Feature Extraction
MLX
Safetensors
qwen3
embeddings
sentence-similarity
quantization
omlx
oq4e
4-bit precision
Instructions to use TiGa-RCE/Qwen3-Embedding-8B-MLX-oQ4e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/Qwen3-Embedding-8B-MLX-oQ4e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-8B-MLX-oQ4e TiGa-RCE/Qwen3-Embedding-8B-MLX-oQ4e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| { | |
| "enabled": true, | |
| "cache_path": "/Volumes/Models/models/Qwen/.oqe_imatrix/Qwen3-Embedding-8B-MLX-BF16-oQ8e-s128-l512.npz", | |
| "cache_reused": true, | |
| "entry_count": 252, | |
| "calib_dataset": "oqe_code_multilingual", | |
| "collection": { | |
| "dataset": "oqe_code_multilingual", | |
| "requested_samples": 128, | |
| "seq_length": 512, | |
| "adaptive": true, | |
| "adaptive_step_samples": 128, | |
| "adaptive_max_samples": 1024, | |
| "available_samples": 1024, | |
| "micro_batch_size": 28, | |
| "micro_batches": 5, | |
| "batch_plan": { | |
| "micro_batch_size": 28, | |
| "estimated_sample_bytes": 8388608, | |
| "capture_budget_bytes": 235304878, | |
| "system_available_bytes": 23530487808, | |
| "metal_available_bytes": 26796109276, | |
| "live_available_bytes": 23530487808, | |
| "hidden_size": 4096, | |
| "num_experts": 0, | |
| "top_k": 1 | |
| }, | |
| "processed_samples": 128, | |
| "installed_modules": 252, | |
| "capture_module_classes": { | |
| "Linear": 252 | |
| }, | |
| "switch_capture_modules": 0, | |
| "requires_expert_counts": false, | |
| "coverage_sufficient": true, | |
| "collection_sufficient": true, | |
| "coverage": { | |
| "has_expert_counts": false, | |
| "expert_modules": 0, | |
| "total_experts": 0, | |
| "active_experts": 0, | |
| "zero_count_experts": 0, | |
| "active_ratio": 1.0, | |
| "min_count": 0, | |
| "p05_count": 0.0, | |
| "p10_count": 0.0, | |
| "median_count": 0.0, | |
| "max_count": 0, | |
| "min_required_count": 16, | |
| "required_percentile": 5 | |
| }, | |
| "rounds": [ | |
| { | |
| "processed_samples": 128, | |
| "coverage_sufficient": true, | |
| "collection_sufficient": true, | |
| "coverage": { | |
| "has_expert_counts": false, | |
| "expert_modules": 0, | |
| "total_experts": 0, | |
| "active_experts": 0, | |
| "zero_count_experts": 0, | |
| "active_ratio": 1.0, | |
| "min_count": 0, | |
| "p05_count": 0.0, | |
| "p10_count": 0.0, | |
| "median_count": 0.0, | |
| "max_count": 0, | |
| "min_required_count": 16, | |
| "required_percentile": 5 | |
| } | |
| } | |
| ] | |
| }, | |
| "expert_coverage": { | |
| "has_expert_counts": false, | |
| "expert_modules": 0, | |
| "total_experts": 0, | |
| "active_experts": 0, | |
| "zero_count_experts": 0, | |
| "active_ratio": 1.0, | |
| "min_count": 0, | |
| "p05_count": 0.0, | |
| "p10_count": 0.0, | |
| "median_count": 0.0, | |
| "max_count": 0, | |
| "min_required_count": 16, | |
| "required_percentile": 5 | |
| }, | |
| "applied": [ | |
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| "model.layers.0.self_attn.q_proj", | |
| "model.layers.0.self_attn.v_proj", | |
| "model.layers.1.mlp.down_proj", | |
| "model.layers.1.mlp.gate_proj", | |
| "model.layers.1.mlp.up_proj", | |
| "model.layers.1.self_attn.k_proj", | |
| "model.layers.1.self_attn.o_proj", | |
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| "model.layers.1.self_attn.v_proj", | |
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| "model.layers.12.mlp.down_proj", | |
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| "model.layers.22.self_attn.o_proj", | |
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| "model.layers.22.self_attn.v_proj", | |
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| "model.layers.28.self_attn.v_proj", | |
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| ], | |
| "missing": [ | |
| "model.embed_tokens" | |
| ], | |
| "mismatched": [], | |
| "zero_count_experts": 0 | |
| } |