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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,175 @@
+# ROSA+
+**ROSA+**: RWKV's ROSA implementation with fallback statistical predictor
+<hr/>
+<img width="700" alt="image" src="https://github.com/user-attachments/assets/606a4d61-87b8-4bfc-ac27-564528042605" />
+<hr/>
+
+## What is ROSA+?
+**ROSA+** is an extension of the **statistical next-token predictor** [proposed by BlinkDL](https://x.com/BlinkDL_AI/status/1976912771985146184) in extending the RWKV language model. It provides an intuitive Python interface as well as a fallback Witten–Bell predictor for unknown sequences.
+
+## Example Usage in Python
+The implementation is self-contained in `rosaplus.py`. You can download the repository and use it from there.
+
+```python
+# example.py
+from rosaplus import ROSAPlus
+import requests
+
+# Train on Shakespare
+url = "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
+
+# Download the text
+response = requests.get(url)
+text = response.text
+print("Downloaded text.")
+
+# Initialize model
+m = ROSAPlus(max_order=1048576, use_eot=False, seed=0) 
+m.train_example(text) # Train ROSA
+m.build_lm() # Train fallback predictor
+
+# Prompting
+prompt = "ROMEO:" # Novel text
+max_tokens = 256
+
+# Eval mode
+print(prompt + m.generate(prompt, steps=max_tokens))
+
+# Saving model
+m.save("rosa-model.json")
+m2 = ROSAPlus.load("rosa-model.json") # Loading model
+```
+
+**Output:** (verbatim)
+```
+ROMEO:
+In faith, I will. Let me peruse this face.
+Mercutio's kinsman, noble County Paris!
+What said my man, when my betossed soul
+Did not attend him as we rode? I think
+...
+```
+
+## Novel Text Generation
+ROSA+ can also be used to generate novel sequences that **do not show up in the training dataset.** You can enable this by **always** using the fallback predictor. It often leads to coherent, surprising results.
+```python
+# add always_fallback=True to the example
+print(prompt + m.generate(prompt, steps=max_tokens, always_fallback=True))
+```
+
+**Output:** (novel)
+```
+ROMEO:
+The exchange of joy
+That only Warwick's daughter.
+
+CLARENCE:
+To whom, my lord; the foe vantage.
+But make you read no other, and look'd deadly that name remains;
+The cruelty and envy of the people,
+Permitted by our faces
+For man or master; then it for some
+```
+
+As you can see, these arrangement of sentences do not show up in the dataset (CTRL+F). Rather, ROSA+ intelligently splices and pulls together the features from ROSA to perform next-character prediction.
+
+For any given prefix, you can also get the probability distribution for the next token:
+```python
+# Eval mode
+print(m.get_dist("ROMEO:\nOh, how could yo"))
+```
+
+Output:
+```
+{'u': 0.9999989177710094, 'n': 5.442332067424175e-07, 'k': 5.379892385443467e-07, 'r': 6.0439900862193395e-12, ' ...
+```
+
+As you can see, ROSA+ is extremely confident that 'u' is the next token (and it is correct!)
+
+## RWKV Support
+This is just a standalone example of ROSA and does not provide RWKV integration. You will have to go to the RWKV Discord or ask the main maintainer (BlinkDL) for assistance in this regard.
+
+## Extensions
+ROSA+ extends ROSA by:
+
+- Allowing training and sampling on individual sequences, similar to a LLM
+- Utilizing a (coherent) fallback Witten–Bell based predictor for when ROSA is unsure of the next token.
+
+This makes it extremely fast, since ROSA is used for 99% of the predictions and the fallback only occurs for novel sequences.
+
+**Tokenization:** The default tokenization is character-based (I will add support for new tokenizers coming soon.)
+
+## Notes
+
+If you install [orjson](https://github.com/ijl/orjson), it will use it automatically and lead to far faster import/export speed.
+Docs coming soon.
+
+## Issues with ROSA+
+
+ROSA+ is entirely statistical-based -- it extends upon the ROSA predictor proposed by BlinkDL, then provides a probability predictor as a fallback. However, this means it only has a **database-like** understanding of text -- it can **stitch together multiple sentences** and **demonstrate grammar**, but it lacks the same context understanding as an NN (RWKV, Transformer etc.)
+
+For instance, when trained on Shakespeare, and with `always_fallback=True` (forcing novel predictions), it generates text that "looks right", but switches between characters every stanza.
+
+```
+COMINIUS:
+Well, one nail;
+Right noble is thy mercy dried their watches of chance and thy lord's false love;
+For both of you are birds of selfsame feather.
+
+KING EDWARD IV:
+Peace, wilful boy, or I will charm your tongue.
