---

library_name: gguf

tags:

• gguf
• llama.cpp
• lm-studio
• aether
• german
• english
• text-generation

license: apache-2.0

language:

• de
• en

base_model: Qwen/Qwen2.5-3B-Instruct

---

Aether 2.3 - GGUF

Pre-quantized GGUF binaries for Aether 2.3, scaling up to the powerful Qwen2.5-3B-Instruct base architecture.

Trained with SFT (Supervised Fine-Tuning) and PEFT (LoRA) on a custom 3 GB dataset using local NVIDIA RTX GPU acceleration, Aether 2.3 delivers high intelligence, robust conversational capabilities, and exceptional multilingual performance in German and English.

> 🔗 Looking for the Base / LoRA Adapter?

> If you want to use the Hugging Face Transformers PEFT adapter instead, check out the main repository:

> 👉 Maxilicious20/Aether-2.3

---

📦 Available Files & Quantizations

Choose the right file depending on your system's VRAM/RAM and performance needs:

| Filename | Quantization | Quality | Size | Description / Recommendation |

| :--- | :--- | :--- | :--- | :--- |

| aether_2_3_fp16.gguf | FP16 / F16 | Maximum | ~5.75 GB | Uncompressed full precision. Best quality, requires more VRAM. |

| aether_2_3_q8_0.gguf | Q8_0 | Very High | ~3.05 GB | Near-lossless quantization. Excellent balance of precision and speed. |

| aether_2_3_q4_k_m.gguf | Q4_K_M | Balanced | ~1.79 GB | Recommended. Best compromise between speed, size, and minimal quality loss. |

---

🚀 How to Run Locally

1. LM Studio

1. Open LM Studio.
2. Search for Maxilicious20/Aether-2.3-GGUF or paste the repo ID.
3. Download your preferred quantization (e.g., aether_2_3_q4_k_m.gguf).
4. Load the model and start chatting!

2. Ollama / llama.cpp

You can run the GGUF file directly using llama.cpp:

./llama-cli -m aether_2_3_q4_k_m.gguf -p "Hello Aether!" -n 256