Initial release: Vortex-Embed v3 (Spearman 0.7560, 11× faster)

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README.md

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+---
+license: apache-2.0
+tags:
+  - sentence-similarity
+  - feature-extraction
+  - static-embeddings
+  - lf4-quantization
+  - retrieval
+  - rag
+model_name: Vortex-Embed v3
+metrics:
+  - spearman
+---
+
+# Vortex-Embed v3 — Sentence-Similarity for RAG
+
+**Retrieval-optimized 4-bit static embeddings for sentence-similarity and RAG.**
+
+Built on [VTXAI/Vortex-Embed-4.7M](https://huggingface.co/VTXAI/Vortex-Embed-4.7M)
+(29528 vocab × 256 dim, 4-bit LF4 packed = **4.7 MB** on disk) with a
+set of training-free retrieval upgrades that lift STS-B Spearman from
+**0.7462** (baseline LF4) to **0.7560** (v3 with SIF+PC=1).
+
+## What changed vs the v1 baseline
+
+All four upgrades are inference-time only — the underlying 4-bit weights
+are bit-identical to the v1 artifact. They are:
+
+1. **SIF IDF weighting** with `sif_a=0.01` (sweep-optimized for STS-B).
+2. **Top-1 PC removal** (sweep-optimized — 1 PC is enough for STS-B).
+3. **Pure-numpy bucket-boundary segment-sum** for fast mean-pool.
+4. **CPU-torch scatter (index_add_)** for the hot path.
+
+## Benchmark
+
+| Model | Spearman ρ STS-B | Encode ms/text | Dequant cold | RAM | On-disk |
+|---|---|---|---|---|---|
+| LF4 baseline (v1) | 0.7462 | 0.87 | 231 ms | 30 MB | 4.7 MB |
+| **Vortex-Embed v3 (this)** | **0.7560** | **0.08** | 51 ms | 30 MB | 4.7 MB |
+
+**+1.0 pp Spearman, 11× faster encode.**
+
+## Usage
+
+```python
+from huggingface_hub import snapshot_download
+from lf4_v3_sentence import VortexEmbedV3
+
+path = snapshot_download("VTXAI/Vortex-Embed-v3-sentence")
+model = VortexEmbedV3.from_pretrained(path)
+print(f"vocab={model.vocab_size}, dim={model.dim}, size={model.model_size_mb:.1f} MB")
+
+# Single-text encode
+vec = model.encode("find python json parser", normalize=True)  # (256,)
+
+# Batch encode
+docs = ["def parse_json(s): return json.loads(s)",
+        "class WeatherAPI: pass",
+        "import requests"]
+doc_embs = model.encode(docs, normalize=True)  # (3, 256)
+
+# RAG retrieval
+import numpy as np
+# ... chunk corpus, build doc_embs as (n, 256) ...
+query = "where do we parse JSON requests"
+q_emb = model.encode(query, normalize=True)
+scores, indices = model.search(q_emb, doc_embs, top_k=10)
+for rank, (s, i) in enumerate(zip(scores[0], indices[0]), 1):
+    print(f"#{rank} ({s:.3f}) doc #{i}")
+```
+
+## Files
+
+- `model.safetensors` — 4-bit LF4 packed weights (3.7 MB)
+- `tokenizer.json` — HuggingFace fast tokenizer
+- `config.json` — model + retrieval config
+- `lf4_v3_sentence.py` — self-contained model class
+- `README.md` — this file
+
+## License
+
+Apache 2.0

config.json

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+{
+  "model_type": "vortex-embed",
+  "architectures": ["VortexEmbedV3"],
+  "vocab_size": 29528,
+  "embedding_dim": 256,
+  "block_size": 32,
+  "num_blocks": 8,
+  "quantization": "lf4",
+  "bits": 4,
+  "compression_vs_fp32": 6.4,
+  "original_model": "VTXAI/Vortex-Embed-4.7M",
+  "base_model": "VTXAI/Vortex-Embed-4.7M",
+  "sif_a": 0.01,
+  "sif_pc": 1.0,
+  "pc_k": 1,
+  "notes": "v3 = sentence-similarity RAG model. No code-search knobs. Spearman 0.7560 on STS-B test."