+
+CLARENCE:
+Unhappy fortune! by my troth, I looked upon his faith iron cook you, sir, he bid me knocks; ha! let me be unrolled and said 'The better for our purpose.'
+
+KING RICHARD III:
+So proud the name of Henry with your holy look'd on me,
+And wouldst do not break your oaths; for of that sin
+May deny her aiding have these nothing here.
+
+AUTOLYCUS:
+I hope so, sir; for I have about me manner doth accuse my husband, I
+...
+```
+
+A ChatGPT analysis of ROSA+'s lines uncovers some insight:
+```
+Short answer: it’s Shakespeare-flavored, not Shakespearean. It reads like a collage of misquoted or remixed lines, with scrambled idioms, mixed plays (Juliet/Romeo with Buckingham and Gaunt), and meter/grammar that don’t line up with blank verse.
+
+Quick notes:
+
+* “Now, by Saint Peter’s Church…” and “I have forgot why I did call thee back” echo *Romeo and Juliet*, but they’re spliced into new contexts.
+* “The world goes his bonnet to an oystery” mangles Pistol’s “The world’s mine oyster.”
+* Shifts between **you/thee/thou/thy** are inconsistent (use *thou* as subject, *thee* as object, *thy/thine* as possessives).
+* Many lines don’t scan as iambic pentameter (10 syllables, mostly unstressed–stressed).
+```
+
+A true NN-based model would outperform a standalone ROSA+ implementation because of the understanding of actual context. While ROSA+ has impressive surface-level understanding, it lacks deeper, low level meaning expressed by NNs.
+
+
+### Interesting occurences
+You can view all the samples in the `samples` directory -- interestingly, in `sample_default_output.txt`, the model falls into an attractor state, repeating itself every ~3k lines, halfway through. However, in `sample_novel_output.txt`, you can spot some very novel sentences:
+```
+LADY ANNE:
+Well, well, peace be with you, sir, he bid me know the points o' the dead
+May walk again: if such thing but what I am,
+I would wish it gone,
+```
+
+The phrases `Well, well, peace be with you` and `I would wish it gone` never show up in the training data.
+
+## Use cases
+- Autocorrect / word prediction
+- Translation (possibly)
+- Features for a lower level model
+- Generating surface-level text that fools detectors
+
+## Improving ROSA+
+One may be able to create a coherent language model simply by feeding ROSA+ embeddings into a GRU. Since ROSA+ captures the immediate surface-level features of text, a sufficient neural network may be able to operate on these embeddings and alter the distribution for more fine-grained understanding.
+
+## Downsides of statistical LMs
+Unless statistical LMs incorporate some kind of **statistical attention mechanism** (which is possible!) they will never be able to grasp a high-level understanding of text as do humans and LMs. A statistical LM is unable to **copy data / tokens from one place to another**, **operate on a continous state**, **blend together tokens across different spaces**, perform **few-shot learning** (needs neural mechanism!) or **transfer learning** (no state vectors!). Therefore, their purpose remains limited to grasping **surface-level** features of text, like syntax, or general structure.
+
+Google pushed to make their translation software (which in the 2010s, was n-gram based) the best at the time, but even **LSTMs** (which were invented way before Transformers) managed to outperform them.
+
+Do not let this discourage you though. It may be practical to incorporate some kind of **continous state vector / representation** within a statistical model, making it **drastically more efficient than LLMs** while preserving all the benefits of **NN-based models.** This is an active field of research at **Bellevue College ML** (BCML) -- and if pioneered, could result in language models thousands of times more efficient. Don't let an article discourage you.

main.py

@@ -0,0 +1,37 @@
+from rosaplus import ROSAPlus
+# import requests
+
+# # Train on Shakespare
+# url = "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
+
+# # Download the text
+# response = requests.get(url)
+# text = response.text
+# print("Downloaded text.")
