Feature Extraction
MLX
Safetensors
qwen2
embeddings
sentence-similarity
quantization
omlx
oq4e
4-bit precision
custom_code
Instructions to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-oQ4e 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-oQ4e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-oQ4e --local-dir gte-Qwen2-1.5B-instruct-MLX-oQ4e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download EVALUATION.json from TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-oQ4e: direct link, hf CLI and curl.
- Browser
- Download file 509 Bytes
-
https://huggingface.co/TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-oQ4e/resolve/main/EVALUATION.json
- Command line
-
hf download hf://TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-oQ4e/EVALUATION.json
-
curl -L -o EVALUATION.json https://huggingface.co/TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-oQ4e/resolve/main/EVALUATION.json
509 Bytes
| { | |
| "pair_count": 24, | |
| "top1": 1.0, | |
| "recall_at_5": 1.0, | |
| "mrr": 1.0, | |
| "mean_aligned_embedding_cosine_vs_bf16": 0.9881011843681335, | |
| "minimum_aligned_embedding_cosine_vs_bf16": 0.9773147106170654, | |
| "score_rmse_vs_bf16": 0.013694853521883488, | |
| "queries_with_rank_change": 0, | |
| "gate_passed": false, | |
| "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 | |
| } | |
| } | |