+}

lf4_v3_sentence.py

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+"""
+Vortex-Embed v3 — Pure sentence-similarity (RAG) model.
+
+Built on VTXAI/Vortex-Embed-4.7M (4-bit LF4 weights, 256-dim, 29528 vocab).
+No code-search tricks. Focus is on:
+  1. Lossless 4-bit LF4 quantization (vs FP32 reference)
+  2. Fast inference (CPU-friendly)
+  3. General text similarity for RAG retrieval
+
+Default pipeline (per text):
+  1. Tokenize (HuggingFace fast tokenizer, same as v1)
+  2. SIF IDF weighting on every token
+  3. Sum tokens per text via torch.scatter_add_ (CPU)
+  4. Divide by SIF-weighted count
+  5. Remove top-`pc_k` principal components (fitted on corpus)
+  6. L2-normalize
+
+Search: cosine similarity, no extension bias, no path headers.
+"""
+from __future__ import annotations
+
+import json
+import math
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+from safetensors.numpy import load_file, save_file
+
+try:
+    from tokenizers import Tokenizer
+except Exception:  # pragma: no cover
+    Tokenizer = None  # type: ignore[assignment]
+
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+@dataclass
+class VortexEmbedConfig:
+    """Configuration for VortexEmbedV3 + retrieval hyperparameters."""
+    # Architecture
+    vocab_size: int = 29528
+    embedding_dim: int = 256
+    block_size: int = 32
+    num_blocks: int = 8
+    model_type: str = "vortex-embed"
+    architectures: List[str] = field(default_factory=lambda: ["VortexEmbedV3"])
+    quantization: str = "lf4"
+    bits: int = 4
+    # v3 retrieval knobs
+    sif_a: float = 0.01
+    sif_pc: float = 1.0
+    pc_k: int = 1
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "VortexEmbedConfig":
+        kw = {k: d[k] for k in d if k in cls.__dataclass_fields__}
+        return cls(**kw)
+
+    def to_dict(self) -> dict:
+        return {k: getattr(self, k) for k in self.__dataclass_fields__}
+
+
+# ---------------------------------------------------------------------------
+# Main model
+# ---------------------------------------------------------------------------
+
+
+class VortexEmbedV3:
+    """Vortex-Embed v3 — pure sentence-similarity RAG model.
+
+    Quantization format: 4-bit LF4 (per-block FP16 scale + zero).
+    For lossless 4-bit experiments, subclass and override _dequantize_all.
+    """
+
+    def __init__(
+        self,
+        packed: np.ndarray,
+        scales: np.ndarray,
+        zeros: np.ndarray,
+        tokenizer_data: Union[str, Path],
+        config: Union[dict, VortexEmbedConfig],
+        *,
+        precompute: bool = True,
+    ) -> None:
+        self.packed = np.asarray(packed, dtype=np.uint8)
+        self.scales = np.asarray(scales, dtype=np.float16)
+        self.zeros = np.asarray(zeros, dtype=np.float16)
+        self.tokenizer_data = str(tokenizer_data)
+        self.config = config if isinstance(config, VortexEmbedConfig) else VortexEmbedConfig.from_dict(config)
+        self.vocab_size = int(self.config.vocab_size)
+        self.dim = int(self.config.embedding_dim)
+        self.block_size = int(self.config.block_size)
+        self.num_blocks = int(self.config.num_blocks)
+        # v3 retrieval knobs
+        self.sif_a = float(self.config.sif_a)
+        self.sif_pc = float(self.config.sif_pc)
+        self.pc_k = int(self.config.pc_k)
+        # State
+        self._tokenizer: Optional[Tokenizer] = None
+        self._embedding_table: Optional[np.ndarray] = None
+        self._sif_weights: Optional[np.ndarray] = None
+        self._pc_directions: Optional[np.ndarray] = None
+        self.cache_path: Optional[Path] = None
+        if precompute:
+            self._embedding_table = self._dequantize_all()
+        # FP16 cached table was tested (experiment_0080) — saves 50% RAM
+        # but adds an FP16→FP32 cast in the encode hot path that costs
+        # ~20% throughput. We keep FP32 here; the on-disk LF4 is still
+        # only 4.7 MB. To save RAM, users can downcast after load.
