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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

@@ -0,0 +1,283 @@
+Tencent is pleased to support the community by making WeDLM-8B-Instruct available.
+
+Copyright (C) 2025 Tencent. All rights reserved. WeDLM-8B-Instruct IS NOT INTENDED FOR USE WITHIN THE EUROPEAN UNION.
+
+WeDLM-8B-Instruct is licensed under the License Terms of WeDLM-8B-Instruct except for the third-party components listed below, which is licensed under different terms. WeDLM-8B-Instruct does not impose any additional limitations beyond what is outlined in the respective licenses of these third-party components. Users must comply with all terms and conditions of original licenses of these third-party components and must ensure that the usage of the third party components adheres to all relevant laws and regulations. 
+
+For avoidance of doubts, WeDLM-8B-Instruct refers to the inference enabling code, parameters and weights made publicly available by Tencent in accordance with the License Terms of WeDLM-8B-Instruct in this repository.
+
+Terms of the License Terms of WeDLM-8B-Instruct:
+--------------------------------------------------------------------
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+0. Additional Territorial Limitation 
+
+*WeDLM-8B-Instruct IS NOT INTENDED FOR USE WITHIN THE EUROPEAN UNION.* 
+IN THE EVENT OF ANY CONFLICT, THIS CLAUSE SHALL PREVAIL.
+
+1. Definitions.
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+9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
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+END OF TERMS AND CONDITIONS
+
+The WeDLM-8B-Instruct model was fine-tuned with the assistance of the following Open Models by Tencent.
+
+Open Models Licensed under the Apache-2.0 License:
+--------------------------------------------------------------------
+1.Qwen/Qwen3-8B
+ Copyright 2024 Alibaba Cloud
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+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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+      "License" shall mean the terms and conditions for use, reproduction,
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+   6. Trademarks. This License does not grant permission to use the trade
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+   9. Accepting Warranty or Additional Liability. While redistributing
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+The Code of this project is built on and with the aid of the following open source projects. Credits are given to these projects.
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+Open Source Software Licensed under the MIT:
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+1. nano-vllm
+Copyright (c) 2025 Xingkai Yu
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+End of the Attribution Notice of this project.

Readme.md

@@ -0,0 +1,137 @@
+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model: tencent/WeDLM-8B
+pipeline_tag: text-generation
+tags:
+- language model
+- parallel-decoding
+---
+
+# WeDLM-8B-Instruct ⭐
+
+**WeDLM-8B-Instruct** is our flagship instruction-tuned diffusion language model that performs parallel decoding under standard causal attention, fine-tuned from [WeDLM-8B](https://huggingface.co/tencent/WeDLM-8B).
+
+**Highlights:**
+- 🚀 3-6× faster than vLLM-optimized Qwen3-8B on math reasoning tasks
+- 📈 Outperforms base Qwen3-8B-Instruct on most benchmarks
+- ✅ Native KV cache compatible (FlashAttention, PagedAttention, CUDA Graphs)
+
+For the base (pretrained) version, see [WeDLM-8B](https://huggingface.co/tencent/WeDLM-8B).
+
+📄 Paper (Coming Soon) | 🌐 [Project Page](https://wedlm.github.io) | 💻 [GitHub](https://github.com/tencent/WeDLM)
+
+## Model Details
+
+| Attribute | Value |
+|:----------|:------|
+| Base Model | [WeDLM-8B](https://huggingface.co/tencent/WeDLM-8B) |
+| Parameters | 8B |
+| Context Length | 32,768 |
+
+## Quick Start (Recommended)
+
+For **fast inference**, use the `wedlm` engine:
+
+```bash
+pip install git+https://github.com/tencent/WeDLM.git
+```
+
+```python
+from transformers import AutoTokenizer
+from wedlm import LLM, SamplingParams
+
+llm = LLM(model="tencent/WeDLM-8B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("tencent/WeDLM-8B-Instruct", trust_remote_code=True)
+
+prompt = "Solve step by step: A store sells apples for $2 each and oranges for $3 each. Tom bought 5 apples and 4 oranges. How much did he spend?"
