test model thing, finally

.gitignore

@@ -0,0 +1,9 @@
+/.venv/
+/typings/
+/wikipedia_clean/
+/CoLA/
+*.xml.bz2
+/__pycache__/
+*.pipe
+*.DS_Store
+*.safetensors

LICENSE.md

@@ -0,0 +1,7 @@
+Copyright (c) 2026 jrz97619761
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -0,0 +1,11 @@
+Hey! Thanks for being here.
+
+Here's the video, if you came here from somewhere else -> [Video](https://example.com)
+
+The semi-trained 4.5m model (it's not done, but also it's in millions of parameters, not billions) is available for you to use. So are the train and benchmark scripts (using MLX, but you can port to other platforms if you want).
+
+However a larger 130m model is not provided, it is too large for GitHub to store. You can train it yourself and perhaps share it on a cloud storage provider instead.
+
+Note that datasets are not included, and if you use a non-puretext dataset like wikipedia dump then feel free to write your own dataset extraction code or use an existing library.
+
+The repo is MIT license, so feel free to fork the repo, I would be very happy to see that. Go ahead and explore!

benchmark.py

@@ -0,0 +1,79 @@
+import mlx.core as mx
+import mlx.nn as nn
+import mlx.optimizers as opt
+import mlx.utils as util
+
+from main import Model
+
+class Classification(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.proj = nn.Linear(dim, 2)
+
+    def __call__(self, x: mx.array): return self.proj(x)
+
+def cola(filepath: str):
+    data = []
+    with open(filepath, 'r', encoding = 'utf-8') as f:
+        for line in f:
+            parts = line.strip().split('\t')
+            if len(parts) == 4: data.append((parts[3].encode('utf-8'), int(parts[1])))
+    return data
+
+def mcc(tp, tn, fp, fn):
+    import math
+    denominator = math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+    return (tp * tn - fp * fn) / denominator if denominator != 0 else 0.0
+
+def run():
+    model = Model(dim = 512, layers = 16, temp = 0.75, lr = 5e-4)
+    model.load('smaller-4.5m.safetensors')
+    model.freeze()
+
+    head = Classification(model.dim)
+    headopt = opt.AdamW(learning_rate = 1e-3)
+
+    data = cola('CoLA/original/raw/in_domain_train.tsv')
+
+    def l(params, state: mx.array, target: int):
+        head.update(params)
+        choice = head(state)
+
+        loss = nn.losses.cross_entropy(choice[None, :], mx.array([target])).mean()
+        return loss, choice
+
+    for epoch in range(3):
+        print(f'\nEpoch {epoch + 1}')
+
+        dummies = [mx.zeros((model.dim, )) for _ in range(model.layercount)]
+        tp, tn, fp, fn = 0, 0, 0, 0
+        
+        for i, (bytes, label) in enumerate(data):
+            final = None
+            for b in bytes:
+                x = model.encoder(mx.array(b))
+                for j, layer in enumerate(model.layers):
+                    x, state, _ = layer(x, dummies[j])
+                    layer.states = mx.stop_gradient(state)
+                
+                final = model.layers[-1].states
+
+            (_, choice), grads = mx.value_and_grad(l, argnums = 0)(head.trainable_parameters(), final, label)
+
+            headopt.update(head, grads)
+            mx.eval(head.parameters(), headopt.state)
+
+            predicted_class = mx.argmax(choice).item()
+            if predicted_class == 1 and label == 1: tp += 1
+            elif predicted_class == 0 and label == 0: tn += 1
+            elif predicted_class == 1 and label == 0: fp += 1
+            elif predicted_class == 0 and label == 1: fn += 1
+
+            score = mcc(tp, tn, fp, fn)
+
+            if i > 0 and i % 500 == 0: print(f'{i + 1}: TP, TN, FP, FN | {tp}, {tn}, {fp}, {fn} ({score})')
+
+        print(f'{i + 1}: TP, TN, FP, FN | {tp}, {tn}, {fp}, {fn} ({score})')
+
+if __name__ == '__main__':
+    run()

