perplexity-ai/pplx-embed-v1-late-0.6b

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pplx-embed-v1-late-0.6b: Late-Interaction Embeddings

pplx-embed-v1-late-0.6b is a token-level late-interaction embedding model for retrieval with MaxSim scoring. It is continued training of pplx-embed-v1-0.6b using ContrastiveLoss to optimize token-level MaxSim.

Token-level embedding dim is 128, which hits the fast path of the optional erikkaum/maxsim MaxSim kernel.

Usage

Using Sentence Transformers

This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the MultiVectorEncoder:

python

from sentence_transformers import MultiVectorEncoder
model = MultiVectorEncoder("perplexity-ai/pplx-embed-v1-late-0.6b", trust_remote_code=True)
query = "What motivates scientific discovery?"documents = [    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",]
query_embeddings = model.encode_query(query)document_embeddings = model.encode_document(documents)print(query_embeddings.shape, document_embeddings[0].shape)# (32, 128) (8, 128)
# MaxSim late-interaction scoring (higher is more relevant)scores = model.similarity(query_embeddings, document_embeddings)print(scores)# tensor([[31.4841, 31.2462, 31.4041]])

Using PyLate (indexing + retrieval)

python

from pylate import indexes, models, retrieve
model = models.ColBERT(    model_name_or_path="perplexity-ai/pplx-embed-v1-late-0.6b",    trust_remote_code=True,)
documents_ids = ["1", "2", "3"]documents = [    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",]
index = indexes.PLAID(    index_folder="pylate-index",    index_name="pplx-embed-v1-late-0.6b",    override=True,)documents_embeddings = model.encode(documents, is_query=False)index.add_documents(documents_ids=documents_ids, documents_embeddings=documents_embeddings)
retriever = retrieve.ColBERT(index=index)queries_embeddings = model.encode(["What motivates scientific discovery?"], is_query=True)scores = retriever.retrieve(queries_embeddings=queries_embeddings, k=3)print(scores)

Using the erikkaum/maxsim kernel (fast MaxSim scoring)

Fused MaxSim for reranking, pair scoring, or evaluation. Supports CUDA (sm_80/86/89) and Metal (Apple Silicon); fp32/fp16/bf16 in, fp32 out; forward-only.

python

import torchfrom kernels import get_kernelfrom pylate import models
device = "cuda" if torch.cuda.is_available() else "mps"model = models.ColBERT(    model_name_or_path="perplexity-ai/pplx-embed-v1-late-0.6b",    trust_remote_code=True,    device=device,)maxsim = get_kernel("erikkaum/maxsim", version=1, trust_remote_code=True)
q_emb = model.encode(["What motivates scientific discovery?"], is_query=True, convert_to_tensor=True)d_emb = model.encode([    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",], is_query=False, convert_to_tensor=True)
# Pad to [B=1, n_candidates, Ld_max, dim] for score_candidates_padded.Lq, dim = q_emb[0].shapen, Ld_max = len(d_emb), max(d.shape[0] for d in d_emb)queries_pad = q_emb[0].unsqueeze(0).to(device, torch.float16)documents_pad = torch.zeros(1, n, Ld_max, dim, device=device, dtype=torch.float16)for i, d in enumerate(d_emb):    documents_pad[0, i, : d.shape[0]] = d.to(device, torch.float16)query_lengths = torch.tensor([Lq], dtype=torch.int32, device=device)doc_lengths = torch.tensor([[d.shape[0] for d in d_emb]], dtype=torch.int32, device=device)
scores = maxsim.score_candidates_padded(queries_pad, documents_pad, query_lengths, doc_lengths)print(scores[0].tolist())  # fp32 scores per candidate

For ragged variable-length pair scoring (eval, distillation, hard-negative mining), use maxsim.score_pairs_packed(...) instead — see the kernel card for the packed API.

Performance

We evaluate pplx-embed-v1-late-0.6b on two standard late-interaction retrieval suites and report the average nDCG@10:

BEIR — average over 15 English retrieval tasks.
MIRACL — average over 18 languages.

Benchmark	`pplx-embed-v1-late-0.6b`	Reference
BEIR (15 tasks)	56.61	colbert-zero: 55.43
MIRACL (18 langs)	66.62	jina-colbert-v2: 62.28

Technical Details

This model uses late interaction: queries and documents are encoded as token-level vectors and scored with MaxSim rather than pooled into a single vector.

For background on the base embedding family, see the pplx-embed-v1-0.6b model card and the technical report: https://arxiv.org/abs/2602.11151.

Usage

Using Sentence Transformers

This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the MultiVectorEncoder:

python

from sentence_transformers import MultiVectorEncoder
model = MultiVectorEncoder("perplexity-ai/pplx-embed-v1-late-0.6b", trust_remote_code=True)
query = "What motivates scientific discovery?"documents = [    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",]
query_embeddings = model.encode_query(query)document_embeddings = model.encode_document(documents)print(query_embeddings.shape, document_embeddings[0].shape)# (32, 128) (8, 128)
# MaxSim late-interaction scoring (higher is more relevant)scores = model.similarity(query_embeddings, document_embeddings)print(scores)# tensor([[31.4841, 31.2462, 31.4041]])

from sentence_transformers import MultiVectorEncoder
model = MultiVectorEncoder("perplexity-ai/pplx-embed-v1-late-0.6b", trust_remote_code=True)
query = "What motivates scientific discovery?"documents = [    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",]
query_embeddings = model.encode_query(query)document_embeddings = model.encode_document(documents)print(query_embeddings.shape, document_embeddings[0].shape)# (32, 128) (8, 128)
# MaxSim late-interaction scoring (higher is more relevant)scores = model.similarity(query_embeddings, document_embeddings)print(scores)# tensor([[31.4841, 31.2462, 31.4041]])