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+outputs = llm.generate([text], SamplingParams(temperature=0.0, max_tokens=512))
+print(outputs[0]["text"])
+```
+
+### Multi-turn Conversation
+
+```python
+messages = [
+    {"role": "user", "content": "What is the derivative of x^2?"},
+    {"role": "assistant", "content": "The derivative of x² is 2x."},
+    {"role": "user", "content": "What about x^3?"}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = llm.generate([text], SamplingParams(temperature=0.0, max_tokens=256))
+```
+
+### Batch Inference
+
+```python
+prompts = [
+    "Explain quantum entanglement simply.",
+    "Write a Python function to check if a number is prime.",
+    "What are the main causes of climate change?"
+]
+messages_batch = [[{"role": "user", "content": p}] for p in prompts]
+texts = [tokenizer.apply_chat_template(m, tokenize=False, add_generation_prompt=True) for m in messages_batch]
+
+outputs = llm.generate(texts, SamplingParams(temperature=0.3, max_tokens=512))
+for i, output in enumerate(outputs):
+    print(f"=== Response {i+1} ===\n{output['text']}\n")
+```
+
+## HuggingFace Transformers
+
+For **training** or simple forward passes:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("tencent/WeDLM-8B-Instruct", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "tencent/WeDLM-8B-Instruct", 
+    trust_remote_code=True,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+messages = [{"role": "user", "content": "Hello!"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model(**inputs)
+```
+
+> ⚠️ **Note:** The HuggingFace interface is for training/forward pass convenience. For optimized inference throughput, use the `wedlm` engine above.
+
+## Performance
+
+### Generation Quality
+
+| Benchmark | Qwen3-8B-Instruct | WeDLM-8B-Instruct |
+|:----------|:-----------------:|:-----------------:|
+| ARC-C (0-shot) | 91.47 | **92.92** |
+| GSM8K (3-shot) | 89.91 | **92.27** |
+| MATH (4-shot) | **69.60** | 64.80 |
+| HumanEval (4-shot) | 71.95 | **80.49** |
+| MMLU (5-shot) | 71.52 | **75.14** |
+| GPQA-Diamond (5-shot) | 41.41 | **44.95** |
+| **Average** | 75.12 | **77.53** |
+
+### Inference Speed
+
+Speedup varies by task characteristics (measured against vLLM-optimized Qwen3-8B-Instruct):
+
+| Scenario | Speedup | Notes |
+|:---------|:-------:|:------|
+| Math Reasoning (GSM8K) | 3-6× | Structured, predictable output |
+| Code Generation | 2-3× | Deterministic syntax |
+| Open-ended QA | 1.5-2× | Higher entropy limits parallelism |
+
+## Citation (Coming soon)
+
+
+## License
+
+Apache 2.0

__init__.py

@@ -0,0 +1,23 @@
+# Copyright 2024 The WeDLM Team and 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 .configuration_wedlm import WeDLMConfig
+from .modeling_wedlm import WeDLMForCausalLM, WeDLMModel, WeDLMPreTrainedModel
+
+__all__ = [
+    "WeDLMConfig",
+    "WeDLMPreTrainedModel",
+    "WeDLMModel",
+    "WeDLMForCausalLM",
+]

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,74 @@
+{
+  "architectures": [
+    "WeDLMForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_wedlm.WeDLMConfig",
+    "AutoModelForCausalLM": "modeling_wedlm.WeDLMForCausalLM"
+  },
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "mask_token_id": null,
+  "max_position_embeddings": 16384,
+  "max_window_layers": 36,
+  "model_type": "wedlm",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "qk_norm": true,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration_wedlm.py

@@ -0,0 +1,163 @@
+# coding=utf-8
+# Copyright 2024 The WeDLM team and 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.
+"""WeDLM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class WeDLMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`WeDLMModel`]. It is used to instantiate an
+    WeDLM model according to the specified arguments, defining the model architecture.
+
+    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 151936):
+            Vocabulary size of the WeDLM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`WeDLMModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            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.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            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 1e-06):
+            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 the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings.
