Upload jdeval checkpoint-1000

jdeval-checkpoint-1000/chat_template.jinja

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+{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
+
+'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>
+
+' }}

jdeval-checkpoint-1000/config.json

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+{
+  "add_faster_video": false,
+  "add_time_instruction": false,
+  "architectures": [
+    "LlavaLLaDAModelLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_llada.LLaDAConfig",
+    "AutoModel": "modeling_llada.LLaDAModelLM",
+    "AutoModelForCausalLM": "modeling_llada.LLaDAModelLM"
+  },
+  "bos_token_id": 126080,
+  "dtype": "bfloat16",
+  "embedding_loss_lambda": 0.5,
+  "embedding_loss_type": "infonce_learnable",
+  "embedding_pool_strategy": "response_tokens",
+  "eos_token_id": 126081,
+  "faster_token_stride": 10,
+  "force_sample": false,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "image_aspect_ratio": "square",
+  "image_crop_resolution": null,
+  "image_grid_pinpoints": null,
+  "image_split_resolution": null,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 16384,
+  "mm_hidden_size": 1152,
+  "mm_newline_position": "grid",
+  "mm_patch_merge_type": "flat",
+  "mm_projector_lr": null,
+  "mm_projector_type": "mlp2x_gelu",
+  "mm_resampler_type": null,
+  "mm_spatial_pool_mode": "bilinear",
+  "mm_spatial_pool_stride": null,
+  "mm_tunable_parts": "mm_mlp_adapter,mm_language_model",
+  "mm_use_im_patch_token": false,
+  "mm_use_im_start_end": false,
+  "mm_vision_select_feature": "patch",
+  "mm_vision_select_layer": -2,
+  "mm_vision_tower": "google/siglip2-so400m-patch14-384",
+  "mm_vision_tower_lr": null,
+  "model_type": "llada",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 126081,
+  "pos_skipping_range": 4096,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "tokenizer_model_max_length": 2048,
+  "tokenizer_padding_side": "right",
+  "transformers_version": "4.57.1",
+  "use_auxiliary_embedding_loss": false,
+  "use_cache": false,
+  "use_mm_proj": true,
+  "use_pos_skipping": false,
+  "vision_tower_pretrained": null,
+  "vocab_size": 126349,
+  "y_encoder_dim": 1024,
+  "y_encoder_name": "abhinand/MedEmbed-large-v0.1"
+}

jdeval-checkpoint-1000/configuration_llada.py

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+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" LLaDA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+LLaDA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class LLaDAConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LLaDAModel`]. It is used to instantiate an LLaDA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaDA-8B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaDA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LLaDAModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. 
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. 
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+    """
+
+    model_type = "llada"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # 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.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

