Add glm4

docs/source/en/_toctree.yml

@@ -461,6 +461,8 @@
         title: Gemma2
       - local: model_doc/glm
         title: GLM
+      - local: model_doc/glm4
+        title: glm4
       - local: model_doc/openai-gpt
         title: GPT
       - local: model_doc/gpt_neo

docs/source/en/model_doc/glm4.md

@@ -0,0 +1,45 @@
+<!--Copyright 2025 The GLM & ZhipuAI team and The HuggingFace 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Glm4
+
+## Overview
+
+To be released with the official model launch.
+
+## Glm4Config
+
+[[autodoc]] Glm4Config
+
+## Glm4Model
+
+[[autodoc]] Glm4Model
+    - forward
+
+## Glm4ForCausalLM
+
+[[autodoc]] Glm4ForCausalLM
+    - forward
+
+## Glm4ForSequenceClassification
+
+[[autodoc]] Glm4ForSequenceClassification
+    - forward
+
+## Glm4ForTokenClassification
+
+[[autodoc]] Glm4ForTokenClassification
+    - forward

src/transformers/__init__.py

@@ -482,6 +482,7 @@
         "GitVisionConfig",
     ],
     "models.glm": ["GlmConfig"],
+    "models.glm4": ["Glm4Config"],
     "models.glpn": ["GLPNConfig"],
     "models.got_ocr2": [
         "GotOcr2Config",
@@ -2526,6 +2527,15 @@
             "Llama4PreTrainedModel",
         ]
     )
+    _import_structure["models.glm4"].extend(
+        [
+            "Glm4ForCausalLM",
+            "Glm4ForSequenceClassification",
+            "Glm4ForTokenClassification",
+            "Glm4Model",
+            "Glm4PreTrainedModel",
+        ]
+    )
     _import_structure["models.glpn"].extend(
         [
             "GLPNForDepthEstimation",
@@ -5742,6 +5752,7 @@
         GitVisionConfig,
     )
     from .models.glm import GlmConfig
+    from .models.glm4 import Glm4Config
     from .models.glpn import GLPNConfig
     from .models.got_ocr2 import GotOcr2Config, GotOcr2Processor, GotOcr2VisionConfig
     from .models.gpt2 import (
@@ -7624,6 +7635,13 @@
             GlmModel,
             GlmPreTrainedModel,
         )
+        from .models.glm4 import (
+            Glm4ForCausalLM,
+            Glm4ForSequenceClassification,
+            Glm4ForTokenClassification,
+            Glm4Model,
+            Glm4PreTrainedModel,
+        )
         from .models.glpn import (
             GLPNForDepthEstimation,
             GLPNModel,

src/transformers/models/__init__.py

@@ -110,6 +110,7 @@
     gemma3,
     git,
     glm,
+    glm4,
     glpn,
     got_ocr2,
     gpt2,

src/transformers/models/auto/configuration_auto.py

@@ -130,6 +130,7 @@
         ("gemma3_text", "Gemma3TextConfig"),
         ("git", "GitConfig"),
         ("glm", "GlmConfig"),
+        ("glm4", "Glm4Config"),
         ("glpn", "GLPNConfig"),
         ("got_ocr2", "GotOcr2Config"),
         ("gpt-sw3", "GPT2Config"),
@@ -478,6 +479,7 @@
         ("gemma3_text", "Gemma3ForCausalLM"),
         ("git", "GIT"),
         ("glm", "GLM"),
+        ("glm4", "glm4"),
         ("glpn", "GLPN"),
         ("got_ocr2", "GOT-OCR2"),
         ("gpt-sw3", "GPT-Sw3"),