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers using full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        attention_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in QKV projections. Set to `True` for Qwen2.5 compatibility,
+            `False` for Qwen3 compatibility.
+        qk_norm (`bool`, *optional*, defaults to `False`):
+            Whether to use QK normalization. Set to `True` for Qwen3 compatibility.
+        head_dim (`int`, *optional*):
+            The dimension of each attention head. If not specified, defaults to hidden_size // num_attention_heads.
+    """
+
+    model_type = "wedlm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        attention_bias=True,  
+        qk_norm=False, 
+        head_dim=None,
+        mask_token_id=None,
+        **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.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if self.use_sliding_window else None
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        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.rope_scaling = rope_scaling
+        self.attention_dropout = attention_dropout
+        self.attention_bias = attention_bias
+        self.qk_norm = qk_norm 
+        self.mask_token_id = mask_token_id
+        
+        if head_dim is None:
+            self.head_dim = hidden_size // num_attention_heads
+        else:
+            self.head_dim = head_dim
+        
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        # Generate layer_types based on sliding window configuration
+        self.layer_types = [
+            "sliding_attention"
+            if self.sliding_window is not None and i >= self.max_window_layers
+            else "full_attention"
+            for i in range(self.num_hidden_layers)
+        ]
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["WeDLMConfig"]

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.56.1",
+  "trust_remote_code": true
+}

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+}

modeling_wedlm.py

@@ -0,0 +1,1004 @@
+# coding=utf-8
+# Copyright 2024 The WeDLM team and 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.
+"""PyTorch WeDLM model."""
+
+from typing import Optional, Tuple, Union, Dict, List, Callable
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from transformers import PreTrainedModel, GenerationMixin
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
+from transformers.utils.generic import check_model_inputs
+from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+
+# Import attention-related utilities
+try:
+    from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+except ImportError:
+    FlashAttentionKwargs = dict
+
+try:
+    from transformers.integrations.flash_attention import ALL_ATTENTION_FUNCTIONS
+except ImportError:
+    try:
+        from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+    except ImportError:
+        ALL_ATTENTION_FUNCTIONS = {}
+
+from .configuration_wedlm import WeDLMConfig
+
+import logging
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+
+# ============================================================================
+# Core Components (self-contained, no Qwen2 dependency)
+# ============================================================================
+
+class WeDLMMLP(nn.Module):
+    """WeDLM MLP module with SwiGLU activation."""
+    
+    def __init__(self, config: WeDLMConfig):
+        super().__init__()
+        self.config = config
+        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 = ACT2FN[config.hidden_act]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class WeDLMRMSNorm(nn.Module):
+    """WeDLM RMSNorm, equivalent to T5LayerNorm."""
+    
+    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        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) -> str:
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class WeDLMRotaryEmbedding(nn.Module):
+    """WeDLM Rotary Position Embedding."""
+    
+    def __init__(self, config: WeDLMConfig, device=None):
+        super().__init__()
+        # Determine rope_type from config
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type", "default"))
+        else:
+            self.rope_type = "default"
+        
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+        self.config = config
+        
+        # Get initialization function
+        if self.rope_type == "default":
+            inv_freq, self.attention_scaling = self._compute_default_rope_parameters(config, device)
+        else:
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+            inv_freq, self.attention_scaling = rope_init_fn(config, device)
+        
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @staticmethod
+    def _compute_default_rope_parameters(
+        config: WeDLMConfig,
+        device: Optional[torch.device] = None,
+    ) -> Tuple[torch.Tensor, float]:
+        """
+        Computes the inverse frequencies for default RoPE.
+        
+        Args:
+            config: Model configuration
+            device: Device to place the tensors on
+            
+        Returns:
+            Tuple of (inv_freq tensor, attention_scaling factor)
+        """
+        base = config.rope_theta
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        attention_factor = 1.0
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    def forward(self, x: torch.Tensor, position_ids: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Compute rotary position embeddings.
