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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
license: apache-2.0
language:
- de
- en
---
# 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`:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_id = "Qwen/Qwen2.5-3B-Instruct"
adapter_id = "Maxilicious20/Aether-2.3"
# 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)) |