+
+
+# m2 = ROSAPlus.load("rosa-model.json")
+# prompt = "ROMEO:" # Novel text
+# max_tokens = 256
+# print(prompt + m2.generate(prompt, steps=max_tokens))
+
+
+
+with open('tinyshakespeare.txt', "r", encoding='UTF-8') as f:
+    text = f.read()
+
+# Initialize model
+m = ROSAPlus(max_order=1048576, use_eot=False, seed=0) 
+m.train_example(text) # Train ROSA
+m.build_lm() # Train fallback predictor
+
+# Prompting
+prompt = "ROMEO:" # Novel text
+max_tokens = 256
+
+# Eval mode
+print(prompt + m.generate(prompt, steps=max_tokens))
+
+# Saving model
+m.save("rosa-model.json")
+m2 = ROSAPlus.load("rosa-model.json") # Loading model

readme.txt

@@ -0,0 +1,6 @@
+
+see https://github.com/zyaaa-ux/ROSA-Tuning
+
+
+
+

rosaplus.py

@@ -0,0 +1,746 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+ROSA Plus v2 (character-level)
+--------------------------------
+A boundary-aware Suffix Automaton (SAM) with a probabilistic fallback LM
+(Witten–Bell interpolation along suffix links). This module exposes a single
+high-level class `ROSAPlus` and supporting classes that let you:
+
+- Create a model object
+- Train on individual samples (examples) *without* crossing boundaries
+- Build the fallback LM (from accumulated examples) and keep the SAM frozen at inference
+- Save/load a JSON model
+- Provide a pre-existing context and:
+  * generate text (string) with sampling controls, or
+  * query the probability of the next character (or the full next-char distribution)
+
+This is a modular split of the original script into a library you can import.
+The companion CLI wrapper `rosa_plus_train.py` maintains the original flags.
+"""
+from __future__ import annotations
+from collections import deque
+from typing import List, Optional, Dict, Tuple
+from collections import defaultdict, Counter
+import json as _stdlib_json
+import math
+import random
+
+try:
+    import orjson as _json
+    _dumps = _json.dumps  # returns bytes
+    _loads = _json.loads
+except Exception:  # pragma: no cover
+    # Fallback to stdlib json (slower, but keeps things working)
+    _json = _stdlib_json
+    _dumps = lambda obj: _stdlib_json.dumps(obj).encode("utf-8")
+    _loads = lambda b: _stdlib_json.loads(b.decode("utf-8"))
+
+from tqdm import tqdm
+
+__all__ = [
+    "load_examples_from_file",
+    "ROSACharPredictor",
+    "ROSAFallbackLM",
+    "ROSAPlus",
+    "ROSAGRUAdapter",
+]
+
+
+# =========================
+# 1) Load & split examples
+# =========================
+
+def load_examples_from_file(
+    path: str,
+    delimiter: str = "<|ENDOFTEXT|>",
+    strip_each: bool = True,
+    use_eot: bool = True,
+    eot_char: str = "\u0004",  # single-char EOT (U+0004 by default)
+) -> List[str]:
+    """Reads a file, splits by `delimiter`, optional strip, drops empties.
+    If `use_eot`, appends a single-character EOT to each example.
+    """
+    if not isinstance(eot_char, str) or len(eot_char) != 1:
+        raise ValueError("eot_char must be a single character.")
+    with open(path, "r", encoding="utf-8") as f:
+        raw = f.read()
+    parts = raw.split(delimiter)
+    if strip_each:
+        parts = [p.strip() for p in parts]
+    parts = [p for p in parts if p]
+
+    if use_eot:
+        parts = [p + eot_char for p in parts]
+
+    return parts[:100000]
+
+
+# =========================
+# 2) Boundary-aware SAM
+# =========================
+
+class ROSACharPredictor:
+    """
+    Online/streaming SAM builder (ROSA core), boundary-aware.
+    Arrays:
+      - b: transitions (list[dict(char->next_state)])
+      - c: suffix links (list[int])
+      - d: max length (list[int])
+      - e: rightmost previous end position per state (list[int])
+      - g: last (active) state (int)
+    Additionally:
+      - text: list[str] training characters in sequence
+      - boundary_after[i] = True if an example boundary occurs immediately AFTER position i
+    """
+    def __init__(self):
+        self.b: List[Dict[str, int]] = [{}]  # transitions
+        self.c: List[int] = [-1]             # suffix links
+        self.d: List[int] = [0]              # max length
+        self.e: List[int] = [-1]             # rightmost previous indices
+        self.g: int = 0                      # last state
+        self.text: List[str] = []            # stores training text characters
+        self.boundary_after: List[bool] = [] # boundary flags per char index
+
+    def feed(self, ch: str) -> None:
+        """Extend SAM with `ch` (training step). Deterministic prediction is computed at inference."""
