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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("perplexity-ai/pplx-embed-context-v1-4b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
update readme
#3
by seslami-pplx - opened
README.md
CHANGED
|
@@ -22,6 +22,10 @@ language:
|
|
| 22 |
- Use **`pplx-embed-1`** for independent text embedding (queries, documents, semantic search)
|
| 23 |
- Use **`pplx-embed-1-context`** for document chunks in RAG systems where surrounding context matters
|
| 24 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 25 |

|
| 26 |
|
| 27 |
## Models
|
|
|
|
| 22 |
- Use **`pplx-embed-1`** for independent text embedding (queries, documents, semantic search)
|
| 23 |
- Use **`pplx-embed-1-context`** for document chunks in RAG systems where surrounding context matters
|
| 24 |
|
| 25 |
+
> [!IMPORTANT]
|
| 26 |
+
> `pplx-embed-1` and `pplx-embed-1-context` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*.
|
| 27 |
+
|
| 28 |
+
|
| 29 |

|
| 30 |
|
| 31 |
## Models
|