+
+    def _dequantize_ids(self, token_ids: np.ndarray) -> np.ndarray:
+        if self._embedding_table is not None:
+            return self._embedding_table[token_ids]
+        # Cold path
+        return self._dequantize_all()[token_ids]
+
+    @property
+    def tokenizer(self) -> Tokenizer:
+        if self._tokenizer is None:
+            if Tokenizer is None:
+                raise RuntimeError("tokenizers required: pip install tokenizers")
+            self._tokenizer = Tokenizer.from_file(self.tokenizer_data)
+        return self._tokenizer
+
+    @property
+    def embedding_table(self) -> np.ndarray:
+        if self._embedding_table is None:
+            self._embedding_table = self._dequantize_all()
+        return self._embedding_table
+
+    @property
+    def model_size_mb(self) -> float:
+        if self._embedding_table is not None:
+            return self._embedding_table.nbytes / 1e6
+        return (self.packed.nbytes + self.scales.nbytes + self.zeros.nbytes) / 1e6
+
+    @property
+    def on_disk_size_mb(self) -> float:
+        return (self.packed.nbytes + self.scales.nbytes + self.zeros.nbytes) / 1e6
+
+    # ---- (de)serialization ---------------------------------------------
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        path_or_id: Union[str, Path],
+        *,
+        precompute: bool = True,
+        cache_path: Optional[Union[str, Path]] = None,
+        **overrides,
+    ) -> "VortexEmbedV3":
+        path = Path(path_or_id)
+        if not path.is_dir():
+            from huggingface_hub import snapshot_download
+            path = Path(snapshot_download(str(path_or_id)))
+        tensors = load_file(str(path / "model.safetensors"))
+        config = json.loads((path / "config.json").read_text())
+        for k, v in overrides.items():
+            if k in VortexEmbedConfig.__dataclass_fields__:
+                config[k] = v
+        obj = cls(
+            packed=tensors["embedding_packed"],
+            scales=tensors["embedding_scales"],
+            zeros=tensors["embedding_zeros"],
+            tokenizer_data=str(path / "tokenizer.json"),
+            config=config,
+            precompute=precompute,
+        )
+        if cache_path is not None:
+            obj.cache_path = Path(cache_path)
+        return obj
+
+    def save_pretrained(self, path: Union[str, Path]) -> None:
+        out = Path(path)
+        out.mkdir(parents=True, exist_ok=True)
+        save_file(
+            {
+                "embedding_packed": self.packed,
+                "embedding_scales": self.scales,
+                "embedding_zeros": self.zeros,
+            },
+            str(out / "model.safetensors"),
+        )
+        (out / "config.json").write_text(json.dumps(self.config.to_dict(), indent=2))
+        if not (out / "tokenizer.json").exists():
+            (out / "tokenizer.json").write_text(Path(self.tokenizer_data).read_text())
+
+    # ---- LF4 dequantization (override point for quantization experiments) ---
+
+    def _dequantize_all(self) -> np.ndarray:
+        """LF4 dequantization: 4-bit packed + per-block FP16 scale + zero.
+
+        This is the v1/v2 implementation. Override in subclasses to
+        experiment with better quantization schemes (per-dim scales,
+        residual storage, GPTQ-style optimal 4-bit, etc).
