diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..52373fe24473b1aa44333d318f578ae6bf04b49b 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -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
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1057cdef58879a580976be0945fd13cfd6485c9c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,152 @@
+---
+language:
+ - en
+ - zh
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+---
+
+# GLM-5
+
+
+
+
+ 👋 Join our WeChat or Discord community.
+
+ 📖 Check out the GLM-5 technical blog.
+
+ 📍 Use GLM-5 API services on Z.ai API Platform.
+
+ 👉 One click to GLM-5.
+
+
+## Introduction
+
+We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
+
+Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed [slime](https://github.com/THUDM/slime), a novel **asynchronous RL infrastructure** that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.
+
+## Benchmark
+
+| | GLM-5 | GLM-4.7 | DeepSeek-V3.2 | Kimi K2.5 | Claude Opus 4.5 | Gemini 3 Pro | GPT-5.2 (xhigh) |
+| -------------------------------- | ---------------------- | --------- | ------------- |-----------| --------------- | ------------ | --------------- |
+| HLE | 30.5 | 24.8 | 25.1 | 31.5 | 28.4 | 37.2 | 35.4 |
+| HLE (w/ Tools) | 50.4 | 42.8 | 40.8 | 51.8 | 43.4* | 45.8* | 45.5* |
+| AIME 2026 I | 92.7 | 92.9 | 92.7 | 92.5 | 93.3 | 90.6 | - |
+| HMMT Nov. 2025 | 96.9 | 93.5 | 90.2 | 91.1 | 91.7 | 93.0 | 97.1 |
+| IMOAnswerBench | 82.5 | 82.0 | 78.3 | 81.8 | 78.5 | 83.3 | 86.3 |
+| GPQA-Diamond | 86.0 | 85.7 | 82.4 | 87.6 | 87.0 | 91.9 | 92.4 |
+| SWE-bench Verified | 77.8 | 73.8 | 73.1 | 76.8 | 80.9 | 76.2 | 80.0 |
+| SWE-bench Multilingual | 73.3 | 66.7 | 70.2 | 73.0 | 77.5 | 65.0 | 72.0 |
+| Terminal-Bench 2.0 (Terminus 2) | 56.2 / 60.7 † | 41.0 | 39.3 | 50.8 | 59.3 | 54.2 | 54.0 |
+| Terminal-Bench 2.0 (Claude Code) | 56.2 / 61.1 † | 32.8 | 46.4 | - | 57.9 | - | - |
+| CyberGym | 43.2 | 23.5 | 17.3 | 41.3 | 50.6 | 39.9 | - |
+| BrowseComp | 62.0 | 52.0 | 51.4 | 60.6 | 37.0 | 37.8 | - |
+| BrowseComp (w/ Context Manage) | 75.9 | 67.5 | 67.6 | 74.9 | 67.8 | 59.2 | 65.8 |
+| BrowseComp-Zh | 72.7 | 66.6 | 65.0 | 62.3 | 62.4 | 66.8 | 76.1 |
+| τ²-Bench | 89.7 | 87.4 | 85.3 | 80.2 | 91.6 | 90.7 | 85.5 |
+| MCP-Atlas (Public Set) | 67.8 | 52.0 | 62.2 | 63.8 | 65.2 | 66.6 | 68.0 |
+| Tool-Decathlon | 38.0 | 23.8 | 35.2 | 27.8 | 43.5 | 36.4 | 46.3 |
+| Vending Bench 2 | $4,432.12 | $2,376.82 | $1,034.00 | $1,198.46 | $4,967.06 | $5,478.16 | $3,591.33 |
+
+> *: refers to their scores of full set.
+>
+> †: A verified version of Terminal-Bench 2.0 that fixes some ambiguous instructions.
+See footnote for more evaluation details.
+
+### Footnote
+
+* **Humanity’s Last Exam (HLE) & other reasoning tasks**: We evaluate with a maximum generation length of 131,072 tokens (`temperature=1.0, top_p=0.95, max_new_tokens=131072`). By default, we report the text-only subset; results marked with * are from the full set. We use GPT-5.2 (medium) as the judge model. For HLE-with-tools, we use a maximum context length of 202,752 tokens.