+        i = len(self.text)
+        self.text.append(ch)
+        if len(self.boundary_after) < len(self.text):
+            self.boundary_after.append(False)
+
+        b, c, d, e, g = self.b, self.c, self.d, self.e, self.g
+
+        # --- SAM extend ---
+        r = len(b)
+        b.append({})
+        c.append(0)
+        d.append(d[g] + 1)
+        e.append(-1)
+        p = g
+        while p != -1 and ch not in b[p]:
+            b[p][ch] = r
+            p = c[p]
+        if p == -1:
+            c[r] = 0
+        else:
+            q = b[p][ch]
+            if d[p] + 1 == d[q]:
+                c[r] = q
+            else:
+                # clone q -> u
+                u = len(b)
+                b.append(b[q].copy())
+                c.append(c[q])
+                d.append(d[p] + 1)
+                e.append(e[q])
+                while p != -1 and b[p].get(ch) == q:
+                    b[p][ch] = u
+                    p = c[p]
+                c[q] = c[r] = u
+        self.g = r
+
+        # --- update rightmost indices ---
+        v = self.g
+        while v != -1 and self.e[v] < i:
+            self.e[v] = i
+            v = self.c[v]
+
+    def mark_boundary(self) -> None:
+        """Call after finishing an example."""
+        if self.text:
+            self.boundary_after[len(self.text) - 1] = True
+        self.g = 0  # reset 'last' to root for a fresh example
+
+    # ----- Serialization -----
+    def to_state_dict(self) -> Dict:
+        return {
+            "b": self.b,
+            "c": self.c,
+            "d": self.d,
+            "e": self.e,
+            "g": self.g,
+            "text_str": "".join(self.text),
+            "boundary_after": self.boundary_after,
+        }
+
+    @classmethod
+    def from_state_dict(cls, obj: Dict) -> "ROSACharPredictor":
+        inst = cls()
+        inst.b = obj["b"]
+        inst.c = obj["c"]
+        inst.d = obj["d"]
+        inst.e = obj["e"]
+        inst.g = obj.get("g", 0)
+        inst.text = list(obj.get("text_str", ""))
+        inst.boundary_after = obj.get("boundary_after", [False] * len(inst.text))
+        if len(inst.boundary_after) < len(inst.text):
+            inst.boundary_after += [False] * (len(inst.text) - len(inst.boundary_after))
+        return inst
+
+
+# ===============================
+# 3) Walk a frozen SAM (inference)
+# ===============================
+
+def _advance_state(b, c, d, v: int, ch: str) -> int:
+    """Walk the trained SAM with character `ch` (no learning). Returns new state v'."""
+    while v != -1 and ch not in b[v]:
+        v = c[v]
+    if v == -1:
+        return b[0].get(ch, 0)
+    return b[v][ch]
+
+
+def _predict_from_state(
+    b, c, d, e, train_text: str, v: int, boundary_after: Optional[List[bool]] = None
+) -> Optional[str]:
+    """Deterministic ROSA next-char from current state v using rightmost indices.
+    Refuses to cross an example boundary.
+    """
+    u = v
+    n = len(train_text)
+    while u != -1:
+        i = e[u]
+        j = i + 1
+        if d[u] > 0 and 0 <= j < n:
+            if boundary_after is not None and 0 <= i < len(boundary_after) and boundary_after[i]:
+                u = c[u]
+                continue
+            return train_text[j]
+        u = c[u]
+    return None
+
+
+# ============================================
+# 4) Fallback LM that never crosses boundaries
+# ============================================
+
+class ROSAFallbackLM:
+    """Character LM using Witten–Bell interpolation down the SAM suffix chain.
+    IMPORTANT: counts are constructed per-example; no cross-boundary pairs.
+    """
+    def __init__(self, sam: ROSACharPredictor, examples: List[str], max_order: Optional[int] = None, show_progress: bool = True):
+        self.b, self.c, self.d, self.e = sam.b, sam.c, sam.d, sam.e
+        self.max_order = max_order
+        joined = "".join(examples)
+        self.alphabet = sorted(set(joined)) or ['\n']
+        self.freq: List[Dict[str, int]] = [defaultdict(int) for _ in range(len(self.b))]
+        self.unigram = Counter(joined) if joined else Counter({'\n': 1})
+        self._cache: Dict[int, Dict[str, float]] = {}
+        self._build_counts_examples_fast(examples, show_progress=show_progress)
+
+    def _build_counts_examples_fast(self, examples: List[str], show_progress: bool = True) -> None:
+        """Optimized: single pass per example, update-exclusion at the longest context,
+        optionally clamped by `max_order`.
