: {some output}\n{{user}}]. The model will only work correctly with this format.
* Recommended temperature is from 0.3 to 0.5.
* Improved chat quality in general chat, roleplaying, etc.
* Math and other factual stuff often spits out false "facts", since the model was trained on in-context-learning. It is able to understand some complex words and use them correctly.
All in one, AURORA's aim is to provide a digital friend, which is also accessible to humans with low-end devices.
Using KoboldCPP, we got the model running[using termux] on a POCO X5 Pro 5G[CPU only, Octa Core].
We saw ~5 Tokens generation per second, ~15 Tokens processing per second.
Please support us:
X:
GitHub:
Subdomain on Neocities:
We wish you a friendly chat with AURORA <3