Instructions to use groxaxo/Nemotron-3-Embed-1B-oQ2-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use groxaxo/Nemotron-3-Embed-1B-oQ2-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Nemotron-3-Embed-1B-oQ2-MLX groxaxo/Nemotron-3-Embed-1B-oQ2-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Nemotron-3-Embed-1B-oQ2-MLX
An Apple-Silicon-native OMLX oQ2 conversion of
nvidia/Nemotron-3-Embed-1B-BF16.
This derivative was produced locally on Apple Silicon with the same streaming, sensitivity-driven OMLX pipeline used for the oQ5 release. It uses a base 2-bit affine plan, group size 64, and bfloat16 working dtype. The calibration ran on the checkpoint's bidirectional encoder architecture rather than through a causal-LM wrapper. No weights were retrained.
The quantized safetensors payload is 427,993,279 bytes. OMLX promoted 23 sensitivity-selected projections to 5, 6-bit precision.
Embedding contract
- bidirectional attention (
is_causal: false) - average/mean pooling
- L2-normalized 2,048-dimensional output
query:prefix for queriespassage:prefix for documents
BF16 fidelity smoke test
The source and this oQ2 checkpoint were evaluated using a query, a matching passage, and an unrelated passage. All outputs were 2,048-dimensional and L2-normalized.
| Input | BF16 to oQ2 vector cosine |
|---|---|
| Query | 0.934469 |
| Related passage | 0.920509 |
| Unrelated passage | 0.923551 |
| Comparison | BF16 | oQ2 |
|---|---|---|
| Query to related passage | 0.680575 | 0.715155 |
| Query to unrelated passage | -0.024595 | -0.016734 |
Quick start on Apple Silicon
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade mlx mlx-lm transformers huggingface_hub
hf download groxaxo/Nemotron-3-Embed-1B-oQ2-MLX --local-dir ./Nemotron-3-Embed-1B-oQ2-MLX
python ./Nemotron-3-Embed-1B-oQ2-MLX/embed_mlx.py \
--model ./Nemotron-3-Embed-1B-oQ2-MLX \
--query "Which city is known as the City of Sails?" \
--passage "Auckland is widely known as the City of Sails."
The included script deliberately loads the bare Ministral3 backbone and
applies non-causal attention. The regular MLX-LM generation path is causal and
is not the correct embedding interface for this checkpoint.
Quantization details
| Setting | Value |
|---|---|
| OMLX source revision | a534507fb5ab760d239283868a4c790386b316ff |
| Base model revision | a5e0f804b9e90a1ca6784ecbf6e41595774fc834 |
| OMLX level | oQ2 |
| Base quantization | 2-bit affine |
| Group size | 64 |
| Working dtype | bfloat16 |
| Calibration | 128 samples × 256 tokens |
| Sensitivity-selected boosts | 23 |
See QUANTIZATION_REPORT.md for artifact SHA-256 values and the full local
validation result.
License
This derivative inherits the
OpenMDW-1.1
license from the source model. LICENSE, NOTICE, and
THIRD_PARTY_NOTICES.md are included unchanged from NVIDIA's source
repository.
- Downloads last month
- 70
2-bit
Model tree for groxaxo/Nemotron-3-Embed-1B-oQ2-MLX
Base model
mistralai/Ministral-3-3B-Base-2512