+* **SWE-bench & SWE-bench Multilingual**: We run the SWE-bench suite with OpenHands using a tailored instruction prompt. Settings: `temperature=0.7, top_p=0.95, max_new_tokens=16384`, with a 200K context window.
+* **BrowserComp**: Without context management, we retain details from the most recent 5 turns. With context management, we use the same discard-all strategy as DeepSeek-v3.2 and Kimi K2.5.
+* **Terminal-Bench 2.0 (Terminus 2)**: We evaluate with the Terminus framework using `timeout=2h, temperature=0.7, top_p=1.0, max_new_tokens=8192`, with a 128K context window. Resource limits are capped at 16 CPUs and 32 GB RAM.
+* **Terminal-Bench 2.0 (Claude Code)**: We evaluate in Claude Code 2.1.14 (think mode, default effort) with `temperature=1.0, top_p=0.95, max_new_tokens=65536`. We remove wall-clock time limits due to generation speed, while preserving per-task CPU and memory constraints. Scores are averaged over 5 runs. We fix environment issues introduced by Claude Code and also report results on a verified Terminal-Bench 2.0 dataset that resolves ambiguous instructions (see: [https://huggingface.co/datasets/zai-org/terminal-bench-2-verified](https://huggingface.co/datasets/zai-org/terminal-bench-2-verified)).
+* **CyberGym**: We evaluate in Claude Code 2.1.18 (think mode, no web tools) with (`temperature=1.0, top_p=1.0, max_new_tokens=32000`) and a 250-minute timeout per task. Results are single-run Pass@1 over 1,507 tasks.
+* **MCP-Atlas**: All models are evaluated in think mode on the 500-task public subset with a 10-minute timeout per task. We use Gemini 3 Pro as the judge model.
+* **τ²-bench**: We add a small prompt adjustment in Retail and Telecom to avoid failures caused by premature user termination. For Airline, we apply the domain fixes proposed in the Claude Opus 4.5 system card.
+* **Vending Bench 2**: Runs are conducted independently by [Andon Labs](https://andonlabs.com/evals/vending-bench-2).
+
+
+## Serve GLM-5 Locally
+
+### Prepare environment
+
+vLLM, SGLang, KTransformers, and xLLM all support local deployment of GLM-5. A simple deployment guide is provided here.
+
++ vLLM
+
+ Using Docker as:
+
+ ```shell
+ docker pull vllm/vllm-openai:nightly
+ ```
+
+ or using pip:
+
+ ```shell
+ pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
+ ```
+
+ then upgrade transformers:
+
+ ```
+ pip install git+https://github.com/huggingface/transformers.git
+ ```
+
++ SGLang
+
+ Using Docker as:
+ ```bash
+ docker pull lmsysorg/sglang:glm5-hopper # For Hopper GPU
+ docker pull lmsysorg/sglang:glm5-blackwell # For Blackwell GPU
+ ```
+
+### Deploy
+
++ vLLM
+
+ ```shell
+ vllm serve zai-org/GLM-5-FP8 \
+ --tensor-parallel-size 8 \
+ --gpu-memory-utilization 0.85 \
+ --speculative-config.method mtp \
+ --speculative-config.num_speculative_tokens 1 \
+ --tool-call-parser glm47 \
+ --reasoning-parser glm45 \
+ --enable-auto-tool-choice \
+ --served-model-name glm-5-fp8
+ ```
+
+ Check the [recipes](https://github.com/vllm-project/recipes/blob/main/GLM/GLM5.md) for more details.
+
++ SGLang
+
+ ```shell
+ python3 -m sglang.launch_server \
+ --model-path zai-org/GLM-5-FP8 \
+ --tp-size 8 \
+ --tool-call-parser glm47 \
+ --reasoning-parser glm45 \
+ --speculative-algorithm EAGLE \
+ --speculative-num-steps 3 \
+ --speculative-eagle-topk 1 \
+ --speculative-num-draft-tokens 4 \
+ --mem-fraction-static 0.85 \
+ --served-model-name glm-5-fp8
+ ```
+
+ Check the [sglang cookbook](https://cookbook.sglang.io/autoregressive/GLM/GLM-5) for more details.
