Maxilicious20/Aether-2.5-Coder

Aether 2.5 Coder

Aether 2.5 Coder is a specialized coding and technical reasoning model built on top of the Qwen2.5-Coder-3B-Instruct base architecture. Fine-tuned using SFT (Supervised Fine-Tuning) with Hugging Face TRL and PEFT (LoRA) on custom datasets, Aether 2.5 Coder combines high-precision code generation, script optimization, and debugging with multilingual instruction following in German and English.

🚀 Looking for GGUF versions? If you want to run Aether 2.5 Coder locally via LM Studio, Ollama, or llama.cpp, check out the pre-quantized GGUF repository: 👉 [Maxilicious20/Aether-2.5-Coder-GGUF](https://huggingface.co/Maxilicious20/Aether-2.5-Coder-GGUF)

Model Details

Model Description

• —Developed by: Maxilicious20
• —Model type: Causal Language Model (LoRA Adapter)
• —Language(s) (NLP): German, English, Programming Languages (Python, JavaScript, C++, Luau, etc.)
• —License: Apache-2.0
• —Finetuned from model: Qwen/Qwen2.5-Coder-3B-Instruct

Uses

Direct Use

Aether 2.5 Coder is tailored for automated code completion, script writing, structural refactoring, debugging, and software architecture planning. It delivers top-tier 3B coding performance while maintaining low VRAM consumption for efficient execution on consumer hardware.

Quantized & GGUF Models

For standalone CPU/GPU local execution without Python/Transformers dependencies, use the quantized GGUF binaries:

• —📦 GGUF Repository: Maxilicious20/Aether-2.5-Coder-3B-GGUF
• —Available Quantizations:
• —aether_coder_f16.gguf (Uncompressed / Full Precision)
• —aether_coder_q8_0.gguf (High Quality / 8-bit)
• —aether_coder_q4_k_m.gguf (Recommended / Balanced Speed & VRAM)

How to Get Started with the Model

Python (Transformers & PEFT)

Use the following Python code to load Aether 2.5 Coder with transformers and peft:

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

base_model_id = "Qwen/Qwen2.5-Coder-3B-Instruct"
adapter_id = "Maxilicious20/Aether-2.5-Coder-3B"

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

# Example Prompt
messages = [
    {"role": "system", "content": "You are Aether 2.5 Coder, an expert AI programming assistant."},
    {"role": "user", "content": "Write a Python script to filter and parse a JSON dataset efficiently."}
]

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))