main.py

@@ -0,0 +1,220 @@
+import mlx.core as mx
+import mlx.nn as nn
+import mlx.optimizers as opt
+import mlx.utils as util
+
+class Encoder(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.embed = nn.Embedding(256, dim)
+        self.embedtrace = mx.zeros((256, dim))
+
+    def __call__(self, x: mx.array): return self.embed(x)
+
+class Decoder(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.decode = nn.Linear(dim, 256)
+        self.stop = nn.Linear(dim, 1)
+
+    def __call__(self, x: mx.array): return self.decode(x), mx.sigmoid(self.stop(x))
+
+class Layer(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        
+        self.decay = mx.zeros((dim, ))
+        self.states = mx.zeros((dim, ))
+        self.decaytrace = mx.zeros((dim, ))
+        
+        self.norm = nn.LayerNorm(dim)
+        self.weights = nn.Linear(dim, dim, bias = False)
+        self.silu = nn.SiLU()
+
+    def __call__(self, x: mx.array, dummy: mx.array):
+        decay = mx.sigmoid(self.decay)
+        state = (decay * self.states) + x + dummy
+
+        return x + self.silu(self.weights(self.norm(state))), state, decay
+
+class Model(nn.Module):
+    def __init__(self, dim: int, layers: int, temp: float, lr: float):
+        super().__init__()
+        self.dim = dim
+        self.layercount = layers
+        self.temp = temp
+
+        self.encoder = Encoder(dim)
+        self.decoder = Decoder(dim)
+
+        self.layers = [Layer(dim) for _ in range(layers)]
+        self.optimizer = opt.AdamW(learning_rate = lr)
+
+    def sample(self, output: mx.array):
+        probs = mx.softmax(output)
+        entropy = -mx.sum(probs * mx.log(probs + 1e-8)) / mx.log(mx.array(256))
+
+        temp = mx.maximum(0.1, self.temp * (1.0 - self.temp * entropy)).item()
+        return mx.random.categorical(output / temp)
+
+    def __call__(self, currb: int, nextb: int | None, end: bool):
+        c = mx.array(currb)
+        p = self.trainable_parameters()
+
+        def fwd(params, dummies: list[mx.array]):
+            self.update(params)
+            x = self.encoder(c)
+
+            states, decays = [], []
+
+            for i, layer in enumerate(self.layers):
+                x, state, decay = layer(x, dummies[i])
+
+                states.append(state)
+                decays.append(decay)
+
+            output, stop = self.decoder(x)
+
+            loss = mx.maximum(0.0, 1.0 - mx.sqrt(mx.var(x) + 1e-4))
+            if nextb is not None:
+                n = mx.array(nextb)
+                tgt = mx.stop_gradient(self.encoder(n))
+
+                loss = loss + mx.mean(mx.square(x - tgt))
+                loss = loss - output[n] + mx.logsumexp(output)
+
+                loss = loss + mx.mean(mx.square(stop - mx.array([1.0 if end else 0.0])))
+
+            return loss, (states, decays, output, stop) # loss = variance loss + pred mse loss + crossentropy loss + stop mse loss
+
+        (_, (states, decays, output, stop)), (grads, dlds_s) = mx.value_and_grad(
+            fwd, argnums = (0, 1)
+        )(p, [mx.zeros((self.dim, )) for _ in range(self.layercount)])
+
+        self.update(p)
+
+        embedtrace = (self.encoder.embedtrace * decays[0]) + (mx.arange(256) == c)[:, None]
+        grads["encoder"]["embed"]["weight"] += dlds_s[0] * (self.encoder.embedtrace * decays[0])
+        self.encoder.embedtrace = mx.stop_gradient(embedtrace)
+        mx.eval(self.encoder.embedtrace)
+
+        for i, layer in enumerate(self.layers):
+            dlds = dlds_s[i]
+            
+            decaytrace = (decays[i] * layer.decaytrace) + (decays[i] * (1.0 - decays[i]) * layer.states)
+
+            grads["layers"][i]["decay"] = dlds * decaytrace
+
+            layer.states = mx.stop_gradient(states[i])
+            layer.decaytrace = mx.stop_gradient(decaytrace)
+
+            mx.eval(layer.states, layer.decaytrace)
