pplx embed v1 4b
by perplexity-ai
Open source · 16k downloads · 56 likes
2.2
(56 reviews)EmbeddingAPI & LocalAbout
pplx embed v1 4b is a cutting-edge text embedding model optimized for large-scale semantic search tasks. It generates dense, contextual vector representations suitable for both queries and documents, with a specialized version for RAG systems where surrounding context is critical. Unlike many models that require prefixed instructions, it allows direct embedding of the desired text, simplifying indexing pipelines and enhancing robustness. Its unnormalized int8 embeddings, compared using cosine similarity, strike a balance between performance and efficiency, making it ideal for real-time applications or deployment on limited infrastructure. The model stands out for its accuracy and flexibility without compromising result quality.
Documentation
pplx-embed-v1: Diffusion-Pretrained Dense and Contextual Embeddings
pplx-embed-v1 and pplx-embed-context-v1 are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks.
- Use
pplx-embed-v1for independent text embedding (queries, documents, semantic search) - Use
pplx-embed-context-v1for document chunks in RAG systems where surrounding context matters
[!IMPORTANT]
pplx-embed-v1andpplx-embed-context-v1natively produce unnormalized int8-quantized embeddings. Ensure that you compare them via cosine similarity.
Models
| Model | Dimensions | Context | MRL | Quantization | Instruction | Pooling |
|---|---|---|---|---|---|---|
pplx-embed-v1-0.6B | 1024 | 32K | Yes | INT8/BINARY | No | Mean |
pplx-embed-v1-4B | 2560 | 32K | Yes | INT8/BINARY | No | Mean |
pplx-embed-context-v1-0.6B | 1024 | 32K | Yes | INT8/BINARY | No | Mean |
pplx-embed-context-v1-4B | 2560 | 32K | Yes | INT8/BINARY | No | Mean |
All models are built on diffusion continued pre-trained Qwen3 at Perplexity AI.
Many modern embedding models rely on instruction tuning, where users prepend an instruction string to the text being embedded. This can yield a 2%-3% lift on benchmarks, but it also introduces prompt-selection overhead and can make indexing pipelines brittle (small instruction changes can shift embedding space). We deliberately avoid this requirement: you can embed the text you want to index directly, without having to choose or maintain an instruction prefix.
Usage
Via API
Bash
curl -X POST https://api.perplexity.ai/v1/embeddings \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": [
"Scientists explore the universe driven by curiosity.",
"Children learn through curious exploration.",
"Historical discoveries began with curious questions.",
"Animals use curiosity to adapt and survive.",
"Philosophy examines the nature of curiosity."
],
"model": "pplx-embed-v1-4b"
}'
Using SentenceTransformers
Python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
"perplexity-ai/pplx-embed-v1-4B",
trust_remote_code=True
)
texts = [
"Scientists explore the universe driven by curiosity.",
"Children learn through curious exploration.",
"Historical discoveries began with curious questions.",
"Animals use curiosity to adapt and survive.",
"Philosophy examines the nature of curiosity.",
]
embeddings = model.encode(texts) # Shape: (5, 2560), quantized to int8
embeddings = model.encode(texts, quantization="binary") # Shape: (5, 2560), quantized to binary
Using ONNX models
Python
import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np
tokenizer = AutoTokenizer.from_pretrained("perplexity-ai/pplx-embed-v1-4b", trust_remote_code=True)
session = ort.InferenceSession("onnx/model.onnx")
texts = [
"Scientists explore the universe driven by curiosity.",
"Children learn through curious exploration.",
"Historical discoveries began with curious questions.",
"Animals use curiosity to adapt and survive.",
"Philosophy examines the nature of curiosity.",
]
tokenized = tokenizer(
texts,
padding=True,
truncation=True,
return_tensors="np"
)
onnx_inputs = {
"input_ids": tokenized["input_ids"].astype(np.int64),
"attention_mask": tokenized["attention_mask"].astype(np.int64),
}
# Run inference
onnx_embeddings = session.run([out.name for out in session.get_outputs()], onnx_inputs)
# ONNX produces both int8 and binary precision embeddings:
int8_embeddings = onnx_embeddings[2]
binary_embeddings = onnx_embeddings[3]
packed_embeddings = np.packbits(binary_embeddings != -1, axis=-1)
Using Text Embeddings Inference (TEI)
[!NOTE] Text Embeddings Inference v1.9.2+ is required.
[!IMPORTANT] Currently, only int8-quantized embeddings are available via TEI. Remember to use cosine similarity with unnormalized int8 embeddings.
- CPU w/ Candle:
Bash
docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 --model-id perplexity-ai/pplx-embed-v1-4B --dtype float32
- CPU w/ ORT (ONNX Runtime):
Bash
docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 --model-id onnx-community/pplx-embed-v1-4B --dtype float32
- GPU w/ CUDA:
Bash
docker run --gpus all --shm-size 1g -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cuda-1.9 --model-id perplexity-ai/pplx-embed-v1-4B --dtype float32
If you hit OOM during warmup, lower --max-batch-tokens and --max-client-batch-size. Set --max-batch-tokens to max_sequence_length × batch_size (e.g., 2048 tokens × 8 sequences = 16384).
Alternatively, when running in CUDA you can use the architecture / compute capability specific container instead of the
cuda-1.9, as that includes the binaries for Turing, Ampere, Hopper and Blackwell, so using a dedicated container will be lighter e.g.,ampere-1.9.
And then you can send requests to it via cURL to /embed:
Bash
curl http://0.0.0.0:8080/embed \
-H "Content-Type: application/json" \
-d '{
"inputs": [
"Scientists explore the universe driven by curiosity.",
"Children learn through curious exploration.",
"Historical discoveries began with curious questions.",
"Animals use curiosity to adapt and survive.",
"Philosophy examines the nature of curiosity."
],
"normalize": false
}'
Technical Details
For comprehensive technical details and evaluation results, see our paper on arXiv: https://arxiv.org/abs/2602.11151.
Capabilities & Tags
sentence-transformersonnxsafetensorsbidirectional_pplx_qwen3feature-extractionsentence-similaritymtebcustom_codemultilingualtext-embeddings-inference
Links & Resources