Feature Extraction
MLX
Safetensors
qwen2
embeddings
sentence-similarity
quantization
omlx
q4
4-bit precision
custom_code
Instructions to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gte-Qwen2-1.5B-instruct-MLX-Q4 TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| license: apache-2.0 | |
| library_name: mlx | |
| pipeline_tag: feature-extraction | |
| base_model: TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-BF16 | |
| tags: | |
| - mlx | |
| - embeddings | |
| - feature-extraction | |
| - sentence-similarity | |
| - quantization | |
| - omlx | |
| - q4 | |
| - 4-bit | |
| # gte-Qwen2-1.5B-instruct — MLX Q4 | |
| > **Capacity-first experimental checkpoint:** every Q4-family build in this sweep failed the predeclared minimum aligned-cosine fidelity gate. It is published as an experimental comparator, not as a BF16-equivalent embedding model. | |
| This is the **Q uniform affine quantization** checkpoint from a matched local embedding-quantization experiment. It is published with explicit lineage, calibration evidence where applicable, and the bounded evaluation result that accompanied the conversion. | |
| ## Provenance and lineage | |
| - Upstream model: [`Alibaba-NLP/gte-Qwen2-1.5B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | |
| - Upstream revision recorded for publication: `a9af15a6372d7d6b25e9fb07c2ccb9e1fe645644` | |
| - Revision evidence: upstream revision verified at publication time; historical local snapshot metadata was not retained | |
| - Direct parent: [`TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-BF16`](https://huggingface.co/TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-BF16) | |
| - Conversion rule: every quantized checkpoint branches directly from the family MLX BF16 checkpoint; no lossy checkpoint was used to create another. | |
| - Quantization: Q uniform affine quantization, nominal 4-bit, group size 64 | |
| - Local conversion stack: oMLX 0.5.3, mlx-lm 0.31.3, MLX 0.32.0 | |
| - Full collection: [MLX Embedding Quantization Matrix](https://huggingface.co/collections/TiGa-RCE/mlx-embedding-quantization-matrix-q-oq-oqe-at-4-6-8-bit-6a68d11afb238d4fe967d70b) | |
| `PROVENANCE.json` contains machine-readable lineage and SHA-256 hashes for the published weight files. No importance matrix was used for this checkpoint. | |
| ## Bounded local evaluation | |
| | Metric | Result | | |
| |---|---:| | |
| | Top-1 retrieval | 1.000 | | |
| | MRR | 1.000 | | |
| | Mean aligned cosine vs BF16 | 0.969969 | | |
| | Minimum aligned cosine vs BF16 | 0.948780 | | |
| | Score RMSE vs BF16 | 0.020860 | | |
| | Queries with rank change | 0 | | |
| | Predeclared gate | FAIL | | |
| The evaluation used 24 frozen query/document pairs, the upstream query instruction recipe, last-token pooling, L2 normalization, and direct comparison with vectors from the family BF16 checkpoint. This is an engineering smoke test, not MTEB and not a claim of universal quality. Retrieval success and representation fidelity are reported separately. | |
| ## Runtime scope | |
| This checkpoint targets Apple Silicon through MLX/oMLX. CUDA and PyTorch results are a separate control lane and must not be interpreted as measurements of MLX/Metal kernel performance. | |
| ## License and attribution | |
| Apache-2.0, following the upstream model card. The original model authors retain attribution for the upstream model; this repository contains a local MLX conversion or quantized derivative prepared by TiGa-RCE for reproducibility research. | |