jdeval-checkpoint-1000/latest

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+global_step200

jdeval-checkpoint-1000/model-00001-of-00004.safetensors

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+}

jdeval-checkpoint-1000/modeling_dhara.py

@@ -0,0 +1,1111 @@
+#!/usr/bin/env python3
+"""
+Dhara: Diffusion LLM with Canon Layers
+
+Combines:
+1. Dhara's masked diffusion training (bidirectional attention, high throughput)
+2. Canon layers (local context mixing via causal depthwise convolutions)
+
+Canon layers from "Physics of Language Models: Part 4.1" by Zeyuan Allen-Zhu:
+- Position A: After input LayerNorm, before attention
+- Position C: After post-attention LayerNorm, before MLP
+- kernel_size=4, residual=True, activation=False (default)
+
+Expected benefits:
+- ~280-290 tok/s throughput (Dhara's parallel generation)
+- +0.25-0.5% accuracy improvement (Canon's local context mixing)
+"""
+
+import math
+import warnings
+from typing import Optional, Tuple, Union, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+
+from transformers import PreTrainedModel
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import BaseModelOutputWithPast, MaskedLMOutput
+from transformers.utils import logging
+from transformers.cache_utils import Cache, DynamicCache
+from transformers import PretrainedConfig
+
+logger = logging.get_logger(__name__)
+
+# Optional performance imports
+try:
+    from flash_attn import flash_attn_func
+    FLASH_ATTN_AVAILABLE = True
+except ImportError:
+    FLASH_ATTN_AVAILABLE = False
+
+try:
+    import xformers.ops as xops
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    XFORMERS_AVAILABLE = False
+
+
+class DharaConfig(PretrainedConfig):
+    """
+    Configuration for Dhara model.
+
+    Combines Dhara diffusion config with Canon layer parameters.
+    """
+
+    model_type = "dhara"
+
+    def __init__(
+        self,
+        # Core architecture
+        vocab_size: int = 50304,
+        hidden_size: int = 384,
+        num_hidden_layers: int = 32,
+        num_attention_heads: int = 8,
+        num_key_value_heads: int = 4,
+        intermediate_size: int = 1024,
+        head_dim: int = None,
+        max_position_embeddings: int = 2048,
+
+        # Model specifics
+        hidden_act: str = "silu",
+        rms_norm_eps: float = 1e-6,
+        rope_theta: float = 10000.0,
+        initializer_range: float = 0.02,
+        tie_word_embeddings: bool = True,
+        attention_dropout: float = 0.0,
+
+        # Canon layer parameters
+        canon_set: str = "AC",  # Positions: A (before attn), C (before MLP)
+        canon_kernel: int = 4,  # Kernel size (2-4)
+        canon_residual: bool = True,  # Highly recommended
+        canon_activation: bool = False,  # NOT recommended for transformers
+        canon_bias: bool = False,
+
+        # Diffusion specific
+        mask_token_id: int = None,  # Will be set from tokenizer
+        mask_epsilon: float = 0.001,  # Minimum mask probability
+        num_diffusion_steps: int = 1000,
+
+        # Special tokens
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        pad_token_id: int = 0,
+
+        # Performance flags
+        use_cache: bool = False,
+        use_flash_attention: bool = True,
+        use_xformers: bool = False,
+
+        **kwargs
+    ):
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )
+
+        # Core architecture
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.intermediate_size = intermediate_size
+        self.head_dim = head_dim or (hidden_size // num_attention_heads)
+        self.max_position_embeddings = max_position_embeddings
+
+        # Model specifics
+        self.hidden_act = hidden_act
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+        self.initializer_range = initializer_range
+        self.tie_word_embeddings = tie_word_embeddings
+        self.attention_dropout = attention_dropout
+
+        # Canon parameters
+        self.canon_set = canon_set
+        self.canon_kernel = canon_kernel
+        self.canon_residual = canon_residual
+        self.canon_activation = canon_activation
+        self.canon_bias = canon_bias
+
+        # Diffusion specific
+        self.mask_token_id = mask_token_id if mask_token_id is not None else (vocab_size - 1)
+        self.mask_epsilon = mask_epsilon
+        self.num_diffusion_steps = num_diffusion_steps
+
+        # Special tokens
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.pad_token_id = pad_token_id
+
+        # Performance
+        self.use_cache = use_cache
+        self.use_flash_attention = use_flash_attention
+        self.use_xformers = use_xformers
+
+
+class CanonLayer(nn.Module):
+    """
+    Canon Layer: Causal 1D depthwise convolution for local context mixing.
+
+    From "Physics of Language Models: Part 4.1" by Zeyuan Allen-Zhu.
+    Captures local sequential dependencies with O(n) complexity.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        kernel_size: int = 4,