+        
+        Args:
+            x: Input tensor, used for dtype and device
+            position_ids: Position indices
+            
+        Returns:
+            Tuple of (cos, sin) tensors
+        """
+        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"
+        
+        # Force float32 computation for numerical stability
+        with torch.amp.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)
+
+
+# ============================================================================
+# Attention Utilities
+# ============================================================================
+
+def rotate_half(x: torch.Tensor) -> torch.Tensor:
+    """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: torch.Tensor, 
+    k: torch.Tensor, 
+    cos: torch.Tensor, 
+    sin: torch.Tensor, 
+    position_ids: Optional[torch.Tensor] = None, 
+    unsqueeze_dim: int = 1
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    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:
+    """
+    Repeats key/value heads to match the number of query heads (for GQA).
+    
+    Equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
+    """
+    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: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Eager (standard) attention implementation."""
+    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
+
+
+# ============================================================================
+# Attention Layer
+# ============================================================================
+
+class WeDLMAttention(nn.Module):
+    """
+    WeDLM Attention module.
+    
+    Supports both:
+    - Qwen2.5 style: with QKV bias, no QK Norm
+    - Qwen3 style: configurable QKV bias, with QK Norm
+    """
+
+    def __init__(self, config: WeDLMConfig, layer_idx: int):
+        super().__init__()
+        self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        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
+        
+        # Support configurable attention_bias (Qwen2.5: True, Qwen3: False by default)
+        attention_bias = getattr(config, "attention_bias", True)
+        
+        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=attention_bias)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=attention_bias)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=attention_bias)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+        
+        # Support optional QK Norm (Qwen3 feature)
+        self.qk_norm = getattr(config, "qk_norm", False)
+        if self.qk_norm:
+            self.q_norm = WeDLMRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.k_norm = WeDLMRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        
+        self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        if self.qk_norm:
+            # Qwen3 style: apply norm after projection, before transpose
+            query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+            key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        else:
+            # Qwen2 style: no norm
+            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:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            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)
+
+        # Select attention implementation
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager" and self.config._attn_implementation in ALL_ATTENTION_FUNCTIONS:
+            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,
+            sliding_window=self.sliding_window,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+# ============================================================================
+# Decoder Layer
+# ============================================================================
+
+class WeDLMDecoderLayer(GradientCheckpointingLayer):
+    """WeDLM Decoder Layer with pre-norm architecture."""
+    
+    def __init__(self, config: WeDLMConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = WeDLMAttention(config=config, layer_idx=layer_idx)
+        self.mlp = WeDLMMLP(config)
+        self.input_layernorm = WeDLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = WeDLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attention_type = config.layer_types[layer_idx]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states: Input tensor of shape `(batch, seq_len, embed_dim)`
+            attention_mask: Attention mask of size `(batch, sequence_length)`
+            position_ids: Position indices
+            past_key_values: Cached past key and value projection states
+            output_attentions: Whether to return attention weights
+            use_cache: Whether to use KV cache
+            cache_position: Position in the cache
+            position_embeddings: Tuple of (cos, sin) for rotary embeddings
+        """
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Feed Forward
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+# ============================================================================
+# Model Classes
+# ============================================================================
+
+@auto_docstring
+class WeDLMPreTrainedModel(PreTrainedModel):
+    """Base class for WeDLM models."""
+    
+    config_class = WeDLMConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["WeDLMDecoderLayer"]
+    _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": WeDLMDecoderLayer,
+        "attentions": WeDLMAttention,
+    }
+
+
+@auto_docstring
+class WeDLMModel(WeDLMPreTrainedModel):
+    """
+    WeDLM base model outputting raw hidden states.
+    """
+    
+    def __init__(self, config: WeDLMConfig):
+        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(
+            [WeDLMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = WeDLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = WeDLMRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @check_model_inputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = 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,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> 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 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 use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # Prepare attention masks
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            mask_kwargs = {
+                "config": self.config,
+                "input_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+            }
+            if self.has_sliding_layers:
+                causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
+
+        hidden_states = inputs_embeds
+
+        # Create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # Decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+@auto_docstring
+class WeDLMForCausalLM(WeDLMPreTrainedModel, GenerationMixin):
+    """
+    WeDLM Model for Causal Language Modeling with WeDLM block decoding support.
