Feature Extraction
Transformers
Safetensors
qwen3
text-embeddings-inference
8-bit precision
compressed-tensors
Instructions to use Wfiles/MNLP_M2_quantized_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wfiles/MNLP_M2_quantized_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Wfiles/MNLP_M2_quantized_model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Wfiles/MNLP_M2_quantized_model") model = AutoModel.from_pretrained("Wfiles/MNLP_M2_quantized_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a26557698bd76c46ebc4a3ec1250095f2897222858ada3964ade6221e773dc9
- Size of remote file:
- 752 MB
- SHA256:
- f99a4721db22a41a4a74bc2ffc87f9011bdf12dff925f635a6381f746f1907c7
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