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README.md

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 ---
+language:
+- multilingual
+tags:
+- embeddings
+- retrieval
+- transformer-free
+- safetensors
+- edge-ai
 license: mit
 ---
+
+# Pipeowl-1.10-multilingual (Geometric Embedding)
+
+A transformer-free semantic retrieval engine.
+
+Features:
+- O(n) over vocabulary.
+- No attention.
+- No transformer weights.
+
+## Architecture
+
+- Static embedding table (V × D)
+- Aligned vocabulary index
+- Linear scoring
+- Pluggable decoder stage
+
+## Model Specs
+
+| item | value |
+|-----|------|
+| token size | 734803 |
+| embedding dim | 512 |
+| storage format | safetensors (FP16) |
+| data size | ~728 MB |
+| languages | multilingual |
+| startup time | ~912 ms |
+| query latency | ~65-72 ms |
+
+## Quickstart
+
+```bash
+git clone https://huggingface.co/WangKaiLin/PipeOwl-1.10-multilingual
+cd PipeOwl-1.10-multilingual
+
+pip install numpy safetensors
+
+python quickstart.py
+```
+
+## Example:
+
+Example semantic retrieval results:
+
+```bash
+請輸入句子： 確實
+
+Top-K Tokens:
+1.000 | 確實
+0.871 | 的確
+0.848 | 确实
+0.825 | 確かに
+0.796 | дійсно
+
+請輸入句子： 今天好想睡覺
+
+Top-K Tokens:
+0.711 | 今天
+0.691 | 今天的
+0.677 | 睡觉
+0.658 | 睡覺
+0.653 | 今日は
+
+請輸入句子： i want to sleep
+
+Top-K Tokens:
+0.735 | sleep
+0.686 | спать
+0.671 | schlafen
+0.642 | tidur
+0.638 | want
+
+請輸入句子： 哈囉你好阿
+
+Top-K Tokens:
+0.823 | 哈囉
+0.808 | 你好
+0.777 | こんにちは
+0.767 | 嘿
+0.765 | 嗨
+```
+
+## Repository Structure
+
+```bash
+PipeOwl-1.10-multilingual/
+ ├ README.md
+ ├ config.json
+ ├ LICENSE
+ ├ quickstart.py
+ ├ engine.py
+ ├ tokenizer.json
+ └ pipeowl.safetensors
+```
+
+## LICENSE
+
+MIT

config.json

@@ -0,0 +1,6 @@
+{
+  "model_type": "pipeowl",
+  "architecture": "semantic-field-retrieval",
+  "embedding_dim": 1024,
+  "vocab_size": 734803
+}

engine.py

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+from __future__ import annotations
+
+import json
+import os
+import re
+import math
+from dataclasses import dataclass
+from safetensors.numpy import load_file
+from typing import Dict, List, Tuple, Optional
+import numpy as np  # type: ignore
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve()
+data = load_file("pipeowl.safetensors")
+
+@dataclass
+class PipeOwlConfig:
+    """
+    全域設定。
+
+    embeddings_path:
+        語義場的基底向量矩陣 (V, D)
+        V = 詞彙數
+        D = 向量維度
+
+    delta_scalar_path:
+        每個 token 對應的一維場偏移量 (V,)
+        用來做 score 偏移（目前為靜態 bias）
+
+    vocab_path:
+        vocab list，必須與 embeddings 順序完全對齊。
+        index i <-> emb[i] <-> delta[i]
+
+    alpha:
+        base 相似度權重
+
+    beta:
+        delta 權重（目前為 logit bias，不是動態 loss）
+
+    top_k:
+        retrieval 預設回傳數量
+
+    temperature:
+        decode 階段採樣溫度
+
+    max_new_tokens:
+        decode 最大生成長度
+    """
+    ROOT_DIR = BASE_DIR.parent
+    vocab_path: str = str(ROOT_DIR / "tokenizer.json")
+
+    normalize_rows: bool = False        # True: enforce row-wise normalization for cosine==dot
+    ensure_contiguous: bool = True      # True: make emb contiguous for faster GEMV
+    max_token_len_cap: int = 32         # cap tokenizer max token length to prevent slow path / garbage vocab
+
+    #=============================
+    alpha: float = 1
+    #=============================
+
+    #=============================
+    # scoring mode
+    #=============================
+    score_mode: str = "residual"
+    # options: 
+    # "linear"  -> α*base + β*delta + γ*syntax
+    # "residual" -> α*base + (1 - α*base)*delta 
+    #=============================
+
+    ##=============================
