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chat_template.jinja

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+{% for message in messages %}
+{% if message['role'] == 'assistant' %}
+{{ '<|im_start|>assistant\n' }}
+{% generation %}
+{{ message['content'] }}
+{% endgeneration %}
+{{ '<|im_end|>' }}
+{% else %}
+{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>'}}
+{% endif %}
+{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "NeuroBLASTForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_neuroblast.NeuroBLASTConfig",
+    "AutoModelForCausalLM": "modeling_neuroblast.NeuroBLASTForCausalLM"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_every": 0,
+  "dropout": 0.0,
+  "dtype": "float32",
+  "hidden_act": "silu",
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "kernel_size": 5,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "neuroblast",
+  "num_associative_layers": 32,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 72,
+  "num_key_value_heads": 8,
+  "num_motor_layers": 16,
+  "num_sensory_layers": 24,
+  "pad_token_id": 65537,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "scale": 1.0,
+  "sliding_window": null,
+  "temporal_kernel_size": 5,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 65538
+}

configuration_neuroblast.py

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+# coding=utf-8
+# Copyright 2025 Mariusz Kurman, MedIT Solutions Sp. z o.o, Poland. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""NeuroBLASTConfig model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig, layer_type_validation
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+import math
+import warnings
+
+logger = logging.get_logger(__name__)
+
+
+class NeuroBLASTConfig(PretrainedConfig):
+    model_type = "neuroblast"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `Qwen3`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        energy_dim=128,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_associative_layers=16,
+        num_sensory_layers=8,
+        num_motor_layers=8,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        layer_types=None,
+        attention_dropout=0.0,
+        attention_every=0,
+        dropout=0.0,
+        scale=1.0,
+        kernel_size=5,
+        temporal_kernel_size=5,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.energy_dim = energy_dim
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+
+        self.num_associative_layers = num_associative_layers
+        self.num_sensory_layers = num_sensory_layers
+
+        self.num_motor_layers = num_motor_layers
+
+        if (
+            num_hidden_layers
+            != num_associative_layers + num_sensory_layers + num_motor_layers
+        ):
+            self.num_hidden_layers = (
+                num_associative_layers + num_sensory_layers + num_motor_layers
+            )
+            warnings.warn(
+                f"num_hidden_layers ({num_hidden_layers}) is not equal to num_associative_layers ({num_associative_layers}) + num_sensory_layers ({num_sensory_layers}) + num_motor_layers ({num_motor_layers}). Setting num_hidden_layers to {num_associative_layers + num_sensory_layers + num_motor_layers}."
+            )
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if self.use_sliding_window else None
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.attention_every = attention_every
+        self.scale = scale
+        self.kernel_size = kernel_size
+        self.temporal_kernel_size = temporal_kernel_size
+        self.dropout = dropout
+
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            self.layer_types = [
+                ("full_attention") for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types)
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["NeuroBLASTConfig"]

generation_config.json

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+{
+  "_from_model_config": true,
+  "pad_token_id": 65537,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7fa14abdfa73bff6af64994e22d00b80796f5d7eb02a4084d7897fded10cf710
+size 2386982048