+        use_residual: bool = True,
+        use_activation: bool = False,
+        use_bias: bool = False,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.kernel_size = kernel_size
+        self.use_residual = use_residual
+        self.use_activation = use_activation
+
+        # Depthwise causal convolution
+        self.conv = nn.Conv1d(
+            in_channels=hidden_size,
+            out_channels=hidden_size,
+            kernel_size=kernel_size,
+            padding=kernel_size - 1,  # Causal (left-pad)
+            groups=hidden_size,  # Depthwise
+            bias=use_bias,
+        )
+
+        # Initialize for stability
+        nn.init.normal_(self.conv.weight, mean=0.0, std=0.02)
+        if use_bias:
+            nn.init.zeros_(self.conv.bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: [batch_size, seq_len, hidden_size]
+        Returns:
+            output: [batch_size, seq_len, hidden_size]
+        """
+        batch_size, seq_len, hidden_size = hidden_states.shape
+
+        # Transpose for Conv1d: [B, H, L]
+        x = hidden_states.transpose(1, 2)
+
+        # Apply conv with causal padding
+        out = self.conv(x)
+        # Remove right padding to make it causal
+        out = out[:, :, :seq_len]
+
+        # Optional activation
+        if self.use_activation:
+            out = F.silu(out)
+
+        # Transpose back: [B, L, H]
+        out = out.transpose(1, 2)
+
+        # Residual connection
+        if self.use_residual:
+            out = hidden_states + out
+
+        return out
+
+
+class RMSNorm(nn.Module):
+    """Root Mean Square Layer Normalization"""
+
+    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 RotaryEmbedding(nn.Module):
+    """Rotary Position Embeddings (RoPE)"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    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, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to query and key tensors."""
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    # Cast to input dtype for consistency
+    cos = cos.to(q.dtype)
+    sin = sin.to(q.dtype)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class DharaMLP(nn.Module):
+    """MLP with SwiGLU activation"""
+
+    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 = nn.SiLU()
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """Repeat KV heads for GQA."""
+    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 DharaAttention(nn.Module):
+    """Multi-Head Bidirectional Attention with GQA support (for diffusion)"""
+
+    def __init__(self, config: DharaConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        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 = False  # CRITICAL: Dhara uses bidirectional attention
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value=None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> 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).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).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 cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Flash Attention for bidirectional
+        if FLASH_ATTN_AVAILABLE and self.config.use_flash_attention:
+            query_states = query_states.transpose(1, 2).contiguous()
+            key_states = key_states.transpose(1, 2).contiguous()
+            value_states = value_states.transpose(1, 2).contiguous()
+
+            if query_states.dtype not in [torch.float16, torch.bfloat16]:
+                query_states = query_states.to(torch.bfloat16)
+                key_states = key_states.to(torch.bfloat16)
+                value_states = value_states.to(torch.bfloat16)
+
+            attn_output = flash_attn_func(
+                query_states,
+                key_states,
+                value_states,
+                dropout_p=0.0,
+                causal=False,  # Bidirectional for diffusion
+            )
+
+            attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        else:
+            # Standard attention
+            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
+
+            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.attention_dropout, training=self.training)
+            attn_output = torch.matmul(attn_weights, value_states)
+
+            attn_output = attn_output.transpose(1, 2).contiguous()
+            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DharaDecoderLayer(nn.Module):
+    """
+    Dhara decoder layer with Canon layers at positions A and C.
+
+    Flow:
+        x -> LayerNorm -> [CanonA] -> Attention -> + residual
+        x -> LayerNorm -> [CanonC] -> MLP -> + residual
+    """
+
+    def __init__(self, config: DharaConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.config = config
+
+        # Pre-attention norm
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        # Canon-A: before attention
