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 / README.md

Maxilicious20

Update README.md

608cb76 verified 1 day ago

preview code

Raw

History Blame Contribute Delete

2.17 kB

	---
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
	- lora
	- sft
	- transformers
	- trl
	- german
	- english
	- aether
	license: apache-2.0
	language:
	- de
	- en
	---

	# 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:

	```python
	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))