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