modeling_neuroblast.py

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+# coding=utf-8
+# Copyright 2025 Mariusz Kurman, MedIT Solutions Sp. z o.o, Poland. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""PyTorch NeuroBLAST model."""
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers import GenerationMixin
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_neuroblast import NeuroBLASTConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "NeuroBLASTConfig"
+
+
+class NeuroBLASTRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class NeuroBLASTMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+def rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed.to(q.dtype), k_embed.to(q.dtype)
+
+
+class NeuroBLASTAttention(nn.Module):
+    def __init__(self, config: NeuroBLASTConfig, layer_idx: Optional[int] = None, use_rope: bool = True):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.use_rope = use_rope
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+
+        self.q_norm = NeuroBLASTRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = NeuroBLASTRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+
+        # Norm
+        query_states = self.q_norm(query_states)
+        key_states = self.k_norm(key_states)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The `layer_idx` should be defined when calling the forward function of {self.__class__.__name__}. "
+                    "Please make sure to pass a `layer_idx` when creating this class."
+                )
+            kv_seq_len += past_key_value.get_seq_length(self.layer_idx)
+
+        if self.use_rope and position_embeddings is not None:
+             cos, sin = position_embeddings
+             cos = cos.squeeze(2)
+             sin = sin.squeeze(2)
+             query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=1)
+        else:
+            cos = None
+            sin = None
+
+        if past_key_value is not None:
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs={"cos": cos, "sin": sin})
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.config.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class NeuroBLASTRMSNorm2d(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.variance_epsilon = eps
+
+    def forward(self, x):
+        # x: (B, C, H, W)
+        input_dtype = x.dtype
+        x = x.to(torch.float32)
+        variance = x.pow(2).mean(dim=1, keepdim=True)
+        x_norm = x * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight.view(1, -1, 1, 1) * x_norm.to(input_dtype)
+
+
+class NeuroBLASTCausalConv2DBlock(nn.Module):
+    def __init__(self, config, dilation=1, layer_idx=0):
+        super().__init__()
+        self.config = config
+        self.dilation = dilation
+        self.layer_idx = layer_idx
+        
+        k = config.kernel_size
+        d = config.hidden_size
+        s = config.scale
+        
+        self.conv_padding = (k // 2, 0)
+        
+        if s == 1:
+            self.conv = nn.Conv2d(
+                d, d,
+                kernel_size=(k, k),
+                dilation=(1, dilation),
+                padding=self.conv_padding,
+                bias=False
+            )
+            self.use_gating = False
+            self.use_projection = False
+        elif s > 1:
+            internal_dim = int(d * s)
+            self.conv = nn.Conv2d(
+                d, internal_dim,
+                kernel_size=(k, k),
+                dilation=(1, dilation),
+                padding=self.conv_padding,
+                bias=False
+            )
+            self.use_gating = True
+            self.use_projection = False
+        else:
+            internal_dim = max(int(d * s), d // 4)
+            self.conv = nn.Conv2d(
+                d, internal_dim,
+                kernel_size=(k, k),
+                dilation=(1, dilation),
+                padding=self.conv_padding,
+                bias=False
+            )
+            self.use_gating = False
+            self.use_projection = True
+            self.proj_back = nn.Conv2d(internal_dim, d, kernel_size=(1, 1), bias=False)
+
+        self.norm_in = NeuroBLASTRMSNorm2d(d, eps=config.rms_norm_eps)
+        self.norm_out = NeuroBLASTRMSNorm2d(d, eps=config.rms_norm_eps)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        # x: (B, C, H, W)
+        B, C, H, W = x.shape
+        residual = x
+        y = self.norm_in(x)
+        
+        k = self.config.kernel_size
+        pad_w = (k - 1) * self.dilation
+        
+        # Pad W on the left
+        y_pad = F.pad(y, (pad_w, 0, 0, 0))
+        
+        y = self.conv(y_pad)
+        
+        if self.use_gating:
+            gate, val = torch.chunk(y, 2, dim=1)
+            y = val * F.softmax(gate, dim=1)
+        elif self.use_projection:
+            y = self.proj_back(y)
+            
+        y = self.norm_out(y)
+        
+        x = residual + self.dropout(y)
+        return x
+
+
+class NeuroBLASTDecoderLayer(nn.Module):
+    def __init__(self, config: NeuroBLASTConfig, layer_idx: int, attention_type: str = "full_attention"):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = NeuroBLASTAttention(
+            config=config,
+            layer_idx=layer_idx,
+            use_rope=(attention_type != "no_rope"),
+        )
+        self.mlp = NeuroBLASTMLP(config)
+        self.input_layernorm = NeuroBLASTRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = NeuroBLASTRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+        )
+        hidden_states = residual + hidden_states
+
+        # MLP
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class NeuroBLASTToken2D(nn.Module):
+    def forward(self, x, mode="seq_to_2d"):
+        if mode == "seq_to_2d":
+            return x.permute(0, 2, 1).unsqueeze(2)
+        else:
+            return x.squeeze(2).permute(0, 2, 1)
+
+
+class NeuroBLASTRotaryEmbedding(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dim = config.head_dim
+        self.max_position_embeddings = config.max_position_embeddings
+        self.base = config.rope_theta
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, x, position_ids):
+        # x: (B, L, H, D) or similar. Not used for shape here.
+        # position_ids: (B, L)
+        
+        inv_freq_expanded = self.inv_freq[None, :, None]
+        position_ids_expanded = position_ids[:, :, None].float()
+        freqs = torch.matmul(position_ids_expanded, self.inv_freq[None, None, :])
+        emb = torch.cat((freqs, freqs), dim=-1)
+        cos = emb.cos()
+        sin = emb.sin()
+        
+        # Output: (B, L, 1, D)
+        return cos[:, :, None, :], sin[:, :, None, :]
+
+
+class NeuroBLASTPreTrainedModel(PreTrainedModel):
+    config_class = NeuroBLASTConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["NeuroBLASTDecoderLayer", "NeuroBLASTCausalConv2DBlock"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class NeuroBLASTModel(NeuroBLASTPreTrainedModel):
