Instructions to use Maxilicious20/Aether-2.5-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maxilicious20/Aether-2.5-Coder with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "Maxilicious20/Aether-2.5-Coder")

Transformers

How to use Maxilicious20/Aether-2.5-Coder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Maxilicious20/Aether-2.5-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Maxilicious20/Aether-2.5-Coder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Maxilicious20/Aether-2.5-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maxilicious20/Aether-2.5-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maxilicious20/Aether-2.5-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.5-Coder

SGLang

How to use Maxilicious20/Aether-2.5-Coder with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Maxilicious20/Aether-2.5-Coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maxilicious20/Aether-2.5-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Maxilicious20/Aether-2.5-Coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maxilicious20/Aether-2.5-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Maxilicious20/Aether-2.5-Coder with Docker Model Runner:
```
docker model run hf.co/Maxilicious20/Aether-2.5-Coder
```

Aether-2.5-Coder / README.md

Maxilicious20

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	---
	base_model: Qwen/Qwen2.5-Coder-3B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:Qwen/Qwen2.5-Coder-3B-Instruct
	- lora
	- sft
	- transformers
	- trl
	- german
	- english
	- coding
	- code-generation
	- aether
	- gguf
	license: apache-2.0
	language:
	- de
	- en
	---

	# 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](https://huggingface.co/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`:

	```python
	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))