boboliu/Qwen3-Embedding-0.6B-W4A16-G128

🤗 Hugging Face sourcefeature-extractionapache-2.0599M params2.1 GBsafetensors✓ 2 checksumsupdated today

Submit in one command

Run it next to your model folder. It makes the torrent, checks your files against Hugging Face, and submits it. You just start seeding and paste your key from your account. It only reads your files and never changes them. Read the script first if you like.

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo boboliu/Qwen3-Embedding-0.6B-W4A16-G128 ./model-folder

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Qwen3-Embedding-0.6B-W4A16-G128

GPTQ Quantized https://huggingface.co/Qwen/Qwen3-Embedding-0.6B with THUIR/T2Ranking and m-a-p/COIG-CQIA for calibration set.

What's the benefit?

VRAM Usage: 3228M -> 2124M

What's the cost?

~1.69% lost in C-MTEB.

C-MTEB	Param.	Mean(Task)	Mean(Type)	Class.	Clust.	Pair Class.	Rerank.	Retr.	STS
multilingual-e5-large-instruct	0.6B	58.08	58.24	69.80	48.23	64.52	57.45	63.65	45.81
bge-multilingual-gemma2	9B	67.64	75.31	59.30	86.67	68.28	73.73	55.19	-
gte-Qwen2-1.5B-instruct	1.5B	67.12	67.79	72.53	54.61	79.5	68.21	71.86	60.05
gte-Qwen2-7B-instruct	7.6B	71.62	72.19	75.77	66.06	81.16	69.24	75.70	65.20
ritrieve_zh_v1	0.3B	72.71	73.85	76.88	66.5	85.98	72.86	76.97	63.92
Qwen3-Embedding-0.6B	0.6B	66.33	67.45	71.40	68.74	76.42	62.58	71.03	54.52
This Model	0.6B-W4A16	65.21	66.30	71.36	66.12	74.96	62.63	69.10	53.65

How to use it?

pip install compressed-tensors optimum and auto-gptq / gptqmodel, then goto the official usage guide.