Text Generation
Transformers
Safetensors
English
Korean
aether_v2_7way
foundation-model
sovereign-ai
fully-open
open-source
mixture-of-experts
Mixture of Experts
heterogeneous-attention
latin-square
instruct
sft
fine-tuned
korean
vidraft
aether
conversational
custom_code
Instructions to use FINAL-Bench/Aether-7B-5Attn-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Aether-7B-5Attn-it", trust_remote_code=True, device_map="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
Commit History
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remove oheng/오행 references
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meta: declare inherited pretraining datasets (same corpora as the base model)
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fix: restore YAML frontmatter (CRLF double-conversion broke metadata)
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docs: add 'which Aether model should I use?' comparison across the family
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docs: loading now works with AutoModelForCausalLM + trust_remote_code
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fix: make model loadable via AutoModelForCausalLM (add auto_map, flatten module files to repo root, inherit GenerationMixin)
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fix: make model loadable via AutoModelForCausalLM (add auto_map, flatten module files to repo root, inherit GenerationMixin)
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fix: make model loadable via AutoModelForCausalLM (add auto_map, flatten module files to repo root, inherit GenerationMixin)
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fix: make model loadable via AutoModelForCausalLM (add auto_map, flatten module files to repo root, inherit GenerationMixin)
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fix: make model loadable via AutoModelForCausalLM (add auto_map, flatten module files to repo root, inherit GenerationMixin)
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Docs: attention_mask must be dropped at inference (generate()'s auto-mask degenerates output)
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Model card: document the actual loading path, use_cache=False, measured throughput, and quality caveats
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Fix chat_template: match the actual SFT format (instruction + blank line). The previous Qwen-style template was never trained on and produced degenerate output.
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Model card: document Korean SFT v3 update and limitations
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Korean instruction tuning v3 (identity + language matching)
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count wording: attention types, not mechanisms
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sync Aether family cross-links
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sync Aether family cross-links
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link intermediate-checkpoints dataset
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link intermediate-checkpoints dataset
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revert broken 3-part auto_map (transformers only 2-part-splits)
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add search-friendly discovery tags (foundation-model, sovereign-ai, ...)
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add Aether Foundation Model collection link
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English model card (primary), matching base
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preserve Korean card as README_ko.md
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sync it card with base: hero+comparison images, arch pointer, batch_size=1
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card: add measured base->it delta (+8.2pp) and pretraining domain shares
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rename: -Instruct -> -it (Gemma-style)
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Aether-7B-5Attn-Instruct: SFT winner (LR 6e-6, chosen by held-out benchmark)
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initial commit
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