Feature Extraction
MLX
Safetensors
qwen3
embeddings
sentence-similarity
quantization
omlx
oq6e
6-bit
Instructions to use TiGa-RCE/Qwen3-Embedding-0.6B-MLX-oQ6e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/Qwen3-Embedding-0.6B-MLX-oQ6e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-0.6B-MLX-oQ6e TiGa-RCE/Qwen3-Embedding-0.6B-MLX-oQ6e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 2,459 Bytes
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"architectures": [
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"bos_token_id": 151643,
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"head_dim": 128,
"hidden_act": "silu",
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"initializer_range": 0.02,
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"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.51.3",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151669,
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