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

Libraries

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

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

pipe = pipeline("text-generation", model="FINAL-Bench/Aether-7B-5Attn", trust_remote_code=True)
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", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Aether-7B-5Attn / eval /lmeval_run.py

SeaWolf-AI

sanitize: internal paths/ids/comments

21574a6 verified 15 days ago

Raw

History Blame Contribute Delete

2.92 kB

	# -- coding: utf-8 --
	import os
	os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("GPU", "2")
	os.environ.setdefault("HF_DATASETS_TRUST_REMOTE_CODE", "1")
	import sys, json, importlib.util, torch

	MODEL = os.environ.get("MODEL", "/workspace/aether-annealed")
	MERGE_ADAPTER = os.environ.get("MERGE_ADAPTER", "")
	ADAPTER = os.environ.get("ADAPTER", "")
	TAG = os.environ.get("TAG", "annealed")
	LIMIT = os.environ.get("LIMIT", "")
	LIMIT = int(LIMIT) if LIMIT else None
	TASKS = os.environ.get("TASKS", "arc_easy,arc_challenge,boolq,hellaswag,openbookqa,piqa,sciq,winogrande").split(",")

	sys.path.insert(0, MODEL)
	from transformers import AutoTokenizer
	tok = AutoTokenizer.from_pretrained(MODEL)
	spec = importlib.util.spec_from_file_location("mini_config", MODEL + "/mini_config.py")
	mc = importlib.util.module_from_spec(spec); spec.loader.exec_module(mc)
	cfg = mc.mini_cfg
	cfg.use_cache = False
	cfg.output_router_logits = False
	from aether_pkg.modeling_aether_v2_7way import AETHERV27wayForCausalLM
	from safetensors.torch import load_file
	print("[load]", MODEL, flush=True)
	model = AETHERV27wayForCausalLM(cfg).to("cuda", dtype=torch.bfloat16)
	model.load_state_dict(load_file(MODEL + "/model.safetensors"), strict=False)
	if MERGE_ADAPTER:
	from peft import PeftModel
	model = PeftModel.from_pretrained(model, MERGE_ADAPTER).merge_and_unload(); print("[merged]", MERGE_ADAPTER, flush=True)
	if ADAPTER:
	from peft import PeftModel
	model = PeftModel.from_pretrained(model, ADAPTER); print("[adapter]", ADAPTER, flush=True)
	model.eval()

	import lm_eval
	from lm_eval.models.huggingface import HFLM
	# batch_size=1 on purpose: the NSA branches ignore attention_mask, so any left-padding
	# in a batch would let queries attend to pad tokens and silently corrupt the scores.
	lm = HFLM(pretrained=model, tokenizer=tok, batch_size=1, max_length=2048, backend="causal")
	print("[lm_eval] tasks=%s limit=%s" % (TASKS, LIMIT), flush=True)
	res = lm_eval.simple_evaluate(model=lm, tasks=TASKS, num_fewshot=0, limit=LIMIT,
	bootstrap_iters=1000, verbosity="ERROR")
	out = {}
	print("\n=== AETHER-7B-5Attn (%s) — lm-eval-harness 0-shot ===" % TAG, flush=True)
	print("%-18s %8s %8s %8s %8s" % ("TASK", "acc", "±", "acc_norm", "±"), flush=True)
	print("-" * 60, flush=True)
	for t in TASKS:
	r = res["results"].get(t, {})
	a = r.get("acc,none"); ae = r.get("acc_stderr,none")
	n = r.get("acc_norm,none"); ne = r.get("acc_norm_stderr,none")
	out[t] = {"acc": a, "acc_stderr": ae, "acc_norm": n, "acc_norm_stderr": ne}
	f = lambda v: ("%6.1f" % (100 * v)) if isinstance(v, float) else " -"
	print("%-18s %8s %8s %8s %8s" % (t, f(a), f(ae), f(n), f(ne)), flush=True)
	print("-" * 60, flush=True)
	json.dump(out, open("/workspace/lmeval_%s.json" % TAG, "w"), indent=2, ensure_ascii=False)
	print("[saved] /workspace/lmeval_%s.json" % TAG, flush=True)
	print("LMEVAL_DONE", flush=True)