Instructions to use aeonium/Aeonium-v1.1-Base-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aeonium/Aeonium-v1.1-Base-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aeonium/Aeonium-v1.1-Base-4B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aeonium/Aeonium-v1.1-Base-4B")
model = AutoModelForCausalLM.from_pretrained("aeonium/Aeonium-v1.1-Base-4B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aeonium/Aeonium-v1.1-Base-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aeonium/Aeonium-v1.1-Base-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aeonium/Aeonium-v1.1-Base-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aeonium/Aeonium-v1.1-Base-4B

SGLang

How to use aeonium/Aeonium-v1.1-Base-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aeonium/Aeonium-v1.1-Base-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aeonium/Aeonium-v1.1-Base-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aeonium/Aeonium-v1.1-Base-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aeonium/Aeonium-v1.1-Base-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aeonium/Aeonium-v1.1-Base-4B with Docker Model Runner:
```
docker model run hf.co/aeonium/Aeonium-v1.1-Base-4B
```

Aeoinum v1.1 Base 4B

A state-of-the-art language model for Russian language processing. This checkpoint contains a preliminary version of the model with 4 billion parameters.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("aeonium/Aeonium-v1.1-Base-4B")
model = AutoModelForCausalLM.from_pretrained("aeonium/Aeonium-v1.1-Base-4B").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 on a TPU v4-256 node.

Content Warning

Aeonium v1.1 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.

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Safetensors

Model size

4B params

Tensor type

F32

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