+        self.canon_a = None
+        if "A" in config.canon_set:
+            self.canon_a = CanonLayer(
+                hidden_size=config.hidden_size,
+                kernel_size=config.canon_kernel,
+                use_residual=config.canon_residual,
+                use_activation=config.canon_activation,
+                use_bias=config.canon_bias,
+            )
+
+        # Attention
+        self.self_attn = DharaAttention(config=config, layer_idx=layer_idx)
+
+        # Post-attention norm
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        # Canon-C: before MLP
+        self.canon_c = None
+        if "C" in config.canon_set:
+            self.canon_c = CanonLayer(
+                hidden_size=config.hidden_size,
+                kernel_size=config.canon_kernel,
+                use_residual=config.canon_residual,
+                use_activation=config.canon_activation,
+                use_bias=config.canon_bias,
+            )
+
+        # MLP
+        self.mlp = DharaMLP(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value=None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+
+        # Pre-attention layernorm
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Canon-A (before attention)
+        if self.canon_a is not None:
+            hidden_states = self.canon_a(hidden_states)
+
+        # Self Attention (bidirectional)
+        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,
+        )
+        hidden_states = residual + hidden_states
+
+        # MLP block
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        # Canon-C (before MLP)
+        if self.canon_c is not None:
+            hidden_states = self.canon_c(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 DharaPreTrainedModel(PreTrainedModel):
+    config_class = DharaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DharaDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+
+    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 DharaModel(DharaPreTrainedModel):
+    """
+    Dhara base model with bidirectional attention and Canon layers.
+    """
+
+    def __init__(self, config: DharaConfig):
+        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.layers = nn.ModuleList(
+            [DharaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+        self.config = config
+        self.mask_token_id = config.mask_token_id
+        self._use_flash_attention_2 = config.use_flash_attention and FLASH_ATTN_AVAILABLE
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values=None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = 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[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if self._use_flash_attention_2:
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            # Bidirectional attention mask (not causal)
+            if attention_mask is not None:
+                if attention_mask.dim() == 2:
+                    batch_size, seq_length = attention_mask.shape
+                    attention_mask_4d = attention_mask[:, None, None, :].expand(
+                        batch_size, 1, seq_length, seq_length
+                    ).to(dtype=inputs_embeds.dtype)
+                    attention_mask = torch.where(
+                        attention_mask_4d == 0,
+                        torch.tensor(float('-inf'), dtype=inputs_embeds.dtype, device=attention_mask_4d.device),
+                        torch.tensor(0.0, dtype=inputs_embeds.dtype, device=attention_mask_4d.device)
+                    )
+                else:
+                    attention_mask = attention_mask
+            else:
+                attention_mask = None
+
+        hidden_states = inputs_embeds
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def add_noise_to_tokens(self, input_ids: torch.LongTensor, t: torch.FloatTensor, eps: float = None):
+        """
+        MDM-style masking: Replace tokens with [MASK] based on noise level t.
+
+        Args:
+            input_ids: Input token IDs [batch_size, seq_len]
+            t: Noise level in [0, 1] [batch_size]
+            eps: Minimum mask probability
+
+        Returns:
+            Tuple of (noisy_input_ids, corruption_mask, p_mask)
+        """
+        batch_size, seq_len = input_ids.shape
+        device = input_ids.device
+
+        if eps is None:
+            eps = getattr(self.config, 'mask_epsilon', 0.001)
+        p_mask = (1 - eps) * t + eps
+
+        p_mask = p_mask.unsqueeze(-1).expand(batch_size, seq_len)
+
+        corruption_mask = torch.rand(batch_size, seq_len, device=device) < p_mask
+
+        mask_token_id = self.mask_token_id
+        noisy_input_ids = torch.where(corruption_mask, mask_token_id, input_ids)
+
+        return noisy_input_ids, corruption_mask, p_mask
+
+
+class DharaForMaskedDiffusion(DharaPreTrainedModel, GenerationMixin):
+    """Dhara Model with Masked Diffusion head for training"""