+
++ xLLM and other Ascend NPU
+
+ Please check the deployment guide [here](https://github.com/zai-org/GLM-5/blob/main/example/ascend.md).
+
++ KTransformers
+
+ Please check the deployment guide [here](https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/kt-kernel/GLM-5-Tutorial.md).
+
+## Citation
+
+Our technical report is coming soon.
\ No newline at end of file
diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000000000000000000000000000000000000..2ab98ef068d62829d17c5ade1827b9f013fa2bbf
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,86 @@
+[gMASK]
+{%- if tools -%}
+<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within XML tags:
+
+{% for tool in tools %}
+{{ tool | tojson(ensure_ascii=False) }}
+{% endfor %}
+
+
+For each function call, output the function name and arguments within the following XML format:
+{function-name}{arg-key-1}{arg-value-1}{arg-key-2}{arg-value-2}...{%- endif -%}
+{%- macro visible_text(content) -%}
+ {%- if content is string -%}
+ {{- content }}
+ {%- elif content is iterable and content is not mapping -%}
+ {%- for item in content -%}
+ {%- if item is mapping and item.type == 'text' -%}
+ {{- item.text }}
+ {%- elif item is string -%}
+ {{- item }}
+ {%- endif -%}
+ {%- endfor -%}
+ {%- else -%}
+ {{- content }}
+ {%- endif -%}
+{%- endmacro -%}
+{%- set ns = namespace(last_user_index=-1) %}
+{%- for m in messages %}
+ {%- if m.role == 'user' %}
+ {% set ns.last_user_index = loop.index0 -%}
+ {%- endif %}
+{%- endfor %}
+{% for m in messages %}
+{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- set reasoning_content = '' %}
+{%- set content = visible_text(m.content) %}
+{%- if m.reasoning_content is string %}
+ {%- set reasoning_content = m.reasoning_content %}
+{%- else %}
+ {%- if '' in content %}
+ {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %}
+ {%- set content = content.split('')[-1].lstrip('\n') %}
+ {%- endif %}
+{%- endif %}
+{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%}
+{{ '' + reasoning_content.strip() + ''}}
+{%- else -%}
+{{ '' }}
+{%- endif -%}
+{%- if content.strip() -%}
+{{ content.strip() }}
+{%- endif -%}
+{% if m.tool_calls %}
+{% for tc in m.tool_calls %}
+{%- if tc.function %}
+ {%- set tc = tc.function %}
+{%- endif %}
+{{- '' + tc.name -}}
+{% set _args = tc.arguments %}{% for k, v in _args.items() %}{{ k }}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{% endfor %}{% endfor %}
+{% endif %}
+{%- elif m.role == 'tool' -%}
+{%- if m.content is string -%}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+ {{- '<|observation|>' }}
+{%- endif %}
+{{- '' }}
+{{- m.content }}
+{{- '' }}
+{%- else -%}
+<|observation|>{% for tr in m.content %}
+{{ tr.output if tr.output is defined else tr }}{% endfor -%}
+{% endif -%}
+{%- elif m.role == 'system' -%}
+<|system|>{{ visible_text(m.content) }}
+{%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+ <|assistant|>{{- '' if (enable_thinking is defined and not enable_thinking) else '' -}}
+{%- endif -%}
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..37a225482af690fc85a716b9a8ceee3c220a97ac