src/transformers/models/auto/modeling_auto.py

@@ -123,6 +123,7 @@
         ("gemma3_text", "Gemma3TextModel"),
         ("git", "GitModel"),
         ("glm", "GlmModel"),
+        ("glm4", "Glm4Model"),
         ("glpn", "GLPNModel"),
         ("got_ocr2", "GotOcr2ForConditionalGeneration"),
         ("gpt-sw3", "GPT2Model"),
@@ -533,6 +534,7 @@
         ("gemma3_text", "Gemma3ForCausalLM"),
         ("git", "GitForCausalLM"),
         ("glm", "GlmForCausalLM"),
+        ("glm4", "Glm4ForCausalLM"),
         ("got_ocr2", "GotOcr2ForConditionalGeneration"),
         ("gpt-sw3", "GPT2LMHeadModel"),
         ("gpt2", "GPT2LMHeadModel"),
@@ -1036,6 +1038,7 @@
         ("gemma", "GemmaForSequenceClassification"),
         ("gemma2", "Gemma2ForSequenceClassification"),
         ("glm", "GlmForSequenceClassification"),
+        ("glm4", "Glm4ForSequenceClassification"),
         ("gpt-sw3", "GPT2ForSequenceClassification"),
         ("gpt2", "GPT2ForSequenceClassification"),
         ("gpt_bigcode", "GPTBigCodeForSequenceClassification"),
@@ -1237,6 +1240,7 @@
         ("gemma", "GemmaForTokenClassification"),
         ("gemma2", "Gemma2ForTokenClassification"),
         ("glm", "GlmForTokenClassification"),
+        ("glm4", "Glm4ForTokenClassification"),
         ("gpt-sw3", "GPT2ForTokenClassification"),
         ("gpt2", "GPT2ForTokenClassification"),
         ("gpt_bigcode", "GPTBigCodeForTokenClassification"),

src/transformers/models/auto/tokenization_auto.py

@@ -238,6 +238,7 @@
             ),
             ("git", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
             ("glm", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),
+            ("glm4", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),
             ("gpt-sw3", ("GPTSw3Tokenizer" if is_sentencepiece_available() else None, None)),
             ("gpt2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
             ("gpt_bigcode", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),

src/transformers/models/glm4/__init__.py

@@ -0,0 +1,27 @@
+# Copyright 2025 The HuggingFace 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 typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_glm4 import *
+    from .modeling_glm4 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/glm4/configuration_glm4.py

@@ -0,0 +1,152 @@
+# coding=utf-8
+# Copyright 2025 The GLM4 & ZhipuAI team and 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 ...configuration_utils import PretrainedConfig
+
+
+class Glm4Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Glm4Model`]. It is used to instantiate an Glm4
+    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 Glm4-4-9b-chat.
+    e.g. [THUDM/glm-4-0414-9b-chat-chat](https://huggingface.co/THUDM/glm-4-0414-9b-chat-chat)
+    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 151552):
+            Vocabulary size of the Glm4 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Glm4Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 13696):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 40):
+            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*, defaults to 2):
+            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`.
+        partial_rotary_factor (`float`, *optional*, defaults to 0.5): The factor of the partial rotary position.
+        head_dim (`int`, *optional*, defaults to 128):
+            The attention head dimension.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The legacy activation function. It is overwritten by the `hidden_activation`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 131072):
+            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 1.5625e-07):
+            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`.
+        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.
+        pad_token_id (`int`, *optional*, defaults to 151329):
+            Padding token id.
+        eos_token_id (`int` | `list`, *optional*, defaults to `[151329, 151336, 151338]`):
+            End of stream token id.
+        bos_token_id (`int`, *optional*):
+            Beginning of stream token id.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `True`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+    ```python
+    >>> from transformers import Glm4Model, Glm4Config
+    >>> # Initializing a Glm4 glm4-4-9b-chat style configuration
+    >>> configuration = Glm4Config()
+    >>> # Initializing a model from the glm4-4-9b-chat style configuration
+    >>> model = Glm4Model(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "glm4"
+    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_up_proj": "colwise_rep",  # we need to replicate here due to the `chunk` operation
+        "layers.*.mlp.down_proj": "rowwise_rep",  # we need to replicate here due to the `chunk` operation
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=151552,
+        hidden_size=4096,
+        intermediate_size=13696,
+        num_hidden_layers=40,
+        num_attention_heads=32,
+        num_key_value_heads=2,
+        partial_rotary_factor=0.5,
+        head_dim=128,
+        hidden_act="silu",
+        attention_dropout=0.0,
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=0.00000015625,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        pad_token_id=151329,
+        eos_token_id=[151329, 151336, 151338],
+        bos_token_id=None,
+        attention_bias=True,
+        **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
+        self.partial_rotary_factor = partial_rotary_factor
+        self.head_dim = head_dim
+        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.use_cache = use_cache
+        self.rope_theta = rope_theta
+        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,
+        )
+
+
+__all__ = ["Glm4Config"]