+    def __init__(self, config: NeuroBLASTConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.token2d = NeuroBLASTToken2D()
+        
+        self.sensory_layers = nn.ModuleList()
+        dilatation_step = 1
+        for i in range(config.num_sensory_layers):
+            if i % 2 == 0:
+                layer = NeuroBLASTDecoderLayer(config, layer_idx=i, attention_type="full_attention")
+            else:
+                dilation = min(2 ** ((i - 1) // dilatation_step), 8)
+                layer = NeuroBLASTCausalConv2DBlock(config, dilation=dilation, layer_idx=i)
+            self.sensory_layers.append(layer)
+            
+        self.sensory_to_associative = NeuroBLASTRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        
+        self.associative_layers = nn.ModuleList()
+        next_layer_type = "full_attention"
+        for i in range(config.num_associative_layers):
+            idx = i + config.num_sensory_layers
+            if i % 2 == 0:
+                layer = NeuroBLASTDecoderLayer(config, layer_idx=idx, attention_type=next_layer_type)
+                if next_layer_type == "full_attention":
+                    next_layer_type = "no_rope"
+                else:
+                    next_layer_type = "full_attention"
+            else:
+                dilation = min(2 ** ((i - 1) // dilatation_step), 8)
+                layer = NeuroBLASTCausalConv2DBlock(config, dilation=dilation, layer_idx=idx)
+            self.associative_layers.append(layer)
+            
+        self.sensory_to_motor = NeuroBLASTRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        
+        self.motor_layers = nn.ModuleList()
+        next_layer_type = "full_attention"
+        for i in range(config.num_motor_layers):
+            idx = i + config.num_sensory_layers + config.num_associative_layers
+            layer = NeuroBLASTDecoderLayer(config, layer_idx=idx, attention_type=next_layer_type)
+            if next_layer_type == "full_attention":
+                next_layer_type = "no_rope"
+            else:
+                next_layer_type = "full_attention"
+            self.motor_layers.append(layer)
+            
+        self.norm = NeuroBLASTRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = NeuroBLASTRotaryEmbedding(config)
+        
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            past_length = 0
+            if past_key_values is not None:
+                if isinstance(past_key_values, DynamicCache):
+                    past_length = past_key_values.get_seq_length()
+                elif isinstance(past_key_values, (tuple, list)):
+                    past_length = past_key_values[0][0].shape[-2]
+            
+            position_ids = torch.arange(past_length, past_length + seq_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length), dtype=torch.bool, device=inputs_embeds.device
+            )
+            
+        # Create causal mask
+        min_dtype = torch.finfo(inputs_embeds.dtype).min
+        causal_mask = torch.full((seq_length, seq_length), min_dtype, device=inputs_embeds.device)
+        causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask = causal_mask[None, None, :, :] # (1, 1, L, L)
+        
+        # Expand attention_mask
+        # attention_mask: (B, L) -> (B, 1, 1, L)
+        padding_mask = attention_mask[:, None, None, :].to(inputs_embeds.dtype)
+        padding_mask = (1.0 - padding_mask) * min_dtype
+        
+        combined_mask = causal_mask + padding_mask
+        
+        # Initialize cache if needed
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        hidden_states = inputs_embeds
+        
+        # RoPE
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        residual = hidden_states
+        
+        # Sensory
+        for i, layer in enumerate(self.sensory_layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            
+            if i % 2 == 1:
+                # Conv layer
+                hidden_states = self.token2d(hidden_states, mode="seq_to_2d")
+                hidden_states = layer(hidden_states)
+                hidden_states = self.token2d(hidden_states, mode="d2_to_seq")
+            else:
+                # Attention layer
+                layer_outputs = layer(
+                    hidden_states,
+                    attention_mask=combined_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values, 
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    position_embeddings=position_embeddings,
+                )
+                hidden_states = layer_outputs[0]
+                if output_attentions:
+                    all_self_attns += (layer_outputs[1],)
+
+        hidden_states = hidden_states + self.sensory_to_associative(F.silu(residual))
+        
+        # Associative
+        for i, layer in enumerate(self.associative_layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+                
+            if i % 2 == 1:
+                hidden_states = self.token2d(hidden_states, mode="seq_to_2d")
+                hidden_states = layer(hidden_states)
+                hidden_states = self.token2d(hidden_states, mode="d2_to_seq")
+            else:
+                layer_outputs = layer(
+                    hidden_states,
+                    attention_mask=combined_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    position_embeddings=position_embeddings,
+                )
+                hidden_states = layer_outputs[0]
+                if output_attentions:
+                    all_self_attns += (layer_outputs[1],)
+                    
+        hidden_states = hidden_states + self.sensory_to_motor(F.silu(-residual))
+        
+        # Motor
+        for i, layer in enumerate(self.motor_layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+                
+            layer_outputs = layer(
+                hidden_states,
+                attention_mask=combined_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                position_embeddings=position_embeddings,
+            )
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+                
+        hidden_states = self.norm(hidden_states)
+        
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+            
+        if not return_dict:
+            return tuple(v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None)
+            
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+class NeuroBLASTPreTrainedModel(PreTrainedModel):
+    config_class = NeuroBLASTConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["NeuroBLASTBlock"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+class NeuroBLASTForCausalLM(NeuroBLASTPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = NeuroBLASTModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+    

