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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57e3f3dbe6b3bfe9fc2f05b43260d789f27686cb643a843eefd7dc1b9b266a6e
- Size of remote file:
- 11.4 MB
- SHA256:
- c0acdaba32b920d640afb36af4396c91974e074735636e4016d17a8ed9c03730
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