Feature Extraction
MLX
Safetensors
ministral3
apple-silicon
quantized
mixed-precision
axquant
axq
development
mistral3
MXFP8
embedding
sentence-similarity
8-bit precision
Instructions to use AutomatosX/AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download AutomatosX/AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8 --local-dir AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download axquant_plan.json from AutomatosX/AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8: direct link, hf CLI and curl.
- Browser
- Download file 131 kB
-
https://huggingface.co/AutomatosX/AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8/resolve/main/axquant_plan.json
- Command line
-
hf download hf://AutomatosX/AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8/axquant_plan.json
-
curl -L -o axquant_plan.json https://huggingface.co/AutomatosX/AX-Nemotron-3-Embed-1B-MLX-AXQ-MXFP8/resolve/main/axquant_plan.json
131 kB
| { | |
| "analysis_sha256": "d688d7f271f8bd5c850515e3fc5e01138710005fbdee7719a2beb4895c993a8d", | |
| "architecture_profile": { | |
| "adapter_id": "mistral3-dense-v1", | |
| "audio_present": false, | |
| "config_model_type": "ministral3", | |
| "dense": true, | |
| "mtp_declared": false, | |
| "notes": [ | |
| "Mistral3 multimodal shells (including Ministral-3): language path is optimized; vision tower is stripped by MLX-LM sanitize and preserved only when present as protected tensors in the source inventory.", | |
| "Nemotron-3-Embed (model_type=ministral3) is feature-extraction only; do not claim generative or MTP metrics." | |
| ], | |
| "optimization_scope": "text-path", | |
| "product_family": "mistral3", | |
| "support_level": "supported", | |
| "support_tier": "convertible", | |
| "text_layer_count": 16, | |
| "vision_present": false | |
| }, | |
| "assignments": [ | |
| { | |
| "bits": 8, | |
| "group_size": 32, | |
| "method": "affine", | |
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| "task_loss_delta": 0.0, | |
| "token_disagreement": 0.0 | |
| }, | |
| "module_path": "embed_tokens", | |
| "outlier_strategy": "none", | |
| "parameters": 268435456, | |
| "predicted_loss": 0.0, | |
| "reason": "manual rule embedding-8: Embedding floor 8-bit affine", | |
| "role": "embedding", | |
| "scale_strategy": "group-affine", | |
| "strategy_metadata": { | |
| "selected_from_candidates": 1, | |
| "storage_bpw": 9.0 | |
| }, | |
| "tensor": "embed_tokens.weight" | |
| }, | |
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| "bits": 16, | |
| "group_size": null, | |
| "method": "bf16", | |
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| }, | |
| "module_path": "layers.0.input_layernorm", | |
| "outlier_strategy": "none", | |
| "parameters": 2048, | |
| "predicted_loss": 0.0, | |
| "reason": "manual rule protect-norms: Protection floor — norms stay BF16", | |
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