--- /dev/null
+++ b/config.json
@@ -0,0 +1,63 @@
+{
+ "architectures": [
+ "DeepseekV32ForCausalLM"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "dtype": "bfloat16",
+ "eos_token_id": [
+ 154820,
+ 154827,
+ 154829
+ ],
+ "ep_size": 1,
+ "first_k_dense_replace": 3,
+ "hidden_act": "silu",
+ "head_dim": 64,
+ "hidden_size": 6144,
+ "index_head_dim": 128,
+ "index_n_heads": 32,
+ "index_topk": 2048,
+ "indexer_rope_interleave": true,
+ "initializer_range": 0.02,
+ "intermediate_size": 12288,
+ "kv_lora_rank": 512,
+ "max_position_embeddings": 202752,
+ "moe_intermediate_size": 2048,
+ "moe_layer_freq": 1,
+ "model_type": "deepseek_v32",
+ "n_group": 1,
+ "n_routed_experts": 256,
+ "n_shared_experts": 1,
+ "norm_topk_prob": true,
+ "num_attention_heads": 64,
+ "num_experts_per_tok": 8,
+ "num_hidden_layers": 20,
+ "num_key_value_heads": 64,
+ "num_nextn_predict_layers": 1,
+ "pad_token_id": 154820,
+ "pretraining_tp": 1,
+ "q_lora_rank": 2048,
+ "qk_head_dim": 256,
+ "qk_nope_head_dim": 192,
+ "qk_rope_head_dim": 64,
+ "rms_norm_eps": 1e-05,
+ "rope_interleave": true,
+ "rope_parameters": {
+ "rope_theta": 1000000,
+ "rope_type": "default"
+ },
+ "routed_scaling_factor": 2.5,
+ "scoring_func": "sigmoid",
+ "tie_word_embeddings": false,
+ "topk_group": 1,
+ "topk_method": "noaux_tc",
+ "transformers_version": "5.0.2.dev0",
+ "use_cache": true,
+ "v_head_dim": 256,
+ "vocab_size": 154880,
+ "auto_map": {
+ "AutoConfig": "configuration_deepseek_v32.DeepseekV32Config",
+ "AutoModelForCausalLM": "modeling_deepseek_v32.DeepseekV32ForCausalLM"
+ }
+}
\ No newline at end of file
diff --git a/configuration_deepseek_v32.py b/configuration_deepseek_v32.py
new file mode 100644
index 0000000000000000000000000000000000000000..7874bb589d3573559f808da87298cda24e97f726
--- /dev/null
+++ b/configuration_deepseek_v32.py
@@ -0,0 +1,61 @@
+# coding=utf-8
+# Copyright 2025 bzantium and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on the DeepSeekV3 implementations from the DeepSeek AI team. (https://huggingface.co/deepseek-ai/DeepSeek-V3)
+
+# 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.
+"""DeepSeekV3.2 model configuration"""
+
+from typing import Optional
+
+from transformers.models.deepseek_v3.configuration_deepseek_v3 import DeepseekV3Config
+
+
+DEEPSEEK_V32_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class DeepseekV32Config(DeepseekV3Config):
+ r"""
+ This is the configuration class to store the configuration of a [`DeepseekV32Model`]. It is used to instantiate a DeepSeek
+ V3.2 model according to the specified arguments, defining the model architecture.
+
+ DeepSeek V3.2 extends DeepSeek V3 with native sparse attention mechanism using an indexer for efficient
+ attention computation on long sequences.
+ Configuration objects inherit from [`DeepseekV3Config`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+ Args:
+ index_topk (`int`, *optional*, defaults to 2048):
+ Number of top-k tokens to select for sparse attention. This enables the native sparse attention
+ mechanism in DeepSeek V3.2.
+ **kwargs:
+ All other arguments from DeepseekV3Config.