+    ## "linear"
+    ## score = α*base + β*delta + γ*syntax
+    ##=============================
+    beta: float = 0.05
+    ##gamma: float = 1.5
+    ##=============================
+    ## if linear 
+    ## α=0.97 β=0.03 performance well
+    ## α=1 β=0.00 just same as model
+    ##=============================
+
+    ##=============================
+    ## "residual" 
+    ## score = α*base + (1 - α*base)*delta
+    ##=============================
+    ## if residual 
+    ## α=1 just same as model 
+    ## α=0.9 performance well 
+    ## α=0.5 find more meaning 
+    ##=============================
+
+    ##=============================
+    ## retrieval
+    ##=============================
+    top_k: int = 16
+    ##=============================
+
+    ##=============================
+    ## decode
+    ##=============================
+    temperature: float = 0.13
+    ##=============================
+    ## temperature = 0.13 performance well
+    ##=============================
+    max_new_tokens: int = 64
+    ##=============================
+
+"""
+def softmax(scores, temperature=0.3):
+    scores = np.array(scores) / temperature
+    exp = np.exp(scores - np.max(scores))
+    return exp / exp.sum()
+"""
+    
+def eval_token_nll(engine, text):
+    tokens = engine.tokenizer.tokenize(text)
+    if len(tokens) < 2:
+        return float("inf")
+
+    total_bits = 0.0
+    count = 0
+
+    for i in range(len(tokens) - 1):
+        context = "".join(tokens[:i+1])
+        target_token = tokens[i+1]
+
+        q = engine.encode(context)
+        logits = engine.score_vocab(q)
+        probs = engine.logits_to_probs(logits)
+
+        idx = engine.token_to_id.get(target_token)
+        p = float(probs[idx]) if idx is not None else 1e-9
+        p = max(p, 1e-9)
+
+        total_bits += -math.log2(p)
+        count += 1
+
+    return total_bits / count
+
+## semanticizer 
+class VocabTokenizer:
+    """
+    字串最大匹配 tokenizer。
+
+    設計目標：
+        將輸入文字拆成 vocab 中存在的 token。
+
+    方法：
+        - 使用最大長度優先匹配
+
+    適用情境：
+        vocab 是字 / 詞 級別，且已對齊 embedding。
+    """
+    def __init__(self, vocab_list, *, max_len_cap: Optional[int] = None):
+        self.vocab_set = set(vocab_list)
+
+        mx = max(len(t) for t in vocab_list)
+        if max_len_cap is not None:
+            mx = min(mx, int(max_len_cap))
+        self.max_len = mx
+
+    def tokenize(self, text):
+        text = text.lower().strip()
+
+        tokens = []
+        i = 0
+        n = len(text)
+
+        while i < n:
+            matched = False
+
+            for L in range(self.max_len, 0, -1):
+                if i + L <= n:
+                    piece = text[i:i+L]
+
+                    if piece in self.vocab_set:
+                        tokens.append(piece)
+                        i += L
+                        matched = True
+                        break
+
+            if not matched:
+                # 🔥 fallback char（最後才做）
+                tokens.append(text[i])
+                i += 1
+
+        return tokens
+
+class PipeOwlEngine:
+    """
+    PipeOwl 幾何語義引擎核心。
+
+    設計哲學：
+        index = 語義場座標
+
+        emb[i]     -> 詞向量
+        delta[i]   -> 詞的場偏移量
+        vocab[i]   -> 詞本身
+
+    核心流程：
+        text
+          ↓
+        tokenize
+          ↓
+        mean embedding
+          ↓
+        score = alpha*base + beta*delta
+          ↓
+        top-k
+          ↓
+        decode
+
+    這是一個：
+        Field-based retrieval language system
+    """
+
+    def __init__(self, cfg: PipeOwlConfig):
+        self.cfg = cfg
+        
+        #self.emb: np.ndarray = None            # (V, D) float32