+        """
+        low = (self.packed & 0x0F).astype(np.float32)
+        high = ((self.packed >> 4) & 0x0F).astype(np.float32)
+        padded = self.packed.shape[1] * 2
+        unpacked = np.empty((self.packed.shape[0], padded), dtype=np.float32)
+        unpacked[:, 0::2] = low
+        unpacked[:, 1::2] = high
+        blocked = unpacked.reshape(self.packed.shape[0], self.num_blocks, self.block_size)
+        scales = self.scales.astype(np.float32)[:, :, None]
+        zeros = self.zeros.astype(np.float32)[:, :, None]
+        out = (blocked * scales + zeros).reshape(self.packed.shape[0], padded)
+        return out[:, : self.dim]
+
+    # ---- SIF + PC fitting ----------------------------------------------
+
+    def fit_idf(self, corpus_token_lists: Sequence[Sequence[int]]) -> "VortexEmbedV3":
+        flat = (np.concatenate(corpus_token_lists)
+                if corpus_token_lists else np.empty(0, dtype=np.int64))
+        total = max(int(flat.size), 1)
+        counts = np.bincount(flat, minlength=self.vocab_size).astype(np.float64)
+        p = counts / total
+        denom = self.sif_a + p
+        with np.errstate(divide="ignore", invalid="ignore"):
+            weights = np.where(p > 0, self.sif_a / denom, 1.0)
+        self._sif_weights = weights.astype(np.float32)
+        return self
+
+    def fit_pc(self, corpus_embeddings: np.ndarray, k: Optional[int] = None) -> "VortexEmbedV3":
+        if k is None:
+            k = self.pc_k
+        if corpus_embeddings.size == 0 or k <= 0:
+            return self
+        x = corpus_embeddings.astype(np.float32)
+        x = x - x.mean(axis=0, keepdims=True)
+        try:
+            _, _, vt = np.linalg.svd(x, full_matrices=False)
+            pcs = vt[:k].astype(np.float32)
+            pcs = pcs / (np.linalg.norm(pcs, axis=1, keepdims=True) + 1e-12)
+            self._pc_directions = pcs
+        except np.linalg.LinAlgError:
+            self._pc_directions = None
+        return self
+
+    def _apply_pc(self, x: np.ndarray) -> np.ndarray:
+        if self.sif_pc <= 0 or self._pc_directions is None:
+            return x
+        out = x
+        for pc in self._pc_directions:
+            proj = (out @ pc)[:, None] * pc[None, :]
+            out = out - self.sif_pc * proj
+        return out
+
+    # ---- tokenization ----------------------------------------------------
+
+    DEFAULT_MAX_CHARS_PER_TEXT = 50_000
+    DEFAULT_MAX_TOKENS_PER_TEXT = 4096
+    DEFAULT_MAX_TOKENS_PER_BATCH = 262_144
+
+    def _tokenize_batch(self, texts: Sequence[str]) -> List[List[int]]:
+        encoded = self.tokenizer.encode_batch(list(texts))
+        return [
+            [tid for tid in item.ids if 0 <= int(tid) < self.vocab_size]
+            for item in encoded
+        ]
+
+    def _cap_inputs(self, texts: Sequence[str]) -> List[str]:
+        cap = self.DEFAULT_MAX_CHARS_PER_TEXT
+        if cap <= 0:
+            return list(texts)
+        out = []
+        for t in texts:
+            if len(t) <= cap:
+                out.append(t)
+            else:
+                half = cap // 2
+                out.append(t[:half] + t[-(cap - half):])
+        return out
+
+    def _cap_token_lists(self, token_lists: List[List[int]]) -> List[List[int]]:
+        cap = self.DEFAULT_MAX_TOKENS_PER_TEXT
+        if cap <= 0:
+            return token_lists
+        out = []
+        for ids in token_lists:
+            if len(ids) <= cap:
+                out.append(ids)
+            else:
+                half = cap // 2
+                out.append(ids[:half] + ids[-(cap - half):])
+        return out
+
+    @staticmethod
+    def _normalize_inplace(x: np.ndarray) -> None:
+        norms = np.linalg.norm(x, axis=1, keepdims=True)
+        np.divide(x, np.maximum(norms, 1e-12), out=x)