+
+        self.optimizer.update(self, grads)
+        mx.eval(self.parameters(), self.optimizer.state)
+
+        return self.sample(output).item(), stop.item()
+
+    def save(self, path: str):
+        data = {}
+        for k, v in util.tree_flatten(self.parameters()): data[f"m.{k}"] = v
+        for k, v in util.tree_flatten(self.optimizer.state): data[f"o.{k}"] = v
+
+        data[f"embedtrace"] = self.encoder.embedtrace
+        for i, layer in enumerate(self.layers):
+            data[f"state.{i}"] = layer.states
+            data[f"decaytrace.{i}"] = layer.decaytrace
+            
+        mx.save_safetensors(path, data)
+
+    def load(self, path: str):
+        import os
+        if not os.path.exists(path): return
+
+        data, model, opts = mx.load(path), {}, {}
+        
+        for k, v in data.items():
+            if k.startswith("m."): model[k[2:]] = v
+            elif k.startswith("o."): opts[k[2:]] = v
+            elif k.startswith("state."): self.layers[int(k.split('.')[1])].states = v
+            elif k.startswith("decaytrace."): self.layers[int(k.split('.')[1])].decaytrace = v
+            elif k == "embedtrace": self.encoder.embedtrace = v
+            
+        if model: self.update(util.tree_unflatten(list(model.items())))
+        if opts: self.optimizer.state = util.tree_unflatten(list(opts.items()))
+
+class Runtime:
+    def __init__(self, path: str, threshold: float, **kwargs):
+        self.model = Model(**kwargs)
+        self.path = path
+        self.threshold = threshold
+        self.step = 0
+
+    def save(self):
+        self.step += 1
+        if self.step % 500 == 0: self.model.save(self.path)
+
+    def call(self, c: int, n: int | None, end: bool):
+        outputs = self.model(c, n, end)
+        self.save()
+        return outputs
+
+    def write(self, b: int):
+        import sys
+        sys.stdout.buffer.write(bytes([b]))
+        sys.stdout.flush()
+
+    def chat(self):
+        import itertools
+
+        while True:
+            text = input(f'\n[{self.now()}]\nUser >> ')
+            data = (text + '\n').encode('utf-8')
+            
+            for i, (c, n) in enumerate(itertools.pairwise(data)):
+                b, _ = self.call(c, n, i == len(data) - 2)
+
+            print(f'\n[{self.now()}]\nModel >> ', end = '', flush = True)
+
+            b = data[-1]
+            while True:
+                b, stop = self.call(b, None, False)
+                self.write(b)
+                if stop > self.threshold: break
+
+    def dataset(self):
+        import glob, itertools
+        files = glob.glob('wikipedia_clean/**/wiki_*', recursive = True)
+
+        while True:
+            for file in files:
+                with open(file, 'r', encoding = 'utf-8', errors = 'ignore') as f:
+                    for line in f:
+                        data = line.encode('utf-8')
+                        for i, (c, n) in enumerate(itertools.pairwise(data)):
+                            b, _ = self.call(c, n, i == len(data) - 2)
+                            self.write(b)
+
+    def now(self):
+        from datetime import datetime
+        return datetime.now().strftime('%d/%m/%Y, %H:%M:%S')
+
+    def __call__(self):
+        try: mode = bool(int(input(f'\n[{self.now()}]\nmode >> ')))
+        except ValueError:
+            print('\nInvalid mode.')
+            return
+
+        self.model.load(self.path)
+        print()
+
+        try:
+            match mode:
+                case False: self.dataset()
+                case True: self.chat()
+
+        finally: self.model.save(self.path)
+
+if __name__ == '__main__':
+    Runtime(path = 'larger-130m.safetensors', threshold = 0.35, dim = 2048, layers = 32, temp = 0.75, lr = 5e-4)()
+    # param count = (256 * dim) + (dim * dim + dim * 2 + dim) + (256 * dim + dim + 1)