Maxilicious20 / Aether-2.3

Aether 2.3

Aether 2.3 represents a major milestone in the Aether model series, scaling up to the Qwen2.5-3B-Instruct base architecture. Trained with SFT (Supervised Fine-Tuning) via Hugging Face TRL and PEFT (LoRA) on a custom 3 GB dataset using local NVIDIA RTX GPU acceleration, Aether 2.3 offers significantly higher intelligence, broader contextual understanding, and superior multilingual responses in German and English.

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/Qwen2.5-3B-Instruct

Uses

Direct Use

Aether 2.3 is designed for high-capability conversational AI, complex instruction following, creative text generation, and technical reasoning. Thanks to its LoRA adapter implementation, it delivers flagship 3B-class performance while remaining light enough to run efficiently on local hardware.

How to Get Started with the Model

Use the following Python code to load Aether 2.3 with transformers and peft :

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

base_model_id = "Qwen/Qwen2.5-3B-Instruct"
adapter_id = "Maxilicious20/Aether-2.3"

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

# Example Prompt
messages = [
    {"role": "system", "content": "You are Aether 2.3, an advanced AI assistant."},
    {"role": "user", "content": "Hello! What improvements do you bring as a 3B model?"}
]

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))