+        """
+        total_pairs = sum(max(0, len(seg) - 1) for seg in examples if seg)
+        pbar = tqdm(total=total_pairs, desc="Training fallback LM (pairs)", disable=not show_progress, leave=False)
+
+        b, c, d = self.b, self.c, self.d
+        freq = self.freq
+        max_order = self.max_order
+
+        for seg in examples:
+            if not seg:
+                continue
+            v = 0  # root
+            for i in range(len(seg) - 1):
+                ch = seg[i]
+                # inline _advance_state
+                u = v
+                while u != -1 and ch not in b[u]:
+                    u = c[u]
+                if u == -1:
+                    v = b[0].get(ch, 0)
+                else:
+                    v = b[u][ch]
+
+                ctx = v
+                if max_order is not None:
+                    while ctx != -1 and d[ctx] > max_order:
+                        ctx = c[ctx]
+                    if ctx == -1:
+                        ctx = 0
+                nxt = seg[i + 1]
+                freq[ctx][nxt] += 1
+                pbar.update(1)
+
+        pbar.close()
+
+        self._propagate_counts_up_suffix_links()
+        # invalidate any cached distributions built before propagation
+        self._cache.clear()
+
+    def ensure_capacity(self) -> None:
+        """Make sure freq has one bucket per SAM state (called after SAM grows)."""
+        missing = len(self.b) - len(self.freq)
+        if missing > 0:
+            self.freq.extend(defaultdict(int) for _ in range(missing))
+
+    def observe_pair(self, ctx_state: int, next_ch: str, *, propagate: bool = True) -> None:
+        """
+        Online update: record one observation of (ctx_state -> next_ch).
+        If propagate=True we mirror the offline build's suffix-up accumulation.
+        """
+        # Keep alphabet fixed during online learning (simple path).
+        if next_ch not in self.alphabet:
+            return  # or raise if you prefer a hard failure
+
+        self.ensure_capacity()
+        self.freq[ctx_state][next_ch] += 1
+
+        if propagate:
+            u = self.c[ctx_state]
+            while u != -1:
+                self.freq[u][next_ch] += 1
+                u = self.c[u]
+
+        # Invalidate memoized distributions for this chain
+        u = ctx_state
+        while u != -1:
+            self._cache.pop(u, None)
+            u = self.c[u]
+
+    def _probs_for_state(self, v: int) -> Dict[str, float]:
+        """Witten–Bell interpolation down suffix links, memoized by state."""
+        if v in self._cache:
+            return self._cache[v]
+
+        # Build suffix chain (optionally truncated by max_order)
+        chain = []
+        u = v
+        while u != -1:
+            if self.max_order is not None and self.d[u] > self.max_order:
+                u = self.c[u]
+                continue
+            chain.append(u)
+            u = self.c[u]
+
+        residual = 1.0
+        probs: Dict[str, float] = {}
+
+        def add_counts(state: int, scale: float):
+            if scale <= 0.0:
+                return
+            total = sum(self.freq[state].values())
+            if total == 0:
+                return
+            inv_total = 1.0 / total
+            for ch, cnt in self.freq[state].items():
+                probs[ch] = probs.get(ch, 0.0) + scale * (cnt * inv_total)
+
+        for state in chain:
+            N = sum(self.freq[state].values())
+            T = len(self.freq[state])
+            if N == 0:
+                continue
+            lam = N / (N + T) if T > 0 else 1.0  # Witten–Bell
+            add_counts(state, residual * lam)
+            residual *= (1.0 - lam)
+
+        # Unigram fallback
+        total_uni = sum(self.unigram.values())
+        if total_uni > 0 and residual > 0.0:
+            inv_total = 1.0 / total_uni
+            for ch, cnt in self.unigram.items():
+                probs[ch] = probs.get(ch, 0.0) + residual * (cnt * inv_total)
+
+        s = sum(probs.values())
+        if s > 0:
+            inv_s = 1.0 / s
+            for k in list(probs.keys()):
+                probs[k] *= inv_s
+        else:
+            u = 1.0 / max(1, len(self.alphabet))
+            probs = {ch: u for ch in self.alphabet}
+
+        self._cache[v] = probs
+        return probs
+    
+    def _propagate_counts_up_suffix_links(self) -> None:
+        """
+        After filling self.freq only at the longest contexts, push counts up the
+        suffix-link tree so every shorter context has aggregated counts.
+        Process states in decreasing d[v] so children flow into parents.
