Instructions to use Maxilicious20/Aether-2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maxilicious20/Aether-2.2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "Maxilicious20/Aether-2.2")

Transformers

How to use Maxilicious20/Aether-2.2 with Transformers:

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

pipe = pipeline("text-generation", model="Maxilicious20/Aether-2.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Maxilicious20/Aether-2.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Maxilicious20/Aether-2.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maxilicious20/Aether-2.2"
# 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-2.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.2

SGLang

How to use Maxilicious20/Aether-2.2 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-2.2" \
    --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-2.2",
		"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-2.2" \
        --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-2.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Maxilicious20/Aether-2.2 with Docker Model Runner:
```
docker model run hf.co/Maxilicious20/Aether-2.2
```

Aether 2.2

Aether 2.2 is a lightweight, fine-tuned language model based on Qwen2.5-1.5B-Instruct. It was optimized using PEFT (LoRA) to deliver improved response quality, instruction following, and conversational fluency in both German and English while maintaining minimal VRAM usage.

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/Qwen2.5-1.5B-Instruct

Uses

Direct Use

This model is designed as an intelligent assistant for text generation, conversational chat, and general reasoning tasks. Thanks to its lightweight LoRA adapter format, it can be run locally with minimal VRAM requirements.

How to Get Started with the Model

Use the following Python code with transformers and peft to load Aether 2.2 directly:

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

base_model_id = "Qwen/Qwen2.5-1.5B-Instruct"
adapter_id = "Maxilicious20/Aether-2.2"

# 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 2.2 LoRA Adapter
model = PeftModel.from_pretrained(base_model, adapter_id)

# Example Prompt
messages = [
    {"role": "system", "content": "You are Aether, a helpful AI assistant."},
    {"role": "user", "content": "Hello! Who are you and what can you do?"}
]

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))

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Model tree for Maxilicious20/Aether-2.2

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Qwen/Qwen2.5-1.5B

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Qwen/Qwen2.5-1.5B-Instruct

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