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config (14).json

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+{
+  "architectures": [
+    "DuchifatCore"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_duchifat_v2.DuchifatConfig",
+    "AutoModelForCausalLM": "modeling_duchifat_v2.DuchifatCore"
+  },
+  "dtype": "bfloat16",
+  "hidden_size": 768,
+  "max_seq": 1024,
+  "model_type": "duchifat_v2",
+  "nhead": 12,
+  "num_layers": 12,
+  "transformers_version": "5.2.0",
+  "vocab_size": 33152
+}

configuration_duchifat_v2.py

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+from transformers import PretrainedConfig
+
+class DuchifatConfig(PretrainedConfig):
+    model_type = "duchifat_v2"
+
+    def __init__(
+        self,
+        vocab_size=50257,
+        hidden_size=768,
+        num_layers=12,
+        nhead=12,
+        max_seq=1024,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.nhead = nhead
+        self.max_seq = max_seq

generation_config (6).json

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+{
+  "_from_model_config": true,
+  "transformers_version": "5.2.0"
+}

model (12).safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7b633de8690e452d782eb01d841c2ab2ca9a61eeaf67200458669341b04365b8
+size 273541208

modeling_duchifat_v2.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutput
+from .configuration_duchifat_v2 import DuchifatConfig
+
+class DuchifatBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(config.hidden_size)
+        self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size)
+        self.wo = nn.Linear(config.hidden_size, config.hidden_size)
+        self.ln2 = nn.LayerNorm(config.hidden_size)
+        self.mlp = nn.Sequential(
+            nn.Linear(config.hidden_size, 4 * config.hidden_size),
+            nn.GELU(approximate='tanh'),
+            nn.Linear(4 * config.hidden_size, config.hidden_size)
+        )
+        self.n_head = config.nhead
+        self.head_dim = config.hidden_size // config.nhead
+
+    def forward(self, x):
+        norm_x = self.ln1(x)
+        B, T, C = norm_x.size()
+        qkv = self.qkv(norm_x).view(B, T, 3, self.n_head, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        # Flash Attention (SDPA)
+        attn_out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        attn_out = attn_out.transpose(1, 2).contiguous().view(B, T, C)
+
+        x = x + self.wo(attn_out)
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+class DuchifatPreTrainedModel(PreTrainedModel):
+    config_class = DuchifatConfig
+    base_model_prefix = "model"
+    _no_split_modules = ["DuchifatBlock"]
+
+class DuchifatCore(DuchifatPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.wpe = nn.Embedding(config.max_seq, config.hidden_size)
+        self.blocks = nn.ModuleList([DuchifatBlock(config) for _ in range(config.num_layers)])
+        self.ln_f = nn.LayerNorm(config.hidden_size)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, value):
+        self.wte = value
+
+    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
+        # טיפול במקרה שבו input_ids לא נשלח כראוי
+        if input_ids is None:
+            raise ValueError("You must specify input_ids")
+            
+        B, T = input_ids.size()
+        device = input_ids.device
+        
+        # בניית פוזיציות (Absolute Positional Embeddings)
+        pos = torch.arange(0, T, dtype=torch.long, device=device)
+        
+        x = self.wte(input_ids) + self.wpe(pos)
+
+        for block in self.blocks:
+            x = block(x)
+
+        logits = self.lm_head(self.ln_f(x))
+
+        loss = None
+        if labels is not None:
+            # Shift logits/labels עבור Causal Language Modeling (הזזה של 1 ימינה)
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss = F.cross_entropy(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
+
+        return CausalLMOutput(
+            loss=loss,
+            logits=logits
+        )
+
+    # פונקציה חיונית שמאפשרת ל-generate לעבוד
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs):
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask
+        }
+
+    # תמיכה ב-Beam Search ובדיקות קאש בסיסיות
+    def _reorder_cache(self, past_key_values, beam_idx):
+        return past_key_values

tokenizer_config (14).json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "is_local": false,
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}