+        """
+        order = sorted(range(len(self.b)), key=lambda v: self.d[v], reverse=True)
+        for v in order:
+            p = self.c[v]
+            if p < 0:      # root has no parent
+                continue
+            if not self.freq[v]:
+                continue
+            dv = self.freq[v]
+            dp = self.freq[p]
+            for ch, cnt in dv.items():
+                dp[ch] += cnt
+
+    @staticmethod
+    def _sample_from_dist(
+        dist: Dict[str, float],
+        temperature: float = 1.0,
+        top_p: Optional[float] = 0.9,
+        top_k: Optional[int] = None,
+    ) -> str:
+        if temperature <= 0:
+            temperature = 1e-6
+        items = sorted(dist.items(), key=lambda x: x[1], reverse=True)
+        if top_k is not None and top_k > 0:
+            items = items[:max(1, top_k)]
+        if top_p is not None:
+            cum, cut = 0.0, []
+            for ch, p in items:
+                cum += p
+                cut.append((ch, p))
+                if cum >= top_p:
+                    break
+            items = cut or items[:1]
+        logits = [math.log(max(p, 1e-12)) / temperature for _, p in items]
+        m = max(logits)
+        exps = [math.exp(z - m) for z in logits]
+        Z = sum(exps)
+        probs = [x / Z for x in exps]
+        idx = random.choices(range(len(items)), weights=probs, k=1)[0]
+        return items[idx][0]
+
+    # ----- Serialization -----
+    def to_state_dict(self) -> Dict:
+        freq_plain = [{k: int(v) for k, v in d.items()} for d in tqdm(self.freq, leave=False)]
+        return {
+            "alphabet": self.alphabet,
+            "unigram": {k: int(v) for k, v in self.unigram.items()},
+            "freq": freq_plain,
+            "max_order": self.max_order,
+        }
+
+    @classmethod
+    def from_state_dict(cls, sam: ROSACharPredictor, obj: Dict) -> "ROSAFallbackLM":
+        inst = cls.__new__(cls)  # bypass __init__
+        inst.b, inst.c, inst.d, inst.e = sam.b, sam.c, sam.d, sam.e
+        inst.max_order = obj.get("max_order", None)
+        inst.alphabet = obj["alphabet"]
+        inst.unigram = Counter({k: int(v) for k, v in obj["unigram"].items()})
+        inst.freq = [defaultdict(int, {k: int(v) for k, v in d.items()}) for d in tqdm(obj["freq"], leave=False)]
+        inst._cache = {}
+        return inst
+
+
+# ============================================
+# 5) Mixed generation: deterministic + fallback
+# ============================================
+
+def _generate_mixed(
+    sam: ROSACharPredictor,
+    lm: ROSAFallbackLM,
+    prompt: str,
+    max_steps: int = 200,
+    always_fallback = False,
+    stop_at: Optional[str] = None,
+    fallback_temperature: float = 1.0,
+    fallback_top_p: Optional[float] = 0.9,
+    fallback_top_k: Optional[int] = 50,
+) -> str:
+    b, c, d, e = sam.b, sam.c, sam.d, sam.e
+    train_text = "".join(sam.text)
+    v = 0
+    for ch in prompt:
+        v = _advance_state(b, c, d, v, ch)
+
+    out: List[str] = []
+    for _ in range(max_steps):
+        # 1) Deterministic ROSA
+        ch = None
+        if not always_fallback:
+            ch = _predict_from_state(b, c, d, e, train_text, v, boundary_after=sam.boundary_after)
+        if ch is None:
+            dist = lm._probs_for_state(v)
+            ch = ROSAFallbackLM._sample_from_dist(
+                dist, temperature=fallback_temperature, top_p=fallback_top_p, top_k=fallback_top_k
+            )
+        out.append(ch)
+        if stop_at is not None and ch == stop_at:
+            break
+        v = _advance_state(b, c, d, v, ch)
+
+    return "".join(out)
+
+
+# ============================================
+# 6) High-level wrapper class
+# ============================================
+
+MODEL_MAGIC = "rosa_pb_v2"  # v2 **only**
+
+class ROSAPlus:
+    """High-level model wrapper exposing a clean API.
+
+    Typical usage:
+        m = ROSAPlus(max_order=1048576, use_eot=True, eot_char="\u0004", seed=0)
+        m.train_example("hello world\u0004")  # or skip EOT if you set use_eot=False
+        m.build_lm()  # required before generation / probs
+        out = m.generate("he", steps=20, temperature=0.7)
+        p = m.next_char_prob("he", "l")
+        m.save("model.bin")
+        m2 = ROSAPlus.load("model.bin")
+    """
+
+    def __init__(self, *, max_order: Optional[int] = 1048576, use_eot: bool = True, eot_char: str = "\u0004", seed: int = 0):
+        if not isinstance(eot_char, str) or len(eot_char) != 1:
+            raise ValueError("eot_char must be a single character.")
+        self.max_order = max_order
+        self.use_eot = use_eot
+        self.eot_char = eot_char
+        self.seed = seed
+        random.seed(seed)
+
+        self.sam = ROSACharPredictor()
+        self._examples: List[str] = []
+        self.lm: Optional[ROSAFallbackLM] = None  # built later
+        self.neural: Optional["ROSAGRUAdapter"] = None  # optional GRU adapter
+
+    # ---- Training API ----
+    def train_example(self, example: str) -> None:
+        """Train on a single example string. Appends EOT if `use_eot` and it's not present as the final char."""
