Retroactive batch sync

config.json

@@ -0,0 +1,38 @@
+{
+  "architectures": [
+    "NandiForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_nandi.NandiConfig",
+    "AutoModel": "modeling_nandi.NandiModel",
+    "AutoModelForCausalLM": "modeling_nandi.NandiForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "embedding_rank": 196,
+  "eos_token_id": 0,
+  "factorized_embedding": true,
+  "head_dim": 52,
+  "hidden_act": "silu",
+  "hidden_size": 832,
+  "initializer_range": 0.02,
+  "intermediate_size": 2496,
+  "layer_sharing": true,
+  "layer_sharing_repeats": 2,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "nandi",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 100000
+  },
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "vocab_size": 131072
+}

configuration_nandi.py

@@ -0,0 +1,120 @@
+# Copyright 2026 RTA AI Labs. 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 transformers.configuration_utils import PretrainedConfig
+
+
+class NandiConfig(PretrainedConfig):
+    r"""
+    Configuration class for the Nandi model.
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoConfig, AutoModelForCausalLM
+
+    >>> configuration = AutoConfig.from_pretrained("Rta-AILabs/Nandi-150M-remote", trust_remote_code=True)
+
+    >>> model = AutoModelForCausalLM.from_pretrained("Rta-AILabs/Nandi-150M-remote", trust_remote_code=True)
+
+    >>> 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",
+    }
+
+    def __init__(
+        self,
+        vocab_size=131072,
+        hidden_size=832,
+        intermediate_size=2496,
+        num_hidden_layers=16,
+        num_attention_heads=16,
+        num_key_value_heads=4,
+        head_dim=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.008,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=0,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_parameters=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        factorized_embedding=True,
+        embedding_rank=196,
+        layer_sharing=True,
+        layer_sharing_repeats=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
+        self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.pretraining_tp = pretraining_tp
+        self.rope_parameters = rope_parameters if rope_parameters is not None else {"rope_theta": 100000.0}
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+        self.factorized_embedding = factorized_embedding
+        self.embedding_rank = embedding_rank
+        self.layer_sharing = layer_sharing
+        self.layer_sharing_repeats = layer_sharing_repeats if layer_sharing else 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 "
+                f"`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().__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,
+        )
+
+
+__all__ = ["NandiConfig"]

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 0,
+  "transformers_version": "4.57.6"
+}

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70e2835501bca2d2df3ec0995d7131a0695ff67fa0fe07cba858eb9e26537a57
+size 614820354