+ ```python
+ >>> from transformers import DeepseekV32Model, DeepseekV32Config
+ >>> # Initializing a Deepseek-V3.2 style configuration
+ >>> configuration = DeepseekV32Config()
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "deepseek_v32"
+
+ def __init__(
+ self,
+ index_topk: Optional[int] = 2048,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.index_topk = index_topk
+
+
+__all__ = ["DeepseekV32Config"]
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..640e99c64d2f17d76e2f1f13af219fb369e1004e
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,12 @@
+{
+ "_from_model_config": true,
+ "eos_token_id": [
+ 154820,
+ 154827,
+ 154829
+ ],
+ "pad_token_id": 154820,
+ "temperature": 1.0,
+ "top_p": 0.95,
+ "transformers_version": "5.0.2.dev0"
+}
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+ }
+}
\ No newline at end of file
diff --git a/modeling_deepseek_v32.py b/modeling_deepseek_v32.py
new file mode 100644
index 0000000000000000000000000000000000000000..97ad9e1bc830d4a9ec96e21c502f55a8388ed9e7
--- /dev/null
+++ b/modeling_deepseek_v32.py
@@ -0,0 +1,339 @@
+import math
+import warnings
+from collections.abc import Callable
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers import initialization as init
+from transformers.cache_utils import Cache
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GenericForSequenceClassification, GenericForTokenClassification
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import logging
+from transformers.models.deepseek_v3.modeling_deepseek_v3 import (
+ DeepseekV3Attention,
+ DeepseekV3DecoderLayer,
+ DeepseekV3ForCausalLM,
+ DeepseekV3MLP,
+ DeepseekV3Model,
+ DeepseekV3MoE,
+ DeepseekV3PreTrainedModel,
+ DeepseekV3RMSNorm,
+ DeepseekV3RotaryEmbedding,
+ apply_rotary_pos_emb_interleave,
+ yarn_get_mscale,
+)
+from transformers.models.llama.modeling_llama import (
+ apply_rotary_pos_emb,
+ eager_attention_forward,
+)
+from configuration_deepseek_v32 import DeepseekV32Config
+
+
+logger = logging.get_logger(__name__)
+
+
+class DeepseekV32RMSNorm(DeepseekV3RMSNorm):
+ pass
+
+
+class DeepseekV32RotaryEmbedding(DeepseekV3RotaryEmbedding):
+ pass
+
+
+class DeepseekV32MLP(DeepseekV3MLP):
+ pass
+
+
+class DeepseekV32MoE(DeepseekV3MoE):
+ pass
+
+
+class DeepseekV32SparseAttention(nn.Module):
+ """
+ DeepSeek V3.2 sparse attention mechanism with indexer.
+
+ This implements the native sparse attention from DeepSeek V3.2 which uses
+ an indexer to select top-k tokens for attention computation, making it
+ more efficient for long sequences.
+ """
+
+ def __init__(self, config: DeepseekV32Config, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.attention_dropout = config.attention_dropout
+ self.num_heads = config.num_attention_heads
+
+ self.q_lora_rank = config.q_lora_rank
+ self.qk_rope_head_dim = config.qk_rope_head_dim
+ self.kv_lora_rank = config.kv_lora_rank
+ self.v_head_dim = config.v_head_dim
+ self.qk_nope_head_dim = config.qk_nope_head_dim
+ self.qk_head_dim = config.qk_head_dim
+ self.index_topk = config.index_topk
+
+ self.is_causal = True
+
+ # Query projection
+ if self.q_lora_rank is None:
+ self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
+ else:
+ self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
+ self.q_a_layernorm = DeepseekV32RMSNorm(config.q_lora_rank)
+ self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
+
+ # Key-Value projections
+ self.kv_a_proj_with_mqa = nn.Linear(
+ config.hidden_size,
+ self.kv_lora_rank + self.qk_rope_head_dim,
+ bias=config.attention_bias,
+ )
+ self.kv_a_layernorm = DeepseekV32RMSNorm(self.kv_lora_rank)
+ self.kv_b_proj = nn.Linear(
+ self.kv_lora_rank,
+ self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
+ bias=False,
+ )
+
+ # Output projection
+ self.o_proj = nn.Linear(
+ self.num_heads * self.v_head_dim,
+ config.hidden_size,
+ bias=config.attention_bias,
+ )
+
+ # Indexer components for sparse attention
+ self.wq_b = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
+ self.wk = nn.Linear(config.hidden_size, self.qk_head_dim, bias=config.attention_bias)
+ self.k_norm = DeepseekV32RMSNorm(self.qk_head_dim)
+ self.weights_proj = nn.Linear(config.hidden_size, self.num_heads, bias=False)
+
+ self.scaling = self.qk_head_dim ** (-0.5)
+ if self.config.rope_parameters.get("rope_type", "default") != "default":
+ mscale_all_dim = self.config.rope_parameters.get("mscale_all_dim", 0)
+ scaling_factor = self.config.rope_parameters["factor"]
+ if mscale_all_dim:
+ mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+ self.scaling = self.scaling * mscale * mscale
+
+ 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: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ batch_size, seq_length = hidden_states.shape[:-1]