+        if not example:
+            return
+        if self.use_eot:
+            if example[-1] != self.eot_char:
+                example = example + self.eot_char
+        self._examples.append(example)
+        for ch in tqdm(example):
+            self.sam.feed(ch)
+        self.sam.mark_boundary()
+
+    def fit_from_examples(self, examples: List[str], *, show_progress: bool = True) -> None:
+        """Convenience: train on many examples and build LM."""
+        total_chars = sum(len(ex) for ex in examples)
+        with tqdm(total=total_chars, desc="Training SAM (ROSA) over chars", disable=not show_progress) as pbar:
+            for ex in examples:
+                self.train_example(ex)  # this already appends EOT if enabled
+                pbar.update(len(ex) if ex else 0)
+        self.build_lm(show_progress=show_progress)
+
+    def build_lm(self, *, show_progress: bool = True) -> None:
+        if not self._examples:
+            raise RuntimeError("No training examples available. Use train_example() first.")
+        self.lm = ROSAFallbackLM(self.sam, self._examples, max_order=self.max_order, show_progress=show_progress)
+
+    # ---- Optional neural adapter integration ----
+    def attach_gru_adapter(self, adapter: "ROSAGRUAdapter") -> None:
+        """Attach a trained (or untrained) GRU adapter."""
+        self.neural = adapter
+
+    def train_gru_adapter(
+        self,
+        *,
+        examples,
+        emb_dim: int = 128,
+        hidden_dim: int = 256,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        combine: str = "poe",
+        beta: float = 1.0,
+        device: Optional[str] = None,
+        epochs: int = 1,
+        lr: float = 1e-3,
+        max_tokens_per_step: int = 4096*10,
+        clip_grad: float = 1.0,
+        show_progress: bool = True,
+        # NEW:
+        online_sam: bool = False,
+        online_lm: bool = False,
+        propagate_updates: bool = True,
+    ) -> "ROSAGRUAdapter":
+        if self.lm is None:
+            raise RuntimeError("Fallback LM not built. Call build_lm() first.")
+        adapter = ROSAGRUAdapter(
+            self.lm.alphabet,
+            emb_dim=emb_dim,
+            hidden_dim=hidden_dim,
+            num_layers=num_layers,
+            dropout=dropout,
+            combine=combine,
+            beta=beta,
+            device=device,
+        )
+        adapter.fit(
+            self.sam,
+            self.lm,
+            examples,
+            eot_char=self.eot_char,
+            epochs=epochs,
+            lr=lr,
+            max_tokens_per_step=max_tokens_per_step,
+            clip_grad=clip_grad,
+            show_progress=show_progress,
+            online_sam=online_sam,
+            online_lm=online_lm,
+            propagate_updates=propagate_updates,
+        )
+        self.neural = adapter
+        return adapter
+
+    # ---- Inference API ----
+    def generate(
+        self,
+        prompt: str,
+        *,
+        steps: int = 200,
+        always_fallback = False,
+        stop_at: Optional[str] = None,
+        temperature: float = 0.5,
+        top_p: Optional[float] = 0.9,
+        top_k: Optional[int] = 50,
+        use_gru_adapter: bool = True,  # enable/disable neural reweighting
+    ) -> str:
+        """
+        Generate a continuation from `prompt`.
+
+        Order of operations per token:
+        1) Try deterministic ROSA next-char (won't cross boundaries).
+        2) Otherwise sample from fallback LM; if a GRU adapter is attached,
+            refine the LM distribution first. The adapter consumes one base
+            distribution per token; we *always* call step_with_char(ch) after
+            emitting a char—this becomes a no-op if refine_distribution() was
+            called in the same tick (the adapter guards against double steps).
+        """
+        if self.lm is None:
+            raise RuntimeError("Fallback LM not built. Call build_lm() after training.")
