Feature Extraction
Transformers
Safetensors
English
bidirectional_pplx_qwen3
quantized
4bit
bnb
custom_code
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use manu02/pplx-embed-v1-0.6b-bnb-4bit-nf4-dq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manu02/pplx-embed-v1-0.6b-bnb-4bit-nf4-dq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="manu02/pplx-embed-v1-0.6b-bnb-4bit-nf4-dq", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("manu02/pplx-embed-v1-0.6b-bnb-4bit-nf4-dq", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07eaf28c3285da72d56644bad5b1971077a05209c205ec480a06b3196a638589
- Size of remote file:
- 850 MB
- SHA256:
- ccb52a10a1fc0fb4207c742dd145ef35ffa9083cbde8895b3d303152ab27abed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.