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flaglow/
dragonkue-BGE-m3-ko-mlx-fp16

Name: dragonkue-BGE-m3-ko-mlx-fp16
Availability: InStock

safetensors

xlm-roberta

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Conversion

Converted to MLX format for Apple Silicon.

python -m mlx_embeddings.convert --hf-path /Volumes/970EvoPlus/Users/mac_ex/github/mlx_converter/models/dragonkue--BGE-m3-ko --mlx-path /Volumes/970EvoPlus/Users/mac_ex/github/mlx_converter/models/dragonkue-BGE-m3-ko-mlx-fp16

Parameter	Value
Converter	`mlx_embeddings.convert`
Source	`dragonkue/BGE-m3-ko`
Output	`flaglow/dragonkue-BGE-m3-ko-mlx-fp16`
Quantized	`no`
q-mode	`-`
q-bits	`-`
q-group-size	`-`

Benchmark Results

Evaluated on AutoRAG Korean Embedding Benchmark (dataset: ocr_corpus_v3 / qa_v4). Metrics: NDCG · F1 · Recall · Precision · mAP · mRR at top-k retrieval. For these retrieval metrics, higher is better. Bold values mark the best score for each metric at the same Top-k.

Quantization	Model	Top-k	NDCG	F1	Recall	Precision	mAP	mRR
original	`BGE-m3-ko`	top_1	0.6404	0.6404	0.6404	0.6404	0.6404	0.6404
affine-4bit	`dragonkue-BGE-m3-ko-mlx-affine-4bit`	top_1	0.6404	0.6404	0.6404	0.6404	0.6404	0.6404
fp16	`dragonkue-BGE-m3-ko-mlx-fp16`	top_1	0.6667	0.6667	0.6667	0.6667	0.6667	0.6667
original	`BGE-m3-ko`	top_3	0.7409	0.3991	0.7982	0.2661	0.7208	0.7208
affine-4bit	`dragonkue-BGE-m3-ko-mlx-affine-4bit`	top_3	0.7619	0.4211	0.8421	0.2807	0.7339	0.7339
fp16	`dragonkue-BGE-m3-ko-mlx-fp16`	top_3	0.7531	0.4123	0.8246	0.2749	0.7281	0.7281
original	`BGE-m3-ko`	top_5	0.7811	0.2982	0.8947	0.1789	0.7428	0.7428
affine-4bit	`dragonkue-BGE-m3-ko-mlx-affine-4bit`	top_5	0.777	0.2924	0.8772	0.1754	0.7427	0.7427
fp16	`dragonkue-BGE-m3-ko-mlx-fp16`	top_5	0.7642	0.2895	0.8684	0.1737	0.7289	0.7289
original	`BGE-m3-ko`	top_10	0.7918	0.1707	0.9386	0.0939	0.7448	0.7448
affine-4bit	`dragonkue-BGE-m3-ko-mlx-affine-4bit`	top_10	0.792	0.1675	0.9211	0.0921	0.7493	0.7493
fp16	`dragonkue-BGE-m3-ko-mlx-fp16`	top_10	0.7996	0.1707	0.9386	0.0939	0.7547	0.7547

flaglow/dragonkue-BGE-m3-ko-mlx-fp16

Conversion

Benchmark Results

flaglow/
dragonkue-BGE-m3-ko-mlx-fp16