+        #self.delta: np.ndarray = None          # (V,) float32
+        self.emb = data["embeddings"].astype(np.float32)
+        self.delta = data["delta_field"].astype(np.float32)  
+        self.token_to_id: Dict[str, int] = {}
+        self.id_to_token: List[str] = []
+
+        # Decoder (optional)
+        self.decoder = MicroGPTDecoder()       # inference-only stub; plug your trained weights later
+
+        self._load_assets()
+
+    # -------------------------
+    # asset loading
+    # -------------------------
+
+    def _load_assets(self) -> None:
+        """
+        載入語義場資產。
+
+        載入內容：
+            1. embeddings (V, D)
+            2. delta scalar (V,)
+            3. vocab list (V,)
+
+        關鍵假設：
+            三者必須 index 完全對齊。
+
+        幾何意義：
+            每個 index i 對應語義空間中的一個固定場點。
+
+        """
+        if not os.path.exists(self.cfg.vocab_path):
+            raise FileNotFoundError(self.cfg.vocab_path)
+
+        emb = self.emb
+
+        # embeddings: (V, D)
+
+        if emb.dtype != np.float32:
+            emb = emb.astype(np.float32, copy=False)
+
+        # ChatGPT note: make C-contiguous for faster GEMV
+        if self.cfg.ensure_contiguous and not emb.flags["C_CONTIGUOUS"]:
+            emb = np.ascontiguousarray(emb)
+
+        if self.cfg.normalize_rows:
+            norms = np.linalg.norm(emb, axis=1, keepdims=True) + 1e-12
+            emb = emb / norms
+
+        # delta: (V,)
+        self.delta = data["delta_field"]
+        if self.delta.dtype != np.float32:
+            self.delta = self.delta.astype(np.float32, copy=False)
+
+        if self.emb.ndim != 2:
+            raise ValueError(f"embeddings must be 2D (V, D), got shape={self.emb.shape}")
+
+        # (V, D)
+        V, _ = self.emb.shape
+
+        if self.delta.ndim != 1 or self.delta.shape[0] != V:
+            raise ValueError(f"delta must be shape (V,), got {self.delta.shape}, expected ({V},)")
+
+        # vocab json: build token_to_id and id_to_token
+        with open(self.cfg.vocab_path, "r", encoding="utf-8-sig") as f:
+            vocab_list = json.load(f)
+
+        if not isinstance(vocab_list, list):
+            raise ValueError("vocab must be a list for geometric field mode")
+
+        if len(vocab_list) != V:
+            raise ValueError(f"vocab size {len(vocab_list)} != embeddings V {V}")
+
+        self.vocab = vocab_list
+        self.id_to_token = vocab_list
+        self.token_to_id = {t: i for i, t in enumerate(vocab_list)}
+
+        self.tokenizer = VocabTokenizer(self.vocab)
+
+    # -------------------------
+    # encode (from vector library)
+    # -------------------------
+
+    def encode(self, text: str):
+        """
+        將文字投影到語義場中。
+
+        流程：
+            1. tokenize -> token list
+            2. 取每個 token 對應 emb
+            3. 做 mean pooling
+            4. normalize
+
+        數學形式：
+            q = normalize( mean( emb[token_i] ) )
+
+        幾何意義：
+            這是在語義場中求質心。
+
+        風險：
+            - mean pooling 會削弱方向性
+        """
+        # ChatGPT note: exact token fast-path (prevents "貓頭鷹 = mean(貓,頭,鷹)" pollution)
+        idx0 = self.token_to_id.get(text)
+        if idx0 is not None:
+            v = self.emb[idx0].astype(np.float32, copy=False)
+            # emb rows already normalized if cfg.normalize_rows=True; keep safe anyway:
+            v = v / (np.linalg.norm(v) + 1e-12)
+            return v
+
+        tokens = self.tokenizer.tokenize(text)
+        if not tokens:
+            return np.zeros(self.emb.shape[1], dtype=np.float32)
+
+        vecs = []
+        wts = []
