[Ernie 4.5] Add ernie text models

docs/source/en/_toctree.yml

@@ -441,6 +441,10 @@
         title: Encoder Decoder Models
       - local: model_doc/ernie
         title: ERNIE
+      - local: model_doc/ernie4_5
+        title: Ernie4_5
+      - local: model_doc/ernie4_5_moe
+        title: Ernie4_5_MoE
       - local: model_doc/ernie_m
         title: ErnieM
       - local: model_doc/esm

docs/source/en/model_doc/ernie4_5.md

@@ -0,0 +1,99 @@
+<!--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.
+
+⚠️ 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.
+
+-->
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+    </div>
+</div>
+
+# Ernie 4.5
+
+## Overview
+
+The Ernie 4.5 model was released in the [Ernie 4.5 Model Family](https://ernie.baidu.com/blog/posts/ernie4.5/) release by baidu.
+This family of models contains multiple different architectures and model sizes. This model in specific targets the base text
+model without mixture of experts (moe) with 0.3B parameters in total. It uses the standard [Llama](./llama.md) at its core.
+
+Other models from the family can be found at [Ernie 4.5 MoE](./ernie4_5_moe.md).
+
+<div class="flex justify-center">
+    <img src="https://ernie.baidu.com/blog/posts/ernie4.5/overview.png"/>
+</div>
+
+
+## Usage Tips
+
+### Generate text
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-0.3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+This model was contributed by [Anton Vlasjuk](https://huggingface.co/AntonV).
+The original code can be found [here](https://github.com/PaddlePaddle/ERNIE).
+
+
+## Ernie4_5Config
+
+[[autodoc]] Ernie4_5Config
+
+## Ernie4_5Model
+
+[[autodoc]] Ernie4_5Model
+    - forward
+
+## Ernie4_5ForCausalLM
+
+[[autodoc]] Ernie4_5ForCausalLM
+    - forward

docs/source/en/model_doc/ernie4_5_moe.md

@@ -0,0 +1,183 @@
+<!--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.
+
+⚠️ 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.
+
+-->
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+    </div>
+</div>
+
+# Ernie 4.5 MoE
+
+## Overview
+
+The Ernie 4.5 MoE model was released in the [Ernie 4.5 Model Family](https://ernie.baidu.com/blog/posts/ernie4.5/) release by baidu.
+This family of models contains multiple different architectures and model sizes. This model in specific targets the base text
+model with mixture of experts (moe) - one with 21B total, 3B active parameters and another one with 300B total, 47B active parameters.
+It uses the standard [Llama](./llama.md) at its core combined with a specialized MoE based on [Mixtral](./mixtral.md) with additional shared
+experts.
+
+Other models from the family can be found at [Ernie 4.5](./ernie4_5.md).
+
+<div class="flex justify-center">
+    <img src="https://ernie.baidu.com/blog/posts/ernie4.5/overview.png"/>
+</div>
+
+
+## Usage Tips
+
+### Generate text
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+### Distributed Generation with Tensor Parallelism
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    tp_plan="auto",
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+### Quantization with Bitsandbytes
+
+```python
+import torch
+from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+This model was contributed by [Anton Vlasjuk](https://huggingface.co/AntonV).
+The original code can be found [here](https://github.com/PaddlePaddle/ERNIE).
+
+
+## Ernie4_5_MoEConfig
+
+[[autodoc]] Ernie4_5_MoEConfig
+
+## Ernie4_5_MoEModel
+
+[[autodoc]] Ernie4_5_MoEModel
+    - forward
+
+## Ernie4_5_MoEForCausalLM
+
+[[autodoc]] Ernie4_5_MoEForCausalLM
+    - forward
+    - generate

src/transformers/modeling_utils.py

@@ -2977,6 +2977,17 @@ def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
         else:
             output_embeddings.weight = input_embeddings.weight
 
+        # Passing hooks over to the embeddings if needed
+        # (currently limited to tensor parallel hooks and flags only)
+        if hasattr(input_embeddings, "_is_hooked") and getattr(input_embeddings, "_hf_tp_plan", None):
+            output_embeddings._is_hooked = input_embeddings._is_hooked
+            output_embeddings._hf_tp_plan = input_embeddings._hf_tp_plan
+            output_embeddings._forward_hooks = input_embeddings._forward_hooks
+            output_embeddings._forward_pre_hooks = input_embeddings._forward_pre_hooks
+            output_embeddings.__repr__ = (
+                lambda: f"{output_embeddings.__repr__()}\nTP Plan: {output_embeddings._hf_tp_plan}"
+            )
+
         if getattr(output_embeddings, "bias", None) is not None:
             output_embeddings.bias.data = nn.functional.pad(
                 output_embeddings.bias.data,

src/transformers/models/auto/configuration_auto.py

@@ -128,6 +128,8 @@
         ("encoder-decoder", "EncoderDecoderConfig"),
         ("eomt", "EomtConfig"),
         ("ernie", "ErnieConfig"),
+        ("ernie4_5", "Ernie4_5Config"),
+        ("ernie4_5_moe", "Ernie4_5_MoEConfig"),
         ("ernie_m", "ErnieMConfig"),
         ("esm", "EsmConfig"),
         ("falcon", "FalconConfig"),
@@ -520,6 +522,8 @@
         ("encoder-decoder", "Encoder decoder"),
         ("eomt", "EoMT"),
         ("ernie", "ERNIE"),
+        ("ernie4_5", "Ernie4_5"),
+        ("ernie4_5_moe", "Ernie4_5_MoE"),
         ("ernie_m", "ErnieM"),
         ("esm", "ESM"),
         ("falcon", "Falcon"),

src/transformers/models/auto/modeling_auto.py

@@ -119,6 +119,8 @@
         ("emu3", "Emu3Model"),
         ("encodec", "EncodecModel"),
         ("ernie", "ErnieModel"),
+        ("ernie4_5", "Ernie4_5Model"),
+        ("ernie4_5_moe", "Ernie4_5_MoEModel"),
         ("ernie_m", "ErnieMModel"),
         ("esm", "EsmModel"),
         ("falcon", "FalconModel"),
@@ -594,6 +596,8 @@
         ("electra", "ElectraForCausalLM"),
         ("emu3", "Emu3ForCausalLM"),
         ("ernie", "ErnieForCausalLM"),
+        ("ernie4_5", "Ernie4_5ForCausalLM"),
+        ("ernie4_5_moe", "Ernie4_5_MoEForCausalLM"),
         ("falcon", "FalconForCausalLM"),
         ("falcon_h1", "FalconH1ForCausalLM"),
         ("falcon_mamba", "FalconMambaForCausalLM"),

src/transformers/models/auto/tokenization_auto.py

@@ -212,6 +212,8 @@
         ("electra", ("ElectraTokenizer", "ElectraTokenizerFast" if is_tokenizers_available() else None)),
         ("emu3", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
         ("ernie", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
+        ("ernie4_5", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)),
+        ("ernie4_5_moe", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)),
         ("ernie_m", ("ErnieMTokenizer" if is_sentencepiece_available() else None, None)),
         ("esm", ("EsmTokenizer", None)),
         ("falcon", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),

src/transformers/models/ernie4_5/__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_ernie4_5 import *
+    from .modeling_ernie4_5 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)