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = DharaModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.config = config
+        self.mask_token_id = config.mask_token_id
+
+        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: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values=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,
+        return_dict: Optional[bool] = None,
+        corruption_mask: Optional[torch.BoolTensor] = None,
+        p_mask: Optional[torch.Tensor] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        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
+
+        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,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        if self.config.tie_word_embeddings:
+            logits = F.linear(hidden_states, self.model.embed_tokens.weight)
+        else:
+            logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            loss = self.compute_diffusion_loss(logits, labels, corruption_mask, p_mask)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def compute_diffusion_loss(self, logits, labels, corruption_mask=None, p_mask=None):
+        """
+        MDM loss with p_mask importance weighting.
+        """
+        if corruption_mask is None or p_mask is None:
+            raise ValueError(
+                "MDM requires both corruption_mask and p_mask for loss computation."
+            )
+
+        loss = F.cross_entropy(
+            logits.view(-1, self.config.vocab_size),
+            labels.view(-1),
+            reduction='none'
+        )
+        loss = loss.view(labels.shape)
+
+        masked_losses = loss[corruption_mask]
+        masked_p_mask = p_mask[corruption_mask]
+
+        weighted_losses = masked_losses / masked_p_mask
+
+        total_positions = labels.shape[0] * labels.shape[1]
+        return weighted_losses.sum() / total_positions
+
+    def add_noise_to_tokens(self, input_ids: torch.LongTensor, t: torch.FloatTensor, eps: float = None):
+        """Delegate to the base model"""
+        return self.model.add_noise_to_tokens(input_ids, t, eps)
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        max_length: Optional[int] = None,
+        max_new_tokens: Optional[int] = None,
+        num_diffusion_steps: int = 10,
+        temperature: float = 1.0,
+        top_p: float = 0.9,
+        top_k: int = 50,
+        do_sample: bool = True,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[int] = None,
+        repetition_penalty: float = 1.2,
+        **kwargs
+    ) -> torch.LongTensor:
+        """
+        Generate text using autoregressive sampling with the diffusion model.
+
+        Since this model was converted from AR to diffusion via WSD training,
+        we generate tokens one at a time left-to-right, using the model's
+        next-token predictions at each position.
+
+        Args:
+            input_ids: Input prompt token IDs [batch_size, prompt_len]
+            max_length: Maximum total sequence length (prompt + generation)
+            max_new_tokens: Number of new tokens to generate (alternative to max_length)
+            num_diffusion_steps: Number of refinement iterations per token (higher = better quality)
+            temperature: Sampling temperature (higher = more random)
+            top_p: Nucleus sampling threshold
+            top_k: Top-k sampling threshold
+            do_sample: Whether to sample or take argmax
+            pad_token_id: Token ID for padding
+            eos_token_id: Token ID for end of sequence
+            repetition_penalty: Penalty for repeating tokens (>1 = less repetition)
+
+        Returns:
+            Generated token IDs including the prompt
+        """
+        # Handle device and dtype
+        device = input_ids.device if input_ids is not None else next(self.parameters()).device
+
+        # Determine generation length
+        if input_ids is not None:
+            batch_size, prompt_len = input_ids.shape
+        else:
+            batch_size, prompt_len = 1, 0
+            input_ids = torch.empty(batch_size, 0, dtype=torch.long, device=device)
+
+        if max_new_tokens is not None:
+            gen_len = max_new_tokens
+        elif max_length is not None:
+            gen_len = max_length - prompt_len
+        else:
+            gen_len = 50  # Default generation length
+
+        if gen_len <= 0:
+            return input_ids
+
+        # Get special token IDs
+        mask_token_id = self.config.mask_token_id
+        if pad_token_id is None:
+            pad_token_id = self.config.pad_token_id if hasattr(self.config, 'pad_token_id') else 0
+        if eos_token_id is None:
+            eos_token_id = self.config.eos_token_id if hasattr(self.config, 'eos_token_id') else 2
+
+        # Start with the prompt
+        generated = input_ids.clone()
+
+        # Track generated tokens for repetition penalty
