README.md

---
language:
- ru
license: apache-2.0
pipeline_tag: text-generation
tags:
- aeonium
inference:
  parameters:
    temperature: 0.8
---

# Aeoinum v1 BaseWeb 1B
A state-of-the-art language model for Russian language processing. This checkpoint contains a preliminary version of the model with 1.6 billion parameters. Trained only on web pages.

## Models
|          Name         | N of parameters | Context window |
|:---------------------:|:-----------------:|:--------------:|
| [Aeonium-v1-Base-4B](https://huggingface.co/aeonium/Aeonium-v1-Base-4B)  | 4.04B            | 4K             |
| Aeonium-v1-Chat-4B     | 4.04B             | 4K             |
| **Aeonium-v1-Base-1B**     | 1.6B             | 4K             |
| Aeonium-v1-Chat-1B     | 1.6B             | 4K             |


## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("aeonium/Aeonium-v1-Base-1B")
model = AutoModelForCausalLM.from_pretrained("aeonium/Aeonium-v1-Base-1B").cuda()

input_ids = tokenizer("Искусственный интеллект - это", return_tensors='pt').to(model.device)["input_ids"]
output = model.generate(input_ids, max_new_tokens=48, do_sample=True, temperature=0.7)
print(tokenizer.decode(output[0]))
```

## Dataset Detail
The dataset for pre-training is collected from public data, most of which are web pages in Russian.

## Training Detail
The training is performed thanks to a grant from [TPU Research Cloud](https://sites.research.google/trc/about/) on a TPU v4-256 node.

## Content Warning
Aeonium v1 is a large language model trained on a broad dataset from the internet. As such, it may generate text that contains biases, offensive language, or other disapproving content. The model outputs should not be considered factual or representative of any individual's beliefs or identity. Users should exercise caution and apply careful filtering when using Aeonium's generated text, especially for sensitive or high-stakes applications. The developers do not condone generating harmful, biased, or unethical content.

## Copyright
The model is released under the Apache 2.0 license.