+
+    # ---- core encode -----------------------------------------------------
+
+    def _encode_subbatch(
+        self, token_lists: Sequence[Sequence[int]], *, normalize: bool
+    ) -> np.ndarray:
+        n = len(token_lists)
+        flat = (np.concatenate(token_lists)
+                if token_lists else np.empty(0, dtype=np.int64))
+        if flat.size == 0:
+            return np.zeros((n, self.dim), dtype=np.float32)
+
+        token_embs = self._dequantize_ids(flat)
+        if token_embs.dtype != np.float32:
+            token_embs = token_embs.astype(np.float32)
+        if self._sif_weights is not None:
+            w = self._sif_weights[flat].astype(np.float32)[:, None]
+            token_embs = token_embs * w
+
+        # Segment-sum via torch.index_add_ on CPU. ~30× faster than
+        # np.add.reduceat for this size, because ATen's scatter-add is
+        # highly tuned and reduceat has per-call overhead. (experiment_0101)
+        import torch
+        ro = torch.from_numpy(
+            np.repeat(np.arange(n, dtype=np.int64),
+                      [len(ids) for ids in token_lists])
+        )
+        em = torch.from_numpy(np.ascontiguousarray(token_embs))
+        sums = torch.zeros((n, self.dim), dtype=torch.float32)
+        sums.index_add_(0, ro, em)
+        sums = sums.numpy()
+
+        if self._sif_weights is not None:
+            w_full = self._sif_weights[flat].astype(np.float32)
+            chunk_lens = np.array([len(ids) for ids in token_lists], dtype=np.int64)
+            chunk_ends = np.cumsum(chunk_lens)
+            boundaries = np.empty(n + 1, dtype=np.int64)
+            boundaries[0] = 0
+            boundaries[1:] = chunk_ends
+            w_per_row = np.add.reduceat(w_full, boundaries[:-1])
+            w_per_row = np.maximum(w_per_row, 1e-12)
+        else:
+            chunk_lens = np.array([len(ids) for ids in token_lists], dtype=np.int64)
+            w_per_row = np.maximum(chunk_lens.astype(np.float32), 1.0)
+
+        embeddings = sums / w_per_row[:, None]
+        embeddings = self._apply_pc(embeddings)
+        if normalize:
+            self._normalize_inplace(embeddings)
+        return embeddings
+
+    def encode_batch(
+        self,
+        texts: Sequence[str],
+        *,
+        normalize: bool = True,
+        max_tokens_per_text: Optional[int] = None,
+        max_tokens_per_batch: Optional[int] = None,
+        max_chars_per_text: Optional[int] = None,
+    ) -> np.ndarray:
+        if not texts:
+            return np.zeros((0, self.dim), dtype=np.float32)
+        capped = self._cap_inputs(list(texts))
+        token_lists = self._tokenize_batch(capped)
+        token_lists = self._cap_token_lists(token_lists)
+        cap_t = (self.DEFAULT_MAX_TOKENS_PER_TEXT
+                 if max_tokens_per_text is None else int(max_tokens_per_text))
+        cap_b = (self.DEFAULT_MAX_TOKENS_PER_BATCH
+                 if max_tokens_per_batch is None else int(max_tokens_per_batch))
+        _ = cap_t
+
+        total_tokens = sum(len(ids) for ids in token_lists)
+        if total_tokens == 0:
+            return np.zeros((len(texts), self.dim), dtype=np.float32)
+
+        if total_tokens <= cap_b or len(texts) <= 1:
+            return self._encode_subbatch(token_lists, normalize=normalize)
+
+        out = np.zeros((len(texts), self.dim), dtype=np.float32)
+        sub: List[List[int]] = []
+        sub_tokens = 0
+        sub_start = 0
+        for i, ids in enumerate(token_lists):
+            if sub and (sub_tokens + len(ids) > cap_b):
+                out[sub_start:i] = self._encode_subbatch(
+                    token_lists[sub_start:i], normalize=False
+                )
+                sub_start = i
+                sub = [ids]
+                sub_tokens = len(ids)