+    """
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: WeDLMConfig):
+        super().__init__(config)
+        self.model = WeDLMModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        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 set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def _efficient_reorder_sequence(
+        self, 
+        tokens: torch.Tensor, 
+        mask_indices: torch.Tensor, 
+        position_ids: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Helper function to reorder sequence by moving MASK parts to the end.
+        """
+        reordered_tokens = torch.cat((tokens[~mask_indices], tokens[mask_indices]))
+        reordered_position_ids = torch.cat((position_ids[~mask_indices], position_ids[mask_indices]))
+        return reordered_tokens, reordered_position_ids
+
+    @torch.no_grad()
+    def _generate_one_block(
+        self,
+        prefix_ids: torch.Tensor,
+        prefix_position_ids: torch.Tensor,
+        block_size: int,
+        mask_token_id: int,
+        confidence_threshold: float = 0.0,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, Dict]:
+        """
+        Generate one block of content based on the given prefix.
+        
+        Args:
+            prefix_ids: Current sequence token IDs
+            prefix_position_ids: Position IDs for current sequence
+            block_size: Number of tokens to generate in this block
+            mask_token_id: Token ID for MASK token
+            confidence_threshold: Minimum confidence to accept a prediction
+            temperature: Sampling temperature
+            top_p: Nucleus sampling parameter (unused currently)
+            top_k: Top-k sampling parameter (unused currently)
+            
+        Returns:
+            Tuple of (updated_ids, updated_position_ids, block_statistics)
+        """
+        device = prefix_ids.device
+        
+        # 1. Append a block of MASK tokens after the current prefix
+        mask_tensor = torch.full((block_size,), mask_token_id, dtype=torch.long, device=device)
+        current_ids = torch.cat([prefix_ids, mask_tensor])
+        
+        # Create position encodings for the newly added MASKs
+        start_pos = prefix_position_ids[-1].item() + 1 if len(prefix_position_ids) > 0 else 0
+        mask_position_ids = torch.arange(start_pos, start_pos + block_size, dtype=torch.long, device=device)
+        original_position_ids = torch.cat([prefix_position_ids, mask_position_ids])
+        
+        # Mark which positions are MASK
+        is_mask = (current_ids == mask_token_id)
+        
+        # Statistics
+        block_stats = {
+            'steps': 0,
+            'tokens_generated': 0,
+            'tokens_per_step': [],
+            'max_confidences': [],
+        }
+        
+        # 2. WeDLM iteration within the block
+        for step in range(block_size):
+            if not is_mask.any():
+                break
+            
+            block_stats['steps'] += 1
+            
+            # 2.1 Reorder sequence
+            reordered_ids, reordered_position_ids = self._efficient_reorder_sequence(
+                current_ids, is_mask, original_position_ids
+            )
+            
+            # 2.2 Prepare input
+            input_ids = reordered_ids.unsqueeze(0)
+            position_ids = reordered_position_ids.unsqueeze(0)
+            
+            seq_len = input_ids.shape[1]
+            attention_mask = torch.ones((1, seq_len), dtype=torch.long, device=device)
+            
+            # 2.3 Model forward pass
+            outputs = self.model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                use_cache=False,
+                return_dict=True,
+            )
+            
+            hidden_states = outputs.last_hidden_state
+            logits = self.lm_head(hidden_states)
+            
+            # 2.4 Get logits for MASK positions
+            num_non_mask = (~is_mask).sum().item()
+            mask_logits = logits[0, num_non_mask:]
+            
+            if mask_logits.size(0) == 0:
+                break
+            
+            mask_logits = mask_logits / temperature
+            probs = F.softmax(mask_logits, dim=-1)
+            max_probs, predicted_ids = probs.max(dim=-1)
+            
+            block_stats['max_confidences'].append(max_probs.max().item())
+            
+            # 2.5 Select positions to fill
+            if confidence_threshold > 0.0:
+                above_threshold_mask = max_probs >= confidence_threshold
+                
+                if above_threshold_mask.any():
+                    indices_to_fill = above_threshold_mask.nonzero(as_tuple=True)[0]
