Feature Extraction
MLX
Safetensors
qwen2
embeddings
sentence-similarity
quantization
omlx
q6
6-bit
custom_code
Instructions to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q6 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-Q6 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-Q6 TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q6
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 508 Bytes
c8627ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"pair_count": 24,
"top1": 1.0,
"recall_at_5": 1.0,
"mrr": 1.0,
"mean_aligned_embedding_cosine_vs_bf16": 0.9977855682373047,
"minimum_aligned_embedding_cosine_vs_bf16": 0.9961316585540771,
"score_rmse_vs_bf16": 0.004666702821850777,
"queries_with_rank_change": 0,
"gate_passed": true,
"gate_criteria": {
"top1_delta_min": 0.0,
"recall_at_5_delta_min": 0.0,
"mrr_delta_min": -0.01,
"minimum_aligned_embedding_cosine_min": 0.99,
"queries_with_rank_change_max": 2
}
}
|