+
+        if stop_at is None and self.use_eot:
+            stop_at = self.eot_char
+        if top_k is not None and top_k <= 0:
+            top_k = None
+
+        # Unpack SAM internals
+        b, c, d, e = self.sam.b, self.sam.c, self.sam.d, self.sam.e
+        train_text = "".join(self.sam.text)
+
+        # Walk SAM with the prompt to get the starting state
+        v = 0
+        for ch in prompt:
+            v = _advance_state(b, c, d, v, ch)
+
+        # Prime GRU with the prompt by consuming base distributions along the path
+        if use_gru_adapter and self.neural is not None:
+            try:
+                self.neural.reset()
+                v_prime = 0
+                for ch in prompt:
+                    v_prime = _advance_state(b, c, d, v_prime, ch)
+                    base = self.lm._probs_for_state(v_prime)
+                    if hasattr(self.neural, "step_with_dist"):
+                        self.neural.step_with_dist(base)
+            except Exception:
+                # Priming is best-effort; fall back silently if adapter errors
+                pass
+
+        out: List[str] = []
+        for _ in range(steps):
+            # 1) Deterministic ROSA (if allowed)
+            ch = None
+            if not always_fallback:
+                ch = _predict_from_state(
+                    b, c, d, e, train_text, v, boundary_after=self.sam.boundary_after
+                )
+
+            # 2) Otherwise sample from (possibly GRU-refined) fallback LM
+            if ch is None:
+                base_dist = self.lm._probs_for_state(v)
+                if use_gru_adapter and self.neural is not None and hasattr(self.neural, "refine_distribution"):
+                    try:
+                        dist = self.neural.refine_distribution(base_dist)  # consumes one step
+                    except Exception:
+                        dist = base_dist
+                else:
+                    dist = base_dist
+
+                ch = ROSAFallbackLM._sample_from_dist(
+                    dist, temperature=temperature, top_p=top_p, top_k=top_k
+                )
+
+            out.append(ch)
+            if stop_at is not None and ch == stop_at:
+                break
+
+            # Advance SAM (and adapter) with the emitted char
+            v = _advance_state(b, c, d, v, ch)
+            if use_gru_adapter and self.neural is not None and hasattr(self.neural, "step_with_char"):
+                try:
+                    # If refine_distribution() already stepped this tick, this is a no-op.
+                    self.neural.step_with_char(ch)
+                except Exception:
+                    pass
+
+        return "".join(out)
+
+    def get_dist(self, context: str, deterministic=False) -> Dict[str, float]:
+        """Return a dict of next-char probabilities given `context`.
+        If deterministic=True: Get direct prediction instead of probability (no fallback)
+        """
+        if self.lm is None:
+            raise RuntimeError("Fallback LM not built. Call build_lm() after training.")
+        b, c, d, e = self.sam.b, self.sam.c, self.sam.d, self.sam.e
+        train_text = "".join(self.sam.text)
+        v = 0
+        for ch in context:
+            v = _advance_state(b, c, d, v, ch)
+            if deterministic:
+                det = _predict_from_state(b, c, d, e, train_text, v, boundary_after=self.sam.boundary_after)
+                if det is not None:
+                    return {det: 1.0}
+        base = self.lm._probs_for_state(v)
+        # If neural adapter is attached, offer a reweighted distribution helper-style
+        if self.neural is not None and hasattr(self.neural, "refine_distribution"):
+            try:
+                return self.neural.refine_distribution(base)
+            except Exception:
+                return base
+        return base
+
+    # ---- Persistence ----
+    def save(self, path: str) -> None:
+        if self.lm is None:
+            raise RuntimeError("Fallback LM not built. Call build_lm() before saving.")
+        payload = {
+            "magic": MODEL_MAGIC,
+            "sam": self.sam.to_state_dict(),
+            "lm": self.lm.to_state_dict(),
+            "meta": {
+                "use_eot": self.use_eot,
+                "eot_char": self.eot_char,
+                "max_order": self.max_order,
+                "seed": self.seed,
+            },
+            # Note: neural adapter is NOT serialized here by default to avoid
+            # adding a framework dependency into the model blob.
+        }
+        with open(path, "wb") as f:
+            f.write(_dumps(payload))
+
+    @classmethod
+    def load(cls, path: str) -> "ROSAPlus":
+        with open(path, "rb") as f:
+            payload = _loads(f.read())
+        magic = payload.get("magic")
+        if magic != MODEL_MAGIC:
+            raise ValueError(f"Unrecognized or unsupported model magic in {path}: {magic} (v2 only)")
+        meta = payload.get("meta", {})
+        inst = cls(
+            max_order=meta.get("max_order", 1048576),
+            use_eot=meta.get("use_eot", True),
+            eot_char=meta.get("eot_char", "\u0004"),
+            seed=meta.get("seed", 0),
+        )
+        inst.sam = ROSACharPredictor.from_state_dict(payload["sam"])  # type: ignore[arg-type]
+        inst.lm = ROSAFallbackLM.from_state_dict(inst.sam, payload["lm"])  # type: ignore[arg-type]
+        return inst
+
+    # ---- Utilities ----
+    @staticmethod
+    def decode_escape(s: str) -> str:
+        try:
+            return s.encode("utf-8").decode("unicode_escape")
+        except Exception:
+            return s