Instructions to use Maxilicious20/Aether-2.5-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maxilicious20/Aether-2.5-Pro 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-2.5-Pro")

Transformers

How to use Maxilicious20/Aether-2.5-Pro with Transformers:

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

pipe = pipeline("text-generation", model="Maxilicious20/Aether-2.5-Pro")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Maxilicious20/Aether-2.5-Pro", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Maxilicious20/Aether-2.5-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maxilicious20/Aether-2.5-Pro"
# 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-2.5-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.5-Pro

SGLang

How to use Maxilicious20/Aether-2.5-Pro 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-2.5-Pro" \
    --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-2.5-Pro",
		"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-2.5-Pro" \
        --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-2.5-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Maxilicious20/Aether-2.5-Pro with Docker Model Runner:
```
docker model run hf.co/Maxilicious20/Aether-2.5-Pro
```

Aether-2.5-Pro / README.md

Maxilicious20

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metadata

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
  - aether-2.5
  - gguf
license: apache-2.0
language:
  - de
  - en

Aether 2.5 Pro

Aether 2.5 Pro is the strongest model in the Aether 2.5 series so far.
It is a fine-tuned version of Qwen2.5-3B-Instruct, trained with TRL + PEFT (LoRA) on a higher-quality and more diverse dataset than previous versions.

Compared to the standard Aether 2.5, the Pro version offers:

Better reasoning
Improved instruction following
Stronger multilingual performance (German + English)
Higher overall response quality while staying efficient for local use

🖥️ Want an easy way to run it?
Download MonoAIStudio – our local chat application.
It comes pre-installed with Aether 2.5, Aether 2.5 Pro and Aether 2.5 Coder.
👉 Download MonoAIStudio.zip

🚀 Looking for GGUF versions?
👉 Maxilicious20/Aether-2.5-Pro-GGUF

Model Details

Model Description

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

Uses

Direct Use

Aether 2.5 Pro is designed for:

High-quality conversational AI
Reasoning and problem solving
Instruction following
General text generation
Local deployment on consumer hardware

Easy Local Usage (Recommended)

The easiest way to use this model is with MonoAIStudio:

Download MonoAIStudio.zip
Extract it
Run MonoAIStudio.exe
All Aether 2.5 models are already included

Quantized & GGUF Models

For use with LM Studio, Ollama, llama.cpp, etc.:

📦 GGUF Repository: Maxilicious20/Aether-2.5-Pro-GGUF

How to Use (Python)

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

base_model_id = "Qwen/Qwen2.5-3B-Instruct"
adapter_id = "Maxilicious20/Aether-2.5-Pro"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

model = PeftModel.from_pretrained(base_model, adapter_id)

messages = [
    {"role": "system", "content": "You are Aether 2.5 Pro, a highly capable AI assistant developed by Mono AI Studio."},
    {"role": "user", "content": "Explain the difference between supervised and unsupervised learning in simple terms."}
]

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=512)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))