special_tokens_map.json

@@ -0,0 +1,77 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<think>",
+    "</think>",
+    "source_1",
+    "source_2",
+    "source_3",
+    "source_4",
+    "source_5",
+    "source_6",
+    "source_7",
+    "source_8",
+    "source_9",
+    "source_10",
+    "<ref",
+    "</ref>",
+    "→",
+    "↺",
+    "※",
+    "?maybe?",
+    "●",
+    "◐",
+    "○",
+    "⚠",
+    "☐",
+    "☑",
+    "✓",
+    "⟨H≈0.1⟩",
+    "⟨H≈0.2⟩",
+    "⟨H≈0.3⟩",
+    "⟨H≈0.4⟩",
+    "⟨H≈0.5⟩",
+    "⟨H≈0.6⟩",
+    "⟨H≈0.7⟩",
+    "⟨H≈0.8⟩",
+    "⟨H≈0.9⟩",
+    "⟨H≈1.0⟩",
+    "⟨H≈1.1⟩",
+    "⟨H≈1.2⟩",
+    "⟨H≈1.3⟩",
+    "⟨H≈1.4⟩",
+    "⟨H≈1.5⟩",
+    "⟨H≈1.6⟩",
+    "⟨H≈1.7⟩",
+    "⟨H≈1.8⟩"
+  ],
+  "bos_token": {
+    "content": "<|im_start|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|mask|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,474 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65491": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65492": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65493": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65494": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65495": {
+      "content": "source_1",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65496": {
+      "content": "source_2",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65497": {
+      "content": "source_3",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65498": {
+      "content": "source_4",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65499": {
+      "content": "source_5",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65500": {
+      "content": "source_6",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65501": {
+      "content": "source_7",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65502": {
+      "content": "source_8",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65503": {
+      "content": "source_9",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65504": {
+      "content": "source_10",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65505": {
+      "content": "<ref",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65506": {
+      "content": "</ref>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65507": {
+      "content": "→",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+}