Instructions to use FINAL-Bench/Aether-6B-11Attn-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Aether-6B-11Attn-base with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Aether-6B-11Attn-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Aether-6B-11Attn-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FINAL-Bench/Aether-6B-11Attn-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Aether-6B-11Attn-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Aether-6B-11Attn-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Aether-6B-11Attn-base

SGLang

How to use FINAL-Bench/Aether-6B-11Attn-base 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-6B-11Attn-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Aether-6B-11Attn-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-6B-11Attn-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Aether-6B-11Attn-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FINAL-Bench/Aether-6B-11Attn-base with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Aether-6B-11Attn-base
```

Aether-6B-11Attn-base / config.json

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remove oheng/ohaeng references — neutral naming

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	{
	"attention_dropout": 0.0,
	"compress_block_size": 32,
	"deltanet_chunk_size": 128,
	"deltanet_d_state": 64,
	"diff_lambda_init": 0.8,
	"diff_lambda_layer_decay": 0.6,
	"expert_intermediate_size": 512,
	"gdn_chunk_size": 64,
	"gdn_d_conv": 4,
	"gdn_expand": 1,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 1024,
	"hybrid_groupnorm_groups": 8,
	"hyena_dropout": 0.0,
	"hyena_filter_order": 64,
	"initializer_range": 0.02,
	"intermediate_size": 512,
	"linear_d_conv": 4,
	"linear_d_state": 64,
	"linear_expand": 1,
	"mamba2_chunk_size": 256,
	"mamba2_d_conv": 4,
	"mamba2_d_state": 128,
	"mamba2_expand": 2,
	"mamba2_headdim": 64,
	"max_position_embeddings": 8192,
	"mla_kv_lora_rank": 256,
	"mla_q_lora_rank": 768,
	"mla_qk_nope_head_dim": 64,
	"mla_qk_rope_head_dim": 64,
	"mla_v_head_dim": 128,
	"model_type": "aether_v2_11attn",
	"nsa_compress_block_size": 32,
	"nsa_select_top_k": 16,
	"nsa_sliding_window": 512,
	"num_attention_heads": 8,
	"num_experts": 25,
	"num_experts_per_tok": 7,
	"num_hidden_layers": 121,
	"num_key_value_heads": 2,
	"output_router_logits": false,
	"pad_token_id": 151643,
	"rezero_alpha_init": 0.0,
	"rms_norm_eps": 1e-06,
	"rope_theta": 1000000.0,
	"router_aux_loss_coef": 0.01,
	"sliding_window_size": 1024,
	"tie_word_embeddings": false,
	"transformers_version": "5.3.0",
	"use_cache": false,
	"use_deepnet_init": true,
	"use_shared_expert": true,
	"vocab_size": 151936,
	"z_loss_coef": 0.001,
	"auto_map": {
	"AutoConfig": "configuration_aether_v2_11attn.AETHERV211AttnConfig",
	"AutoModelForCausalLM": "modeling_aether_v2_11way.AETHERV211AttnForCausalLM",
	"AutoModel": "modeling_aether_v2_11way.AETHERV211AttnModel"
	},
	"architectures": [
	"AETHERV211AttnForCausalLM"
	],
	"cyclic_routing": true,
	"num_cyclic_phases": 5
	}