+        generated_set = set()
+        for i in range(prompt_len):
+            for b in range(batch_size):
+                generated_set.add(input_ids[b, i].item())
+
+        # Generate tokens one at a time (autoregressive style)
+        for pos in range(gen_len):
+            # Add a mask token at the next position
+            current_seq = torch.cat([
+                generated,
+                torch.full((batch_size, 1), mask_token_id, dtype=torch.long, device=device)
+            ], dim=1)
+
+            # Get model predictions
+            outputs = self(input_ids=current_seq)
+            logits = outputs.logits  # [batch, seq_len, vocab]
+
+            # Get logits for the last (masked) position
+            next_token_logits = logits[:, -1, :]  # [batch, vocab]
+
+            # Apply repetition penalty
+            if repetition_penalty != 1.0:
+                for b in range(batch_size):
+                    for prev_token in generated_set:
+                        if prev_token < next_token_logits.shape[1]:
+                            next_token_logits[b, prev_token] /= repetition_penalty
+
+            # Apply temperature
+            if temperature != 1.0 and temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            if do_sample and temperature > 0:
+                # Apply top-k filtering
+                if top_k > 0:
+                    indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
+                    next_token_logits[indices_to_remove] = float('-inf')
+
+                # Apply top-p (nucleus) filtering
+                if top_p < 1.0:
+                    sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+                    # Remove tokens with cumulative probability above threshold
+                    sorted_indices_to_remove = cumulative_probs > top_p
+                    # Shift the indices to the right to keep the first token above threshold
+                    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+                    sorted_indices_to_remove[..., 0] = False
+
+                    # Scatter sorted indices to original indexing
+                    indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                    next_token_logits[indices_to_remove] = float('-inf')
+
+                # Sample from the filtered distribution
+                probs = F.softmax(next_token_logits, dim=-1)
+                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(-1)
+            else:
+                # Greedy decoding
+                next_tokens = next_token_logits.argmax(dim=-1)
+
+            # Add to generated sequence
+            generated = torch.cat([generated, next_tokens.unsqueeze(-1)], dim=1)
+
+            # Update generated set for repetition penalty
+            for b in range(batch_size):
+                generated_set.add(next_tokens[b].item())
+
+            # Check for EOS
+            if eos_token_id is not None and (next_tokens == eos_token_id).all():
+                break
+
+        return generated
+
+    def save_pretrained(self, save_directory, **kwargs):
+        """Override to save in SafeTensors format by default"""
+        kwargs['safe_serialization'] = kwargs.get('safe_serialization', True)
+        return super().save_pretrained(save_directory, **kwargs)
+
+
+def count_parameters(model):
+    """Count total and Canon-specific parameters."""
+    total = sum(p.numel() for p in model.parameters())
+    canon = sum(p.numel() for n, p in model.named_parameters() if 'canon' in n.lower())
+    return total, canon
+
+
+if __name__ == "__main__":
+    # Quick test
+    print("Testing Dhara model creation...")
+
+    config = DharaConfig(
+        vocab_size=50304,
+        hidden_size=384,
+        num_hidden_layers=32,
+        num_attention_heads=8,
+        num_key_value_heads=4,
+        intermediate_size=1024,
+        canon_set="AC",
+        canon_kernel=4,
+        canon_residual=True,
+    )
+
+    model = DharaForMaskedDiffusion(config)
+
+    total, canon = count_parameters(model)
+    print(f"Model created successfully!")
+    print(f"Total params: {total:,} ({total/1e6:.2f}M)")
+    print(f"Canon params: {canon:,} ({100*canon/total:.3f}%)")
+    print(f"Base Dhara would be: {total - canon:,}")
+
+    # Test forward pass
+    batch_size, seq_len = 2, 64
+    input_ids = torch.randint(0, 50304, (batch_size, seq_len))
+
+    # Test with diffusion noise
+    t = torch.rand(batch_size)
+    noisy_ids, corruption_mask, p_mask = model.add_noise_to_tokens(input_ids, t)
+
+    with torch.no_grad():
+        outputs = model(
+            input_ids=noisy_ids,
+            labels=input_ids,
+            corruption_mask=corruption_mask,
+            p_mask=p_mask,
+        )
+
+    print(f"Forward pass: loss={outputs.loss.item():.4f}")
+    print("Ready for training!")

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