+
+        for t in tokens:
+            idx = self.token_to_id.get(t)
+            if idx is None:
+                continue
+
+            vecs.append(self.emb[idx])
+            wts.append(max(1, len(t)))
+
+        if not vecs:
+            return np.zeros(self.emb.shape[1], dtype=np.float32)
+
+        vecs = np.stack(vecs, axis=0).astype(np.float32, copy=False)
+        wts = np.asarray(wts, dtype=np.float32)
+        q = np.average(vecs, axis=0, weights=wts)
+        q /= (np.linalg.norm(q) + 1e-12)
+        return q   
+
+    # -------------------------
+    # probs (decode)
+    # -------------------------
+
+    def logits_to_probs(self, logits: np.ndarray, temperature: Optional[float] = None) -> np.ndarray:
+        T = self.cfg.temperature if temperature is None else float(temperature)
+        x = logits.astype(np.float64) / max(T, 1e-8)
+        x = x - np.max(x)
+        exp_x = np.exp(x)
+        return (exp_x / np.sum(exp_x)).astype(np.float32)
+
+    # -------------------------
+    # loss / scoring (delta)
+    # -------------------------
+    def score_vocab(self, q: np.ndarray, alpha: Optional[float] = None, beta: Optional[float] = None) -> np.ndarray:
+        """
+        計算每個 vocab token 的場分數。
+
+        base:
+            emb @ q
+            若 emb 與 q 已正規化，則為 cosine similarity。
+
+        delta:
+            每個 token 的靜態場偏移量。
+
+        目前語義：
+            delta 是 logit bias。
+            不是 loss、不是 energy gradient。s
+
+        """
+        a = self.cfg.alpha if alpha is None else float(alpha)
+        b = self.cfg.beta if beta is None else float(beta)
+
+        base = self.emb @ q
+
+        if self.cfg.score_mode == "linear":
+            score = a * base + b * self.delta
+
+        elif self.cfg.score_mode == "residual":
+            score = a * base + (1 - a * base) * self.delta
+
+        else:
+            raise ValueError(f"Unknown score_mode: {self.cfg.score_mode}")
+
+        return score.astype(np.float32, copy=False)
+
+    def topk(self, score: np.ndarray, k: Optional[int] = None) -> List[Tuple[str, float]]:
+        """
+        取前 k 高分 token。
+
+        使用 argpartition 提升效率。
+
+        回傳：
+            [(token_string, score), ...]
+
+        幾何意義：
+            找出最接近 query 向量（含場偏移）的場點。
+
+        注意：
+            score 可能 > 1（因為加入 delta）。
+        """
+        k = self.cfg.top_k if k is None else int(k)
+        k = max(1, min(k, score.shape[0]))
+
+        # argpartition for speed
+        idx = np.argpartition(-score, k - 1)[:k]
+        idx = idx[np.argsort(-score[idx])]
+
+        out = []
+        for i in idx:
+            tok = self.id_to_token[i] if i < len(self.id_to_token) else str(i)
+            out.append((tok, float(score[i])))
+        return out
+
+    # -------------------------
+    # decode (microgpt inference-only)
+    # -------------------------
+    def decode(self, prompt_tokens: List[str]) -> str:
+        """
+        Decode 階段。
+
+        目前行為：
+            將 top tokens 拼成 prompt 字串，
+            丟給 microgpt stub。
+
+        設計定位：
+            retrieval 與 generation 分離。
+
+        現狀：
+            microgpt 尚未接上真實權重，
+            目前只是 pipeline 占位。
+        """
+
+        prompt = " ".join([t for t in prompt_tokens if t])
+        return self.decoder.generate(
+            prompt=prompt,
+            temperature=self.cfg.temperature,
+            max_new_tokens=self.cfg.max_new_tokens,
+        )
+
+    # -------------------------
+    # one-shot pipeline
+    # -------------------------
+    def pipeowl(
+        self,
+        text: str,
+        *,
+        top_k: Optional[int] = None,
