pplx-context-mlx

Perplexity's pplx-embed-v2-context-0.6b contextual embeddings on Apple silicon. Each chunk's vector sees the rest of its document, and the upstream checkpoint loads unchanged, with no conversion step, PyTorch, or Transformers.

Independent project; not affiliated with or endorsed by Perplexity.

Quick start

pip install pplx-context-mlx

from pplx_context_mlx import load_model

engine = load_model()  # the first call downloads about 2.4 GB of weights
chunks = engine.encode(
    [["First chunk of a document.", "Second chunk, which sees the first."]],
    normalize_embeddings=True,
)
query = engine.encode_queries([["What does the second chunk see?"]], normalize_embeddings=True)
scores = chunks[0] @ query[0][0]

Each document is a list of chunks, and you get one 1,024-dimensional vector per chunk. Pass dtype="float16" to load_model for half the weight memory and a slightly faster model.

Page images embed as horizontal bands, with one vector per band:

from PIL import Image

bands = engine.encode_images([Image.open("page.png")], chunks=8, normalize_embeddings=True)[0]
page_score = (bands @ query[0][0]).max()

From the command line, input is a JSON list of documents:

pplx-context-mlx docs.json --dtype float16 --normalize > vectors.json

At a glance

Measured on an M4 Pro with 64 GB, against upstream PyTorch on CPU. Sections below have the details and the report behind each figure.

Question	Answer
Same vectors as upstream?	Within 1 int8 step; lowest cosine 0.999998 at float32, 0.99978 at float16
Same retrieval scores as the paper?	ViDoRe-V3 55.82 against 55.81; ConTEB within 0.22 on six of eight tasks, with two gaps upstream shares
How fast?	One query in 7.4 ms; a 7,137-token document in 1.16 s (float16), 3 to 12 times upstream CPU
How much memory?	3.8 GB (float16) or 5.5 GB (float32) on the speed suite; 13.2 GB at 119,041 tokens (float16)

Not tested: a 16 GB Mac, the 9B model, or a comparison with upstream on a GPU. Vector accuracy against upstream is verified up to 13,871 tokens.

Parity

Measured against the upstream PyTorch model at revision 6d1fddab, FP32 on CPU (its PyTorch fallback, not its CUDA fast path), over 16 text cases and 8 image cases, including padded batches, multilingual text, and a 13,871-token document.

MLX dtype	Largest int8 difference	Lowest cosine	Weights
float32 (default)	1 step	0.999998	2.38 GB
float16	1 step	0.99978	1.19 GB

Both rows compare against upstream at FP32. Other checks:

Check	Result	Report
Image pixels, every fixture	Byte-for-byte match	tests/test_images.py
48 real ViDoRe-V3 pages, 384 band vectors (float32)	Within 1 int8 step, lowest cosine 0.9999986	reports/real-pages/summary.json
Upstream on transformers 5.22.1 against the recorded 5.4.0 fixtures	Within 1 int8 step, once its missing config._commit_hash is set by hand; otherwise upstream raises AttributeError	reports/compat/transformers-5.22.1-drift.json
Nine parts of the model broken on purpose	The vectors catch all nine	reports/fault-injection.json

Regenerate with scripts/make_reference.py and scripts/parity_report.py. Results are in reports/parity-*.json.

Retrieval

Scored against the tables in the paper. Reproduce with evals/.

ViDoRe-V3, text to Markdown

Whole pages, one chunk per page, nDCG@10 averaged over six languages, 18,252 pages. This setting has no chunker to choose, so it is the cleanest comparison (paper Table 12).

Task	Paper	MLX float32
Finance (EN)	59.14	59.19
Finance (FR)	43.51	43.54
Industrial	43.47	43.45
Pharmaceuticals	63.37	63.46
Computer science	70.69	70.66
HR	56.22	56.25
Energy	63.62	63.64
Physics	46.47	46.35
Average	55.81	55.82

With 128-token chunks (paper Table 13) the average is 58.33 against 58.51. The paper's chunker is not public, so this run uses fixed token slices.

