Instructions to use Maxilicious20/Aether-2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maxilicious20/Aether-2.3 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "Maxilicious20/Aether-2.3")

Transformers

How to use Maxilicious20/Aether-2.3 with Transformers:

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

pipe = pipeline("text-generation", model="Maxilicious20/Aether-2.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Maxilicious20/Aether-2.3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Maxilicious20/Aether-2.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maxilicious20/Aether-2.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maxilicious20/Aether-2.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.3

SGLang

How to use Maxilicious20/Aether-2.3 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 "Maxilicious20/Aether-2.3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maxilicious20/Aether-2.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Maxilicious20/Aether-2.3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maxilicious20/Aether-2.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Maxilicious20/Aether-2.3 with Docker Model Runner:
```
docker model run hf.co/Maxilicious20/Aether-2.3
```

Aether-2.3 / README.md

Maxilicious20

Update README.md

8b79258 verified 2 days ago

preview code

Raw

History Blame Contribute Delete

2.39 kB

	---
	base_model: Qwen/Qwen2.5-3B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:Qwen/Qwen2.5-3B-Instruct
	- lora
	- sft
	- transformers
	- trl
	- german
	- english
	- aether
	license: apache-2.0
	language:
	- de
	- en
	---

	# Aether 2.3

	Aether 2.3 represents a major milestone in the Aether model series, scaling up to the Qwen2.5-3B-Instruct base architecture. Trained with SFT (Supervised Fine-Tuning) via Hugging Face TRL and PEFT (LoRA) on a custom 3 GB dataset using local NVIDIA RTX GPU acceleration, Aether 2.3 offers significantly higher intelligence, broader contextual understanding, and superior multilingual responses in German and English.

	## Model Details

	### Model Description

	- Developed by: Maxilicious20
	- Model type: Causal Language Model (LoRA Adapter)
	- Language(s) (NLP): German, English
	- License: Apache-2.0
	- Finetuned from model: Qwen/Qwen2.5-3B-Instruct

	## Uses

	### Direct Use

	Aether 2.3 is designed for high-capability conversational AI, complex instruction following, creative text generation, and technical reasoning. Thanks to its LoRA adapter implementation, it delivers flagship 3B-class performance while remaining light enough to run efficiently on local hardware.

	### How to Get Started with the Model

	Use the following Python code to load Aether 2.3 with `transformers` and `peft`:

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel

	base_model_id = "Qwen/Qwen2.5-3B-Instruct"
	adapter_id = "Maxilicious20/Aether-2.3"

	# Load Tokenizer and Base Model
	tokenizer = AutoTokenizer.from_pretrained(base_model_id)
	base_model = AutoModelForCausalLM.from_pretrained(
	base_model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)

	# Load Aether 2.3 LoRA Adapter
	model = PeftModel.from_pretrained(base_model, adapter_id)

	# Example Prompt
	messages = [
	{"role": "system", "content": "You are Aether 2.3, an advanced AI assistant."},
	{"role": "user", "content": "Hello! What improvements do you bring as a 3B model?"}
	]

	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	outputs = model.generate(**inputs, max_new_tokens=256)
	print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))