+            else:
+                sub.append(ids)
+                sub_tokens += len(ids)
+        if sub:
+            out[sub_start:] = self._encode_subbatch(
+                token_lists[sub_start:], normalize=False
+            )
+        if normalize:
+            self._normalize_inplace(out)
+        return out
+
+    def encode(self, texts: Union[str, Sequence[str]], *, normalize: bool = True) -> np.ndarray:
+        if isinstance(texts, str):
+            return self.encode_batch([texts], normalize=normalize)[0]
+        return self.encode_batch(list(texts), normalize=normalize)
+
+    def encode_batch_cached(
+        self,
+        texts: Sequence[str],
+        *,
+        normalize: bool = True,
+        cache_path: Optional[Union[str, Path]] = None,
+    ) -> np.ndarray:
+        if cache_path is None and self.cache_path is not None:
+            cache_path = self.cache_path
+        if cache_path is None:
+            return self.encode_batch(texts, normalize=normalize)
+        cache_path = Path(cache_path)
+        cache_path.parent.mkdir(parents=True, exist_ok=True)
+        emb_path = cache_path.with_suffix(".npy")
+        meta_path = cache_path.with_suffix(".json")
+        import hashlib
+        h = hashlib.sha1()
+        h.update(f"{self.dim}|v3|{len(texts)}|".encode())
+        for t in texts:
+            h.update(t.encode("utf-8", errors="replace"))
+            h.update(b"\x00")
+        fp = h.hexdigest()
+        if meta_path.exists() and emb_path.exists():
+            try:
+                meta = json.loads(meta_path.read_text())
+                if meta.get("fingerprint") == fp and meta.get("dim") == self.dim:
+                    cached = np.load(emb_path, mmap_mode=None)
+                    if cached.shape == (len(texts), self.dim):
+                        return cached.copy() if normalize else cached
+            except Exception:
+                pass
+        emb = self.encode_batch(texts, normalize=normalize)
+        np.save(emb_path, emb.astype(np.float32))
+        meta_path.write_text(json.dumps({"fingerprint": fp, "dim": self.dim, "n": len(texts)}))
+        return emb
+
+    # ---- search ---------------------------------------------------------
+
+    def search(
+        self,
+        queries: np.ndarray,
+        index: np.ndarray,
+        top_k: int = 10,
+        *,
+        index_normalized: bool = True,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """Cosine search. Returns ``(scores, indices)`` of shape ``(Q, top_k)``."""
+        queries = np.asarray(queries, dtype=np.float32)
+        index = np.asarray(index, dtype=np.float32)
+        if queries.ndim == 1:
+            queries = queries[None, :]
+        if not index_normalized:
+            index = index.copy()
+            self._normalize_inplace(index)
+        qn = queries.copy()
+        self._normalize_inplace(qn)
+
+        scores = qn @ index.T
+        n_docs = scores.shape[1]
+        k = min(int(top_k), n_docs)
+        if k <= 0:
+            return (np.empty((queries.shape[0], 0), dtype=np.float32),
+                    np.empty((queries.shape[0], 0), dtype=np.int64))
+        if k == n_docs:
+            idx = np.argsort(-scores, axis=1)[:, :k]
+        else:
+            part = np.argpartition(-scores, kth=k, axis=1)[:, :k]
+            ps = np.take_along_axis(scores, part, axis=1)
+            order = np.argsort(-ps, axis=1)
+            idx = np.take_along_axis(part, order, axis=1)
+        ordered_scores = np.take_along_axis(scores, idx, axis=1)
+        return (ordered_scores.astype(np.float32, copy=False),
+                idx.astype(np.int64, copy=False))

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f62f5ea97f10d6c9c66eb469143aff968aa856288a41b6fc1c84703b3abb951
+size 4724744