Feature Extraction
Transformers
ONNX
Safetensors
multilingual
bidirectional_pplx_qwen3
sentence-similarity
conteb
contextual-embeddings
custom_code
text-embeddings-inference
Instructions to use perplexity-ai/pplx-embed-context-v1-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use perplexity-ai/pplx-embed-context-v1-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="perplexity-ai/pplx-embed-context-v1-4b", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("perplexity-ai/pplx-embed-context-v1-4b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from perplexity-ai/pplx-embed-context-v1-4b: direct link, hf CLI and curl.
- Browser
- Download file 362 Bytes
-
https://huggingface.co/perplexity-ai/pplx-embed-context-v1-4b/resolve/main/modules.json
- Command line
-
hf download hf://perplexity-ai/pplx-embed-context-v1-4b/modules.json
-
curl -L -o modules.json https://huggingface.co/perplexity-ai/pplx-embed-context-v1-4b/resolve/main/modules.json
362 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "", | |
| "type": "st_quantize.FlexibleQuantizer", | |
| "kwargs": ["quantization"] | |
| } | |
| ] | |