Sentence Similarity
MLX
Safetensors
multilingual
English
Chinese
xlm-roberta
mlx-embeddings
omlx
apple-silicon
m1
bge-m3
embeddings
feature-extraction
retrieval
rag
8-bit precision
Instructions to use YCF-AI/bge-m3-8bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use YCF-AI/bge-m3-8bit-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download YCF-AI/bge-m3-8bit-MLX --local-dir bge-m3-8bit-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download config.json from YCF-AI/bge-m3-8bit-MLX: direct link, hf CLI and curl.
- Browser
- Download file 747 Bytes
-
https://huggingface.co/YCF-AI/bge-m3-8bit-MLX/resolve/main/config.json
- Command line
-
hf download hf://YCF-AI/bge-m3-8bit-MLX/config.json
-
curl -L -o config.json https://huggingface.co/YCF-AI/bge-m3-8bit-MLX/resolve/main/config.json
747 Bytes
| { | |
| "_name_or_path": "", | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 8194, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.33.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002, | |
| "quantization": { | |
| "group_size": 64, | |
| "bits": 8 | |
| } | |
| } |