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configuration_nandi.py

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+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/nandi/modular_nandi.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_nandi.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. 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.
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import RopeParameters
+
+
+@strict(accept_kwargs=True)
+class NandiConfig(PretrainedConfig):
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import NandiConfig, NandiForCausalLM
+
+    >>> # Initializing a Nandi style configuration
+    >>> configuration = NandiConfig()
+
+    >>> # Initializing a model from the Nandi style configuration
+    >>> model = NandiForCausalLM(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "nandi"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    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",
+    }
+
+    # Defaults from the provided Nanotron training config.
+    vocab_size: int = 131072
+    hidden_size: int = 832
+    intermediate_size: int = 2496
+    num_hidden_layers: int = 16
+    num_attention_heads: int = 16
+    num_key_value_heads: int | None = 4
+    head_dim: int | None = None
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 2048
+    initializer_range: float = 0.008
+    rms_norm_eps: float = 1e-5
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 1
+    eos_token_id: int | list[int] | None = 0
+    pretraining_tp: int | None = 1
+    tie_word_embeddings: bool = True
+    rope_parameters: RopeParameters | dict | None = None
+    attention_bias: bool = False
+    attention_dropout: float = 0.0
+    mlp_bias: bool = False
+
+    # Nandi-specific options.
+    factorized_embedding: bool = True
+    embedding_rank: int = 196
+    layer_sharing: bool = True
+    layer_sharing_repeats: int = 2
+
+    def __post_init__(self, **kwargs):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.num_attention_heads
+        if self.rope_parameters is None:
+            self.rope_parameters = {"rope_theta": 100000.0}
+        if not self.layer_sharing:
+            self.layer_sharing_repeats = 1
+
+        if self.factorized_embedding and self.embedding_rank <= 0:
+            raise ValueError(
+                f"`embedding_rank` must be positive when `factorized_embedding=True`, got {self.embedding_rank}."
+            )
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError(
+                f"`hidden_size` ({self.hidden_size}) must be divisible by `num_attention_heads` ({self.num_attention_heads})."
+            )
+        if self.layer_sharing_repeats < 1:
+            raise ValueError(f"`layer_sharing_repeats` must be >= 1, got {self.layer_sharing_repeats}.")
+
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["NandiConfig"]

modeling_nandi.py

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+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/nandi/modular_nandi.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_nandi.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. 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.
+
+from collections.abc import Callable
+
+import torch
+import torch.nn as nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, DynamicLayer
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import can_return_tuple, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from .configuration_nandi import NandiConfig
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class NandiRMSNorm(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)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class NandiRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor
+
+    def __init__(self, config: NandiConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_type = self.config.rope_parameters.get("rope_type", "default")
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: NandiConfig | None = None,
+        device: torch.device | None = None,
+        seq_len: int | None = None,
+    ) -> tuple[torch.Tensor, float]:
+        del seq_len
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        attention_factor = 1.0
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.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=None, unsqueeze_dim=1):
+    del position_ids
+    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, k_embed
+
+
+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)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    del kwargs
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class NandiAttention(nn.Module):
+    def __init__(self, config: NandiConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = config.head_dim
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+        )
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class NandiMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)
+        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))
+
+
+class NandiDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: NandiConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = NandiAttention(config=config, layer_idx=layer_idx)
+        self.mlp = NandiMLP(config)
+        self.input_layernorm = NandiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = NandiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        cache_position: torch.LongTensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class _VirtualLayerCache:
+    """Proxy that shifts cache layer indices by `offset` to give each repeat its own virtual slots."""
+
+    def __init__(self, cache: Cache, offset: int):
+        self._cache = cache
+        self._offset = offset
+
+    def __getattr__(self, name):
+        return getattr(self._cache, name)
+
+    def update(self, key_states, value_states, layer_idx, cache_kwargs=None):
+        virtual_idx = layer_idx + self._offset
+        # grow the backing cache if generate() pre-allocated fewer slots than needed
+        while len(self._cache.layers) <= virtual_idx:
+            self._cache.layers.append(DynamicLayer())
+        return self._cache.update(key_states, value_states, virtual_idx, cache_kwargs)
+
+    def get_seq_length(self, layer_idx: int = 0) -> int:
+        return self._cache.get_seq_length(layer_idx + self._offset)
+
+
+@auto_docstring
+class NandiPreTrainedModel(PreTrainedModel):
+    config: NandiConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["NandiDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": NandiDecoderLayer,
+        "attentions": NandiAttention,
+    }
+
+    def __init__(self, config: NandiConfig):
+        super().__init__(config)
+
+
+@auto_docstring
+class NandiModel(NandiPreTrainedModel):
+    def __init__(self, config: NandiConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        embedding_dim = config.embedding_rank if config.factorized_embedding else config.hidden_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, embedding_dim, self.padding_idx)
+        self.embedding_proj = (
+            nn.Linear(config.embedding_rank, config.hidden_size, bias=False) if config.factorized_embedding else None
+        )
+        self.layers = nn.ModuleList(
+            [NandiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = NandiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = NandiRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if self.embedding_proj is not None:
+            inputs_embeds = self.embedding_proj(inputs_embeds)
+
+        repeats = self.config.layer_sharing_repeats if self.config.layer_sharing else 1
+
+        if use_cache and past_key_values is None:
+            # Use lazy DynamicCache (no config) so it grows to accommodate
+            # num_hidden_layers * repeats virtual slots for layer-sharing.
+            past_key_values = DynamicCache()
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            position_ids = position_ids.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            for repeat_idx in range(repeats):
+                # Each repeat gets its own virtual cache slots offset by num_hidden_layers,
+                # so repeat 0 uses slots 0..N-1 and repeat 1 uses slots N..2N-1, etc.
+                repeat_cache = (
+                    _VirtualLayerCache(past_key_values, repeat_idx * self.config.num_hidden_layers)
+                    if (past_key_values is not None and repeat_idx > 0)
+                    else past_key_values
+                )
+                hidden_states = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_embeddings=position_embeddings,
+                    position_ids=position_ids,
+                    past_key_values=repeat_cache,
+                    use_cache=use_cache,
+                    **kwargs,
+                )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+@auto_docstring
+class NandiForCausalLM(NandiPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tp_plan = {"lm_head": "colwise_gather_output"}
+    _pp_plan = {
+        "lm_head_proj": (["hidden_states"], ["hidden_states"]),
+        "lm_head": (["hidden_states"], ["logits"]),
+    }
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = NandiModel(config)
+        self.vocab_size = config.vocab_size
+
+        lm_head_in_features = config.embedding_rank if config.factorized_embedding else config.hidden_size
+        self.lm_head_proj = (
+            nn.Linear(config.hidden_size, config.embedding_rank, bias=False) if config.factorized_embedding else None
+        )
+        self.lm_head = nn.Linear(lm_head_in_features, config.vocab_size, bias=False)
+
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        outputs: BaseModelOutputWithPast = 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,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        if self.lm_head_proj is not None:
+            hidden_states = self.lm_head_proj(hidden_states)
+
+        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)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = ["NandiPreTrainedModel", "NandiModel", "NandiForCausalLM"]