+        alpha: Optional[float] = None,
+        beta: Optional[float] = None,
+        temperature: Optional[float] = None,
+        max_new_tokens: Optional[int] = None,
+    ) -> Dict[str, object]:
+        """
+        單次完整 pipeline。
+
+        流程：
+            text
+              ↓
+            encode
+              ↓
+            score_vocab
+              ↓
+            topk
+              ↓
+            decode
+
+        回傳：
+            {
+                "query": 原始文字,
+                "retrieved": top-k token + 分數,
+                "prompt": 用於 decode 的 token 串,
+                "decoded": 生成結果
+            }
+
+        這是語義場查詢的一次完整觀測。
+        """
+
+        q = self.encode(text)
+        s = self.score_vocab(q, alpha=alpha, beta=beta)
+        retrieved = self.topk(s, k=top_k)
+
+        # build a prompt from top tokens (simple & deterministic)
+        prompt_tokens = [t for (t, _) in retrieved[: min(len(retrieved), 8)]]
+        if temperature is not None:
+            self.cfg.temperature = float(temperature)
+        if max_new_tokens is not None:
+            self.cfg.max_new_tokens = int(max_new_tokens)
+
+        decoded = self.decode(prompt_tokens)
+        return {
+            "query": text,
+            "retrieved": retrieved,
+            "prompt": " ".join(prompt_tokens),
+            "decoded": decoded,
+        }
+
+
+# ----------------------------------------------------------------------
+# microgpt inference-only stub
+# ----------------------------------------------------------------------
+class MicroGPTDecoder:
+    """
+    推理階段占位 decoder。
+
+    設計目的：
+        讓 pipeline 可運行，
+        未來可替換為：
+            - 已訓練 microGPT
+            - 外部 LLM
+            - 或場驅動 sampling 模型
+
+    現在只是 scaffold。
+    
+    Inference-only placeholder.
+
+    Why placeholder?
+    - Your pasted microGPT file trains its own weights in-process.
+    - For a real decode stage, you want:
+      (A) load a trained state_dict from disk, OR
+      (B) keep a tiny trained model in memory, OR
+      (C) use microGPT purely as a sampler over a learned char vocab.
+
+    This class is the stable interface. Plug your implementation later.
+    """
+
+    def __init__(self):
+        # If you already have trained weights, add:
+        # self.state_dict = load(...)
+        pass
+
+    def generate(self, prompt: str, temperature: float = 0.8, max_new_tokens: int = 64) -> str:
+        # Minimal safe fallback: return prompt as “decoded” scaffold.
+        # Replace this with your microgpt forward+sampling once you have weights.
+        # (This keeps the pipeline callable today.)
+        return f"[microgpt_stub] {prompt}"

pipeowl.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7aad2dab21d1d1e7322ad1304d8e38f16e1ab92e42b714337c7d1701bf3d6b96
+size 753908174

quickstart.py

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+from engine import PipeOwlEngine, PipeOwlConfig
+import time
+
+#=== timetest ===
+"""
+t0 = time.perf_counter()
+"""
+#================
+
+engine = PipeOwlEngine(PipeOwlConfig())
+
+#=== timetest ===
+"""
+t1 = time.perf_counter()
+print(f"\n🚀 Cold start time: {(t1 - t0)*1000:.2f} ms\n")
+#""
+for _ in range(20):
+    t0 = time.perf_counter()
+    engine.pipeowl("雪鴞")
+    print((time.perf_counter() - t0) * 1000, "ms")
+"""
+#================
+        
+while True:
+
+    print()
+    query = input("請輸入句子： ")
+
+    out = engine.pipeowl(query, top_k=5)
+
+    print("\nTop-K Tokens:")
+    for text, score in out["retrieved"]:
+        print(f"{score:.3f} | {text}")
+
+#    print("\nDecoded:")
+#    print(out["decoded"])
+
+    print()