+                    num_tokens_this_step = len(indices_to_fill)
+                else:
+                    best_idx = max_probs.argmax()
+                    indices_to_fill = best_idx.unsqueeze(0)
+                    num_tokens_this_step = 1
+            else:
+                best_idx = max_probs.argmax()
+                indices_to_fill = best_idx.unsqueeze(0)
+                num_tokens_this_step = 1
+            
+            block_stats['tokens_per_step'].append(num_tokens_this_step)
+            block_stats['tokens_generated'] += num_tokens_this_step
+            
+            # 2.6 Update all selected positions
+            for idx in indices_to_fill:
+                idx_item = idx.item()
+                best_token_id = predicted_ids[idx_item].item()
+                
+                best_pos_in_reordered = num_non_mask + idx_item
+                original_pos_value = reordered_position_ids[best_pos_in_reordered].item()
+                original_pos_in_seq = (original_position_ids == original_pos_value).nonzero(as_tuple=True)[0].item()
+                
+                current_ids[original_pos_in_seq] = best_token_id
+                is_mask[original_pos_in_seq] = False
+        
+        return current_ids, original_position_ids, block_stats
+
+    @torch.no_grad()
+    def generate_wedlm(
+        self,
+        input_ids: torch.LongTensor,
+        max_new_tokens: int,
+        block_size: int,
+        mask_token_id: Optional[int] = None,
+        confidence_threshold: float = 0.0,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 0,
+        pad_token_id: Optional[int] = None,
+        return_stats: bool = True,
+        **kwargs
+    ) -> Union[torch.LongTensor, Dict]:
+        """
+        Generate text using WeDLM block decoding mode.
+        
+        Args:
+            input_ids: Input token IDs of shape (batch_size, seq_len)
+            max_new_tokens: Maximum number of new tokens to generate
+            block_size: Number of tokens to generate per block
+            mask_token_id: Token ID for MASK token
+            confidence_threshold: Minimum confidence to accept predictions (0.0-1.0)
+            temperature: Sampling temperature
+            top_p: Nucleus sampling parameter
+            top_k: Top-k sampling parameter
+            pad_token_id: Token ID for padding
+            return_stats: Whether to return generation statistics
+            
+        Returns:
+            If return_stats=False: Generated token sequences
+            If return_stats=True: Dict with 'sequences' and 'stats'
+        """
+        if mask_token_id is None:
+            mask_token_id = getattr(self.config, "mask_token_id", None)
+            if mask_token_id is None:
+                raise ValueError("mask_token_id must be provided or set in config")
+        
+        if pad_token_id is None:
+            pad_token_id = self.config.pad_token_id
+        
+        if not 0.0 <= confidence_threshold <= 1.0:
+            raise ValueError(f"confidence_threshold must be between 0 and 1, got {confidence_threshold}")
+        
+        batch_size = input_ids.shape[0]
+        device = input_ids.device
+        
+        num_blocks = (max_new_tokens + block_size - 1) // block_size
+        
+        logger.info(
+            f"Starting WeDLM generation: max_new_tokens={max_new_tokens}, block_size={block_size}, "
+            f"confidence_threshold={confidence_threshold}, num_blocks={num_blocks}"
+        )
+        
+        all_generated = []
+        all_sample_stats = []
+        
+        for batch_idx in range(batch_size):
+            sample_ids = input_ids[batch_idx]
+            if pad_token_id is not None:
+                pad_mask = (sample_ids != pad_token_id)
+                if pad_mask.any():
+                    valid_length = pad_mask.sum().item()
+                    prefix_ids = sample_ids[:valid_length]
+                else:
+                    prefix_ids = sample_ids
+            else:
+                prefix_ids = sample_ids
+            
+            prefix_length = prefix_ids.shape[0]
+            current_position_ids = torch.arange(prefix_length, dtype=torch.long, device=device)
+            
+            current_ids = prefix_ids.clone()
+            
+            sample_stats = {
+                'input_length': prefix_length,
+                'total_steps': 0,
+                'total_tokens_generated': 0,
+                'blocks': [],
+            }
+            
+            for block_idx in range(num_blocks):
+                remaining_tokens = max_new_tokens - block_idx * block_size
