Feature Extraction
Transformers
Safetensors
multilingual
pplx_contextual_qwen3_5
sentence-similarity
contextual-embeddings
custom_code
Instructions to use perplexity-ai/pplx-embed-v2-context-9b-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use perplexity-ai/pplx-embed-v2-context-9b-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="perplexity-ai/pplx-embed-v2-context-9b-preview", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("perplexity-ai/pplx-embed-v2-context-9b-preview", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from perplexity-ai/pplx-embed-v2-context-9b-preview: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/perplexity-ai/pplx-embed-v2-context-9b-preview/resolve/main/tokenizer.json
- Command line
-
hf download hf://perplexity-ai/pplx-embed-v2-context-9b-preview/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/perplexity-ai/pplx-embed-v2-context-9b-preview/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 4ac33d6e7eb2eee3ec4304578626bea4ffc503b547c364e0890e9b722f5db112
- Size of remote file:
- 20 MB
- SHA256:
- d76b678db762df7761041239ca8950fb190135c94bb66ce8aeff5a0ff2933cf5
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