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README.md

@@ -0,0 +1,38 @@
+---
+language: en
+tags:
+  - egpt
+  - llama-architecture
+  - decoder-only
+  - untrained
+license: mit
+---
+
+# eGPT-3B-untrained
+
+Randomly initialized eGPT decoder-only model (3.22B parameters). **Not trained.**
+
+## Architecture
+
+| Field | Value |
+|---|---|
+| Parameters | 3.22B |
+| Layers | 32 |
+| Dim | 3072 |
+| Heads (Q) | 24 |
+| Heads (KV) | 8 |
+| Head dim | 128 |
+| FFN hidden | 8192 |
+| Max seq len | 2048 |
+| Vocab size | 256 |
+| Tokenizer | `google/byt5-small` |
+
+## Loading
+
+```python
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+
+tok   = AutoTokenizer.from_pretrained("LLMsHub/eGPT-3B-untrained", trust_remote_code=True)
+cfg   = AutoConfig.from_pretrained("LLMsHub/eGPT-3B-untrained", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("LLMsHub/eGPT-3B-untrained", trust_remote_code=True)
+```

added_tokens.json

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config.json

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+{
+  "model_type": "egpt",
+  "architectures": [
+    "eGPTForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_egpt.eGPTConfig",
+    "AutoModelForCausalLM": "modeling_egpt.eGPTForCausalLM"
+  },
+  "vocab_size": 256,
+  "dim": 3072,
+  "n_layers": 32,
+  "n_heads": 24,
+  "n_kv_heads": 8,
+  "head_dim": null,
+  "ffn_multiplier": 4.0,
+  "multiple_of": 256,
+  "max_seq_len": 2048,
+  "norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "weight_tying": false
+}

configuration_egpt.py

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+from transformers import PreTrainedConfig
+
+
+class eGPTConfig(PreTrainedConfig):
+    model_type = "egpt"
+
+    def __init__(
+        self,
+        vocab_size: int = 256,
+        dim: int = 768,
+        n_layers: int = 12,
+        n_heads: int = 12,
+        n_kv_heads: int = 12,
+        head_dim: int = None,
+        ffn_multiplier: float = 4.0,
+        multiple_of: int = 256,
+        max_seq_len: int = 2048,
+        norm_eps: float = 1e-5,
+        rope_theta: float = 10000.0,
+        weight_tying: bool = False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.dim = dim
+        self.n_layers = n_layers
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        self.head_dim = head_dim
+        self.ffn_multiplier = ffn_multiplier
+        self.multiple_of = multiple_of
+        self.max_seq_len = max_seq_len
+        self.norm_eps = norm_eps
+        self.rope_theta = rope_theta
+        self.weight_tying = weight_tying

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e66d6146bd1305c45eaa5a037a10d46187dc09ac2ee04731ce123eb94310b78
+size 12892021360

modeling_egpt.py

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+"""eGPT: LLaMA-style decoder-only model — self-contained HuggingFace implementation.
+
+Architecture: RMSNorm, RoPE, Grouped-Query Attention, SwiGLU FFN, no bias.
+Weight keys match eGPT/model.py exactly for checkpoint compatibility.
+"""
+
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+try:
+    from .configuration_egpt import eGPTConfig
+except ImportError:
+    from configuration_egpt import eGPTConfig
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
+
+
+def _precompute_freqs_cis(head_dim: int, max_seq_len: int, theta: float) -> torch.Tensor:
+    freqs = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
+    t = torch.arange(max_seq_len, dtype=torch.float32)
+    freqs = torch.outer(t, freqs)
+    return torch.polar(torch.ones_like(freqs), freqs)
+
+
+def _apply_rotary_emb(xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor):
+    def rotate(x):
+        xc = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+        f = freqs_cis.view(1, x.shape[1], 1, freqs_cis.shape[-1])
+        return torch.view_as_real(xc * f).flatten(-2).type_as(x)
+    return rotate(xq), rotate(xk)
+
+
+class Attention(nn.Module):
+    def __init__(self, cfg: eGPTConfig):
+        super().__init__()
+        self.n_heads = cfg.n_heads
+        self.n_kv_heads = cfg.n_kv_heads
+        self.head_dim = cfg.head_dim or (cfg.dim // cfg.n_heads)
+        self.n_rep = cfg.n_heads // cfg.n_kv_heads
+
+        self.wq = nn.Linear(cfg.dim, cfg.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(cfg.n_heads * self.head_dim, cfg.dim, bias=False)
+
+    def forward(self, x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+        B, T, _ = x.shape
+        xq = self.wq(x).view(B, T, self.n_heads, self.head_dim)
+        xk = self.wk(x).view(B, T, self.n_kv_heads, self.head_dim)
+        xv = self.wv(x).view(B, T, self.n_kv_heads, self.head_dim)
+        xq, xk = _apply_rotary_emb(xq, xk, freqs_cis[:T])
+        if self.n_rep > 1:
+            xk = xk.repeat_interleave(self.n_rep, dim=2)
+            xv = xv.repeat_interleave(self.n_rep, dim=2)
+        out = F.scaled_dot_product_attention(
+            xq.transpose(1, 2), xk.transpose(1, 2), xv.transpose(1, 2), is_causal=True
+        )
+        return self.wo(out.transpose(1, 2).contiguous().view(B, T, -1))
+
+
+class FeedForward(nn.Module):
+    def __init__(self, cfg: eGPTConfig):
+        super().__init__()
+        hidden = int(cfg.dim * cfg.ffn_multiplier * 2 / 3)
+        hidden = cfg.multiple_of * math.ceil(hidden / cfg.multiple_of)
+        self.w1 = nn.Linear(cfg.dim, hidden, bias=False)
+        self.w2 = nn.Linear(hidden, cfg.dim, bias=False)
+        self.w3 = nn.Linear(cfg.dim, hidden, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, cfg: eGPTConfig):
+        super().__init__()
+        self.attn_norm = RMSNorm(cfg.dim, cfg.norm_eps)
+        self.attn = Attention(cfg)
+        self.ffn_norm = RMSNorm(cfg.dim, cfg.norm_eps)
+        self.ffn = FeedForward(cfg)
+
+    def forward(self, x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+        x = x + self.attn(self.attn_norm(x), freqs_cis)
+        x = x + self.ffn(self.ffn_norm(x))
+        return x
+
+
+class eGPTForCausalLM(PreTrainedModel, GenerationMixin):
+    config_class = eGPTConfig
+
+    def __init__(self, config: eGPTConfig):
+        super().__init__(config)
+        head_dim = config.head_dim or (config.dim // config.n_heads)
+
+        self.embed = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layers)])
+        self.norm = RMSNorm(config.dim, config.norm_eps)
+        self.head = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+        if config.weight_tying:
+            self.head.weight = self.embed.weight
+
+        freqs_cis = _precompute_freqs_cis(head_dim, config.max_seq_len * 2, config.rope_theta)
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed
+
+    def set_input_embeddings(self, value):
+        self.embed = value
+
+    def get_output_embeddings(self):
+        return self.head
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> CausalLMOutputWithPast:
+        x = self.embed(input_ids)
+        for layer in self.layers:
+            x = layer(x, self.freqs_cis)
+        x = self.norm(x)
+        logits = self.head(x)
+
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(
+                logits.view(-1, logits.size(-1)).float(),
+                labels.reshape(-1),
+                ignore_index=-100,
+            )
+
+        return CausalLMOutputWithPast(loss=loss, logits=logits)

tokenizer_config.json

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+{
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+      "special": true
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+      "rstrip": false,
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+    "2": {
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