Initial release: TinyWay 1.0.0 (52.94M params)

__init__.py

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+from .modeling_tinyway import TinyWayConfig, TinyWayForCausalLM

config.json

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+{
+  "model_type": "tinyway",
+  "architectures": [
+    "TinyWayForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_tinyway.TinyWayConfig",
+    "AutoModelForCausalLM": "modeling_tinyway.TinyWayForCausalLM"
+  },
+  "vocab_size": 50257,
+  "n_positions": 256,
+  "n_embd": 384,
+  "n_layer": 8,
+  "n_head": 8,
+  "dropout": 0.1,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "pad_token_id": 50256
+}

generation_config.json

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+{
+  "max_new_tokens": 256,
+  "do_sample": true,
+  "temperature": 0.8,
+  "top_p": 0.95,
+  "top_k": 50,
+  "repetition_penalty": 1.1,
+  "eos_token_id": 50256,
+  "pad_token_id": 50256
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0bb66c2d2f46d420d5b0756c68282527ba39392258af7963e29a93becfce3da
+size 213876668

modeling_tinyway.py

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+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.generation.utils import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutput
+
+
+# =========================
+# Config
+# =========================
+
+class TinyWayConfig(PretrainedConfig):
+    model_type = "tinyway"
+
+    def __init__(
+        self,
+        vocab_size=50257,
+        n_positions=256,
+        n_embd=384,
+        n_layer=8,
+        n_head=8,
+        dropout=0.1,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        # --- original fields ---
+        self.vocab_size = vocab_size
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.dropout = dropout
+
+        # --- HF standard aliases (CRITICAL) ---
+        self.hidden_size = n_embd
+        self.num_hidden_layers = n_layer
+        self.num_attention_heads = n_head
+        self.max_position_embeddings = n_positions
+
+
+
+# =========================
+# Attention
+# =========================
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+
+        self.n_head = config.n_head
+        self.head_dim = config.n_embd // config.n_head
+
+        self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd)
+        self.proj = nn.Linear(config.n_embd, config.n_embd)
+
+        self.register_buffer(
+            "mask",
+            torch.tril(torch.ones(config.n_positions, config.n_positions))
+        )
+
+    def forward(self, x):
+        B, T, C = x.shape
+
+        qkv = self.qkv(x)
+        q, k, v = qkv.chunk(3, dim=-1)
+
+        q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+
+        att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        att = att.masked_fill(self.mask[:T, :T] == 0, float("-inf"))
+        att = F.softmax(att, dim=-1)
+
+        out = att @ v
+        out = out.transpose(1, 2).contiguous().view(B, T, C)
+
+        return self.proj(out)
+
+
+# =========================
+# Transformer Block
+# =========================
+
+class DecoderBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attn = CausalSelfAttention(config)
+        self.ffn = nn.Sequential(
+            nn.Linear(config.n_embd, 4 * config.n_embd),
+            nn.GELU(),
+            nn.Linear(4 * config.n_embd, config.n_embd)
+        )
+        self.ln1 = nn.LayerNorm(config.n_embd)
+        self.ln2 = nn.LayerNorm(config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = x + self.dropout(self.attn(self.ln1(x)))
+        x = x + self.dropout(self.ffn(self.ln2(x)))
+        return x
+
+
+# =========================
+# Model
+# =========================
+
+class TinyWayForCausalLM(PreTrainedModel, GenerationMixin):
+    config_class = TinyWayConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.token_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        self.pos_emb = nn.Embedding(config.n_positions, config.n_embd)
+
+        self.blocks = nn.ModuleList(
+            [DecoderBlock(config) for _ in range(config.n_layer)]
+        )
+
+        self.ln = nn.LayerNorm(config.n_embd)
+
+        # MUST match training
+        self.head = nn.Linear(config.n_embd, config.vocab_size)
+
+        self.post_init()
+
+    # ---- HF REQUIRED METHODS ----
+    def get_input_embeddings(self):
+        return self.token_emb
+
+    def set_input_embeddings(self, value):
+        self.token_emb = value
+
+    # ---- Forward ----
+    def forward(self, input_ids, **kwargs):
+        B, T = input_ids.shape
+        pos = torch.arange(T, device=input_ids.device)
+
+        x = self.token_emb(input_ids) + self.pos_emb(pos)
+
+        for block in self.blocks:
+            x = block(x)
+
+        x = self.ln(x)
+        logits = self.head(x)
+
+        return CausalLMOutput(logits=logits)

special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}