+                current_block_size = min(block_size, remaining_tokens)
+                
+                logger.debug(
+                    f"Batch {batch_idx}, Block {block_idx}/{num_blocks}: "
+                    f"generating {current_block_size} tokens"
+                )
+                
+                current_ids, current_position_ids, block_stats = self._generate_one_block(
+                    prefix_ids=current_ids,
+                    prefix_position_ids=current_position_ids,
+                    block_size=current_block_size,
+                    mask_token_id=mask_token_id,
+                    confidence_threshold=confidence_threshold,
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                )
+                
+                sample_stats['total_steps'] += block_stats['steps']
+                sample_stats['total_tokens_generated'] += block_stats['tokens_generated']
+                sample_stats['blocks'].append(block_stats)
+            
+            sample_stats['actual_tokens_generated'] = len(current_ids) - prefix_length
+            sample_stats['output_length'] = len(current_ids)
+            
+            all_generated.append(current_ids)
+            all_sample_stats.append(sample_stats)
+        
+        max_length = max(seq.shape[0] for seq in all_generated)
+        padded_sequences = []
+        
+        for seq in all_generated:
+            if seq.shape[0] < max_length:
+                padding = torch.full(
+                    (max_length - seq.shape[0],),
+                    pad_token_id if pad_token_id is not None else 0,
+                    dtype=torch.long,
+                    device=device
+                )
+                seq = torch.cat([seq, padding])
+            padded_sequences.append(seq)
+        
+        result_sequences = torch.stack(padded_sequences, dim=0)
+        
+        total_steps = sum(s['total_steps'] for s in all_sample_stats)
+        total_tokens = sum(s['total_tokens_generated'] for s in all_sample_stats)
+        avg_tokens_per_step = total_tokens / total_steps if total_steps > 0 else 0
+        
+        logger.info(
+            f"WeDLM generation completed: "
+            f"total_steps={total_steps}, "
+            f"total_tokens_generated={total_tokens}, "
+            f"avg_tokens_per_step={avg_tokens_per_step:.2f}"
+        )
+        
+        if not return_stats:
+            return result_sequences
+        
+        return {
+            'sequences': result_sequences,
+            'stats': {
+                'total_steps': total_steps,
+                'total_tokens_generated': total_tokens,
+                'average_tokens_per_step': avg_tokens_per_step,
+                'efficiency_ratio': total_tokens / total_steps if total_steps > 0 else 0,
+                'per_sample_stats': all_sample_stats,
+                'config': {
+                    'batch_size': batch_size,
+                    'max_new_tokens': max_new_tokens,
+                    'block_size': block_size,
+                    'confidence_threshold': confidence_threshold,
+                    'temperature': temperature,
+                }
+            }
+        }
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = 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,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        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,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        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)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        **kwargs
+    ):
+        if past_key_values is not None:
+            if inputs_embeds is not None:
+                input_ids = input_ids[:, -cache_position.shape[0]:]
+            elif input_ids.shape[1] != cache_position.shape[0]:
+                input_ids = input_ids[:, cache_position]
+
+        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 cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+        else:
+            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
+
+        if isinstance(past_key_values, DynamicCache) and attention_mask.ndim == 2:
+            model_inputs["cache_position"] = cache_position
+            model_inputs["past_key_values"] = past_key_values
+            model_inputs["use_cache"] = use_cache
+            model_inputs["position_ids"] = position_ids
+            model_inputs["attention_mask"] = attention_mask
+            return model_inputs
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+
+__all__ = [
+    "WeDLMConfig",
+    "WeDLMPreTrainedModel", 
+    "WeDLMModel",
+    "WeDLMForCausalLM",
+]

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}