tokenization_nandi.py

@@ -0,0 +1,124 @@
+# Copyright 2026 The HuggingFace Inc. team. 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.
+"""Tokenization classes for the Nandi family."""
+
+from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers
+from tokenizers.models import BPE
+
+from ...tokenization_utils_tokenizers import TokenizersBackend
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?(?:\p{L}\p{M}*)+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+
+
+class NandiTokenizer(TokenizersBackend):
+    model_input_names = ["input_ids", "attention_mask"]
+    model = BPE
+
+    def __init__(
+        self,
+        vocab: str | dict[str, int] | None = None,
+        merges: str | list[str] | None = None,
+        vocab_file=None,
+        merges_file=None,
+        unk_token: str = "<|endoftext|>",
+        bos_token: str = "<|im_start|>",
+        eos_token: str = "<|endoftext|>",
+        pad_token: str = "<|pad|>",
+        add_prefix_space: bool | None = None,
+        **kwargs,
+    ):
+        self._vocab = (
+            vocab
+            if vocab is not None
+            else {
+                "<|endoftext|>": 0,
+            }
+        )
+        self._merges = merges or []
+
+        self._tokenizer = Tokenizer(
+            BPE(
+                vocab=self._vocab,
+                merges=self._merges,
+                dropout=None,
+                unk_token=None,
+                continuing_subword_prefix="",
+                end_of_word_suffix="",
+                fuse_unk=False,
+                byte_fallback=False,
+            )
+        )
+        self._tokenizer.decoder = decoders.ByteLevel()
+        self._tokenizer.normalizer = normalizers.NFC()
+        self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
+            [
+                pre_tokenizers.Split(
+                    Regex(PRETOKENIZE_REGEX),
+                    behavior="isolated",
+                    invert=False,
+                ),
+                pre_tokenizers.ByteLevel(
+                    add_prefix_space=False,
+                    trim_offsets=True,
+                    use_regex=False
+                ),
+            ]
+        )
+
+        super().__init__(
+            vocab_file=vocab_file,
+            merges_file=merges_file,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            add_prefix_space=add_prefix_space,
+            **kwargs,
+        )
+
+    def encode(
+        self,
+        text,
+        text_pair=None,
+        add_special_tokens: bool = True,
+        padding=False,
+        truncation=None,
+        max_length=None,
+        stride: int = 0,
+        padding_side=None,
+        return_tensors=None,
+        **kwargs,
+    ):
+        if isinstance(text, str):
+            # This is a temporary fix to match the behaviour of the training pipeline
+            text = " " + text
+        return super().encode(
+            text,
+            text_pair=text_pair,
+            add_special_tokens=add_special_tokens,
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            stride=stride,
+            padding_side=padding_side,
+            return_tensors=return_tensors,
+            **kwargs,
+        )
+
+
+__all__ = ["NandiTokenizer"]