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](https://huggingface.co/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:

1. 1.Download MonoAIStudio.zip
2. 2.Extract it
3. 3.Run MonoAIStudio.exe
4. 4.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))