Instructions to use FINAL-Bench/Aether-7B-5Attn-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Aether-7B-5Attn-it with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Aether-7B-5Attn-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Aether-7B-5Attn-it", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FINAL-Bench/Aether-7B-5Attn-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Aether-7B-5Attn-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Aether-7B-5Attn-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Aether-7B-5Attn-it

SGLang

How to use FINAL-Bench/Aether-7B-5Attn-it 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 "FINAL-Bench/Aether-7B-5Attn-it" \
    --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": "FINAL-Bench/Aether-7B-5Attn-it",
		"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 "FINAL-Bench/Aether-7B-5Attn-it" \
        --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": "FINAL-Bench/Aether-7B-5Attn-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Aether-7B-5Attn-it with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Aether-7B-5Attn-it
```

Aether-7B-5Attn-it

Aether family —

📚 Part of the Aether Foundation Model collection — base, instruction-tuned, and checkpoints in one place.

🧩 Intermediate checkpoints (110k · 115k · 162k) are released as a dataset: Aether-7B-5Attn-checkpoints. Instruction-tuned (SFT) version of the fully-open Aether-7B-5Attn base model. Post-trained for multiple-choice / benchmark-style answering. 6.59B MoE (~2.98B active), 49 layers on a 7×7 Latin square. Apache-2.0.

Learning rate chosen by held-out accuracy, not loss

Full-parameter SFT was run at three learning rates (2e-6 / 6e-6 / 2e-5). The final checkpoint was selected by held-out benchmark accuracy, not training loss — for small models a high LR lowers training loss while degrading real capability, so loss is the wrong selector.

LR	K-AI 4 avg	Note
2e-6	29.7%	Under-fit (weak on some subjects)
6e-6 (selected)	34.9%	Best & balanced, no degradation
2e-5	32.3%	One subject collapsed (format-degradation sign)

Held-out results (selected checkpoint, 6e-6)

GPQA-Diamond (198): 25.3%
K-AI 4 average (195): 34.9%
- musr_ko 26.5% · com2_main_ko 50.0% · click 32.0% · kommlu_pro 30.4%
vs base (before SFT): base 26.7% → 34.9% (+8.2pp) — same held-out set, same harness.

All evaluations use held-out sets not seen in training. SFT was performed on MMLU-auxiliary (multiple-choice format), which does not overlap with the evaluation subjects (GPQA / K-AI).

Pretraining data mix (inherited from base)

Domain	Share
Math (finemath + open-web-math)	37.8%
Korean (webtext + synth)	21.6%
English web & synthetic (fineweb-edu + cosmopedia)	21.6%
Code (opc)	13.5%
phase15 pre-blend	5.4%

This is the base pretraining mix. This model's post-training (SFT) data is MMLU-auxiliary.

Architecture (inherited from base)

Identical to Aether-7B-5Attn base: 49 layers placed on a 7×7 Latin square with heterogeneous attention (7 labels / 5 distinct mechanisms) and a 25-expert MoE (top-7 + 1 shared). Full structural detail and the diagram are in the base card, §3.2: FINAL-Bench/Aether-7B-5Attn.

Open-source fully-open LLMs — 6-country comparison

Relative to the base Aether. Among six sovereign fully-open models, VIDRAFT is the only single AI startup, and Aether has the most attention types (5) in a Latin-square layout.

Usage

Uses the custom aether_pkg/ architecture (trust_remote_code=True). Follow the loading instructions in the base model card.

⚠️ Use batch_size=1. Some attention branches (NSA family) do not consume a padding mask, so batching padded sequences can silently corrupt results. Run inference one sequence at a time.