Using PyLate (indexing + retrieval)

python

from pylate import indexes, models, retrieve
model = models.ColBERT(    model_name_or_path="perplexity-ai/pplx-embed-v1-late-0.6b",    trust_remote_code=True,)
documents_ids = ["1", "2", "3"]documents = [    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",]
index = indexes.PLAID(    index_folder="pylate-index",    index_name="pplx-embed-v1-late-0.6b",    override=True,)documents_embeddings = model.encode(documents, is_query=False)index.add_documents(documents_ids=documents_ids, documents_embeddings=documents_embeddings)
retriever = retrieve.ColBERT(index=index)queries_embeddings = model.encode(["What motivates scientific discovery?"], is_query=True)scores = retriever.retrieve(queries_embeddings=queries_embeddings, k=3)print(scores)

from pylate import indexes, models, retrieve
model = models.ColBERT(    model_name_or_path="perplexity-ai/pplx-embed-v1-late-0.6b",    trust_remote_code=True,)
documents_ids = ["1", "2", "3"]documents = [    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",]
index = indexes.PLAID(    index_folder="pylate-index",    index_name="pplx-embed-v1-late-0.6b",    override=True,)documents_embeddings = model.encode(documents, is_query=False)index.add_documents(documents_ids=documents_ids, documents_embeddings=documents_embeddings)
retriever = retrieve.ColBERT(index=index)queries_embeddings = model.encode(["What motivates scientific discovery?"], is_query=True)scores = retriever.retrieve(queries_embeddings=queries_embeddings, k=3)print(scores)

Using the erikkaum/maxsim kernel (fast MaxSim scoring)

Fused MaxSim for reranking, pair scoring, or evaluation. Supports CUDA (sm_80/86/89) and Metal (Apple Silicon); fp32/fp16/bf16 in, fp32 out; forward-only.

python

import torchfrom kernels import get_kernelfrom pylate import models
device = "cuda" if torch.cuda.is_available() else "mps"model = models.ColBERT(    model_name_or_path="perplexity-ai/pplx-embed-v1-late-0.6b",    trust_remote_code=True,    device=device,)maxsim = get_kernel("erikkaum/maxsim", version=1, trust_remote_code=True)
q_emb = model.encode(["What motivates scientific discovery?"], is_query=True, convert_to_tensor=True)d_emb = model.encode([    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",], is_query=False, convert_to_tensor=True)
# Pad to [B=1, n_candidates, Ld_max, dim] for score_candidates_padded.Lq, dim = q_emb[0].shapen, Ld_max = len(d_emb), max(d.shape[0] for d in d_emb)queries_pad = q_emb[0].unsqueeze(0).to(device, torch.float16)documents_pad = torch.zeros(1, n, Ld_max, dim, device=device, dtype=torch.float16)for i, d in enumerate(d_emb):    documents_pad[0, i, : d.shape[0]] = d.to(device, torch.float16)query_lengths = torch.tensor([Lq], dtype=torch.int32, device=device)doc_lengths = torch.tensor([[d.shape[0] for d in d_emb]], dtype=torch.int32, device=device)
scores = maxsim.score_candidates_padded(queries_pad, documents_pad, query_lengths, doc_lengths)print(scores[0].tolist())  # fp32 scores per candidate

import torchfrom kernels import get_kernelfrom pylate import models
device = "cuda" if torch.cuda.is_available() else "mps"model = models.ColBERT(    model_name_or_path="perplexity-ai/pplx-embed-v1-late-0.6b",    trust_remote_code=True,    device=device,)maxsim = get_kernel("erikkaum/maxsim", version=1, trust_remote_code=True)
q_emb = model.encode(["What motivates scientific discovery?"], is_query=True, convert_to_tensor=True)d_emb = model.encode([    "Scientists explore the universe driven by curiosity.",    "Children learn through curious exploration.",    "Historical discoveries began with curious questions.",], is_query=False, convert_to_tensor=True)
# Pad to [B=1, n_candidates, Ld_max, dim] for score_candidates_padded.Lq, dim = q_emb[0].shapen, Ld_max = len(d_emb), max(d.shape[0] for d in d_emb)queries_pad = q_emb[0].unsqueeze(0).to(device, torch.float16)documents_pad = torch.zeros(1, n, Ld_max, dim, device=device, dtype=torch.float16)for i, d in enumerate(d_emb):    documents_pad[0, i, : d.shape[0]] = d.to(device, torch.float16)query_lengths = torch.tensor([Lq], dtype=torch.int32, device=device)doc_lengths = torch.tensor([[d.shape[0] for d in d_emb]], dtype=torch.int32, device=device)
scores = maxsim.score_candidates_padded(queries_pad, documents_pad, query_lengths, doc_lengths)print(scores[0].tolist())  # fp32 scores per candidate

For ragged variable-length pair scoring (eval, distillation, hard-negative mining), use maxsim.score_pairs_packed(...) instead — see the kernel card for the packed API.

Performance

We evaluate pplx-embed-v1-late-0.6b on two standard late-interaction retrieval suites and report the average nDCG@10:

BEIR — average over 15 English retrieval tasks.

MIRACL — average over 18 languages.