voyage-4-nano is not served by the Voyage API — the open checkpoint is the only way to run it. Embeddings from this port land in the same space as the hosted voyage-4 models, so you can index locally and query with those: same-text cosine 0.969 against voyage-4-lite, 0.952 against voyage-4, 0.894 against voyage-4-large (12 texts, versus 0.32–0.36 for non-matching text).

Unofficial community port. Not affiliated with Voyage AI or MongoDB.

Use

pip install voyage-4-nano-mlx

python

from voyage_4_nano_mlx import load

emb = load("sanjay920/voyage-4-nano-mlx-8bit")

q = emb.encode_query("Which planet is known as the Red Planet?")
d = emb.encode_document([
    "Venus is often called Earth's twin because of its similar size.",
    "Mars, known for its reddish appearance, is called the Red Planet.",
])
emb.similarity(q, d)          # [[0.4052 0.6514]]

Queries and documents take different trained prefixes; encode_query and encode_document apply them. Use encode() for symmetric tasks.

Matryoshka dims (2048 / 1024 / 512 / 256) and compact outputs:

python

emb.encode_document(docs, dims=256)                        # 256-d, unit norm
emb.encode_document(docs, dims=512, output_dtype="int8")   # 512 int8 values
emb.encode_document(docs, output_dtype="ubinary")          # 2048 bits = 256 bytes

To shrink an index, prefer dims=256 + int8 over a smaller weight file — it cuts the index 32x and leaves the encoder faithful.

Variants

repo	size	min cosine vs fp64 reference
`sanjay920/voyage-4-nano-mlx-bf16`	672 MB	0.99997
`sanjay920/voyage-4-nano-mlx-8bit`	362 MB	0.99966
`sanjay920/voyage-4-nano-mlx-6bit`	280 MB	0.99691
`sanjay920/voyage-4-nano-mlx-4bit`	198 MB	0.97465

Weight quantization saves disk, not time: at typical sequence lengths this model is compute-bound, and 8-bit and 4-bit both run ~7% slower than bf16.

Verification

Against the reference PyTorch implementation, with a float64 run as ground truth:


this variant, pooled cosine vs fp64 reference	0.99966
bf16 on MTEB SciFact / NFCorpus (nDCG@10)	0.75262 / 0.39576
PyTorch reference, same tasks	0.75191 / 0.39568

On MLX's CPU backend the unquantized port matches float64 as closely as PyTorch's own float32 does (max error 1.03e-04 vs 1.07e-04). Tokenization is byte-identical to the reference across 14 texts covering CJK, emoji, URLs and whitespace edge cases. Method and full numbers: docs/PARITY.md.

Model

A Qwen3 backbone run bidirectionally, not an encoder-only model:

Qwen3, 12 layers, d=1024, 16 heads / 8 KV heads, head_dim=128
RMSNorm, SwiGLU, RoPE theta=1e6, per-head q/k RMSNorm, bidirectional attention
-> final norm
-> linear 1024 -> 2048   (per token, before pooling)
-> mean pool over the attention mask
-> L2 normalize

346M parameters, 32k context, multilingual.

Notes

Metal's float32 matmul accumulates at reduced precision, so GPU fp32 is slightly less exact than CPU fp32 (0.9999995 vs 1.0000000 cosine). It does not affect retrieval.
32k-token inputs use dense attention and take ~6 s each.

Source, tests and benchmarks: https://github.com/sanjay920/voyage-4-nano-mlx