ConTEB

nDCG@10 on the official task splits (paper Table 5).

Task	Paper	MLX float32	MLX float16
Geography	94.1	94.10	94.10
Football	79.2	79.17	79.16
SQuAD	87.3	87.27	87.24
NarrativeQA	83.4	83.39	83.40
MLDR	89.8	89.65	89.78
COVID-QA	56.1	56.32	56.21
Insurance	88.0	88.93	88.93
ESG	67.3	53.54	52.74
Average	80.7	79.05	78.94

ESG is the one large gap, and it is not specific to this port: upstream's PyTorch model scored 52.74 under the same harness, and 88.93 on Insurance, the same as MLX. On those two tasks every query has the same top result in both runtimes, with 99.7% to 100% top-10 overlap (reports/conteb/upstream-insurance-esg.json, reports/conteb/mlx-vs-upstream-rankings.json). ESG has 36 queries and documents of up to 60,217 tokens, which were embedded whole here. Its score changes with how much context each chunk sees:

Context per pass	ESG nDCG@10
Non-contextual	49.8
512 tokens	54.3
2,048	62.4
4,096	66.4
8,192	62.1
32,768	57.3
Whole document	53.5

So more context is not automatically better on this task. Which setting the paper used for ESG is not stated, and one query is worth about 2.8 points. Windows split documents between chunks (--window-tokens); results are in reports/conteb/esg-windows/, and the whole-document row is in reports/conteb/mlx-float32.json.

Speed

On an M4 Pro with 64 GB. Each run started with 1-minute and 5-minute load averages under 2.7, as recorded in its report. Background services were running. MLX values are medians of three runs. Upstream ran on CPU at its best thread count for each workload, chosen from 1, 2, 4 and 6, because 1 thread was fastest for short inputs and 4 to 6 for long ones. No CUDA comparison was run.

Workload	MLX float16	MLX float32	Upstream CPU
One query	7.4 ms	11.4 ms	71.9 ms
Queries per second, batches of 32	600	522	50
224-token document	0.029 s	0.032 s	0.160 s
1,785-token document	0.20 s	0.24 s	1.13 s
7,137-token document	1.16 s	1.43 s	4.55 s
A4 page, 8 bands	0.87 s	1.03 s	4.11 s

Results are in reports/benchmark/. Reproduce with scripts/benchmark.py.

Memory

load_model caps MLX's buffer cache at 1 GB by default; set cache_limit_mb to change it. MLX's cache is process-wide, so the cap covers the whole process, and a later load_model call replaces it.

Peak footprints below come from the OS and include the cache. All are for the 0.6B model. Times are indicative, because these runs were not held to the load limits above.

Tokens	float16 time	float16 peak	float32 time	float32 peak
13,681	3.0 s	4.1 GB	3.8 s	6.0 GB
29,521	10.8 s	5.3 GB	17.9 s	9.1 GB
59,041	42.0 s	8.0 GB
119,041	171 s	13.2 GB

Upstream on CPU measured a lower footprint on the speed workloads, 2.6 GB against 5.5 GB for MLX float32, so no memory advantage over upstream is claimed.

Attention scores queries in slices, which keeps memory far below the square of the document length. Very long inputs are possible, but they are not automatically better; see ESG.

Why the cap exists. Without it, a traced run over ESG documents in 32,768-token windows reached 50.47 GB of cache with 2.38 GB of active memory, then ended at window 10 of 38 without a traceback (reports/memory/uncapped-esg-trace.log). An earlier run of that task exited with code 137. What killed them was not identified. With the cap, the same workload holds the cache near 1 GB and completes all 38 windows (reports/memory/capped-esg-trace.log).

Scope

• Models: tested with pplx-embed-v2-context-0.6b only.
• Inputs: text and page images. Video is not supported.
• Weights: this package contains none and downloads them from Perplexity's repository.
• License: the code is MIT. Perplexity's cards for pplx-embed-v2-context-9b and pplx-embed-v2-late-0.6b declare MIT; the 0.6b context card does not currently carry a license tag.

Third-party notices are in NOTICE.