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README.md

@@ -162,7 +162,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 
 # Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("codelion/dhara-70m")
-model = AutoModelForCausalLM.from_pretrained("codelion/dhara-70m")
+model = AutoModelForCausalLM.from_pretrained("codelion/dhara-70m", trust_remote_code=True)
 
 # Generate text
 inputs = tokenizer("The future of AI is", return_tensors="pt")
@@ -235,17 +235,17 @@ for i, output in enumerate(outputs):
 ## Citation
 
 ```bibtex
-@article{sharma2025dhara,
-  title={Dhara: Optimal Architecture for Efficient Diffusion Language Models},
+@article{sharma2025optimal,
+  title={The Optimal Architecture for Small Language Models},
   author={Sharma, Asankhaya},
   year={2025},
-  url={https://huggingface.co/codelion/dhara-70m}
+  url={https://huggingface.co/blog/codelion/optimal-model-architecture}
 }
 ```
 
 ## Related Work
 
-- [Width vs Depth: The Optimal Architecture for Small Language Models](https://huggingface.co/blog/codelion/optimal-architecture) - Blog post describing this work
+- [The Optimal Architecture for Small Language Models](https://huggingface.co/blog/codelion/optimal-model-architecture) - Blog post describing this work
 - [The 1 Billion Token Challenge: Optimal Dataset Mixing](https://huggingface.co/blog/codelion/optimal-dataset-mixing) - Our previous work on optimal pretraining data
 - [GPT-2-70M](https://huggingface.co/codelion/gpt-2-70m) - Our previous model from optimal pretraining experiments
 

config.json

@@ -1,7 +1,12 @@
 {
   "architectures": [
-    "DharaCanonForMaskedDiffusion"
+    "DharaForMaskedDiffusion"
   ],
+  "auto_map": {
+    "AutoConfig": "modeling_dhara.DharaConfig",
+    "AutoModel": "modeling_dhara.DharaForMaskedDiffusion",
+    "AutoModelForCausalLM": "modeling_dhara.DharaForMaskedDiffusion"
+  },
   "attention_dropout": 0.0,
   "bos_token_id": 1,
   "canon_activation": false,
@@ -18,7 +23,7 @@
   "mask_epsilon": 0.001,
   "mask_token_id": 50256,
   "max_position_embeddings": 2048,
-  "model_type": "dhara_canon",
+  "model_type": "dhara",
   "num_attention_heads": 6,
   "num_diffusion_steps": 1000,
   "num_hidden_layers": 32,

modeling_dhara.py

@@ -0,0 +1,760 @@
+#!/usr/bin/env python3
+"""
+Dhara: Diffusion Language Model
+
+A diffusion-based language model that combines:
+1. Masked diffusion training (MDM) with bidirectional attention
+2. Canon layers for local context mixing via causal depthwise convolutions
+3. High-throughput parallel token generation
+
+Usage:
+    from transformers import AutoModel, AutoTokenizer
+    model = AutoModel.from_pretrained("codelion/dhara-70m", trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained("codelion/dhara-70m")
+"""
+
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers import PreTrainedModel
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import 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
+
+
+class DharaConfig(PretrainedConfig):
+    """
+    Configuration for Dhara model.
+
+    Dhara is a diffusion language model with Canon layers for local context mixing.
+    """
+
+    model_type = "dhara"
+
+    def __init__(
+        self,
+        # Core architecture
+        vocab_size: int = 50257,
+        hidden_size: int = 384,
+        num_hidden_layers: int = 32,
+        num_attention_heads: int = 6,
+        num_key_value_heads: int = 6,
+        intermediate_size: int = 1024,
+        head_dim: int = None,
+        max_position_embeddings: int = 2048,
+
+        # Model specifics
+        hidden_act: str = "silu",
+        rms_norm_eps: float = 1e-5,
+        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",
+        canon_kernel: int = 4,
+        canon_residual: bool = True,
+        canon_activation: bool = False,
+        canon_bias: bool = False,
+
+        # Diffusion specific
+        mask_token_id: int = 50256,
+        mask_epsilon: float = 0.001,
+        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 = False,
+        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
+        )
+
+        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
+
+        self.hidden_act = hidden_act
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+        self.initializer_range = initializer_range
+        self.attention_dropout = attention_dropout
+
+        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
+
+        self.mask_token_id = mask_token_id
+        self.mask_epsilon = mask_epsilon
+        self.num_diffusion_steps = num_diffusion_steps
+
+        self.use_cache = use_cache
+        self.use_flash_attention = use_flash_attention
+        self.use_xformers = use_xformers
+
+
+class CanonLayer(nn.Module):
+    """Causal 1D depthwise convolution for local context mixing."""
+
+    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
+
+        self.conv = nn.Conv1d(
+            in_channels=hidden_size,
+            out_channels=hidden_size,
+            kernel_size=kernel_size,
+            padding=kernel_size - 1,
+            groups=hidden_size,
+            bias=use_bias,
+        )
+
+        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:
+        batch_size, seq_len, hidden_size = hidden_states.shape
+        x = hidden_states.transpose(1, 2)
+        out = self.conv(x)
+        out = out[:, :, :seq_len]
+        if self.use_activation:
+            out = F.silu(out)
+        out = out.transpose(1, 2)
+        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):
+    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):
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    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.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:
+    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"""
+
+    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  # Bidirectional for diffusion
+
+        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:
+            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)
+
+        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,
+            )
+            attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+        else:
+            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"""
+
+    def __init__(self, config: DharaConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.config = config
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        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,
+            )
+
+        self.self_attn = DharaAttention(config=config, layer_idx=layer_idx)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        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,
+            )
+
+        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
+        hidden_states = self.input_layernorm(hidden_states)
+
+        if self.canon_a is not None:
+            hidden_states = self.canon_a(hidden_states)
+
+        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
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        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")
+        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 and use_cache:
+            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:
+            if attention_mask is not None:
+                if attention_mask.dim() == 2:
+                    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)
+                    )
+
+        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."""
+        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
+        noisy_input_ids = torch.where(corruption_mask, self.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 and inference"""
+    _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 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
+
+    def save_pretrained(self, save_directory, **kwargs):
+        kwargs['safe_serialization'] = kwargs.get('safe_serialization', True)
+        return super().save_pretrained(save_directory, **kwargs)