+
+ # For training or when index_topk is not effective, fall back to standard attention
+ # This is a simplified implementation - in practice, you'd implement the full sparse indexer
+ if self.training or seq_length <= self.index_topk:
+ warnings.warn(
+ "DeepSeek V3.2 sparse attention is not fully implemented in this version. "
+ "Falling back to standard attention. For production use, please use vLLM or "
+ "other optimized inference engines.",
+ UserWarning,
+ )
+ return self._standard_attention(
+ hidden_states, position_embeddings, attention_mask, past_key_values, cache_position, **kwargs
+ )
+
+ # Sparse attention implementation would go here
+ # This requires custom CUDA kernels for efficient top-k selection and indexing
+ return self._standard_attention(
+ hidden_states, position_embeddings, attention_mask, past_key_values, cache_position, **kwargs
+ )
+
+ def _standard_attention(
+ 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: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Standard attention fallback (same as DeepSeek V3)"""
+ batch_size, seq_length = hidden_states.shape[:-1]
+ query_shape = (batch_size, seq_length, -1, self.qk_head_dim)
+ key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)
+
+ if self.q_lora_rank is None:
+ q_states = self.q_proj(hidden_states)
+ else:
+ q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+ q_states = q_states.view(query_shape).transpose(1, 2)
+ q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+
+ compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+ k_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+
+ k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(key_shape).transpose(1, 2)
+ k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+
+ k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim)
+
+ cos, sin = position_embeddings
+ if self.config.rope_interleave:
+ q_rot, k_rot = apply_rotary_pos_emb_interleave(q_rot, k_rot, cos, sin)
+ else:
+ q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin)
+ k_rot = k_rot.expand(*k_pass.shape[:-1], -1)
+
+ query_states = torch.cat((q_pass, q_rot), dim=-1)
+ key_states = torch.cat((k_pass, k_rot), dim=-1)
+
+ if past_key_values is not None:
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim:
+ value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim])
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim:
+ attn_output = attn_output[:, :, :, : self.v_head_dim]
+
+ attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class DeepseekV32DecoderLayer(nn.Module):
+ def __init__(self, config: DeepseekV32Config, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ # Use sparse attention for V3.2
+ self.self_attn = DeepseekV32SparseAttention(config=config, layer_idx=layer_idx)
+
+ if layer_idx >= config.first_k_dense_replace:
+ self.mlp = DeepseekV32MoE(config)
+ else:
+ self.mlp = DeepseekV32MLP(config)
+
+ self.input_layernorm = DeepseekV32RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = DeepseekV32RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> torch.Tensor:
+ 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
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states
+
+
+class DeepseekV32PreTrainedModel(DeepseekV3PreTrainedModel):
+ config_class = DeepseekV32Config
+ _can_compile_fullgraph = False
+ _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
+
+
+class DeepseekV32Model(DeepseekV3Model):
+ """
+ DeepSeek V3.2 Model with native sparse attention.
+
+ This model extends DeepSeek V3 with an efficient sparse attention mechanism
+ that uses an indexer to select top-k tokens for attention computation.
+ """
+ config_class = DeepseekV32Config
+ _keys_to_ignore_on_load_unexpected = [r"model\.layers\.61.*"]
+
+ def __init__(self, config: DeepseekV32Config):
+ # Skip DeepseekV3Model.__init__ and go directly to PreTrainedModel
+ DeepseekV3PreTrainedModel.__init__(self, 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)
+ # Use V3.2-specific decoder layers
+ self.layers = nn.ModuleList(
+ [DeepseekV32DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = DeepseekV32RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = DeepseekV32RotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+
+class DeepseekV32ForCausalLM(DeepseekV3ForCausalLM):
+ """
+ DeepSeek V3.2 Model for causal language modeling with sparse attention.
+ """
+ config_class = DeepseekV32Config
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super(DeepseekV3ForCausalLM, self).__init__(config)
+ self.model = DeepseekV32Model(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()
+
+
+class DeepseekV32ForSequenceClassification(GenericForSequenceClassification, DeepseekV32PreTrainedModel):
+ pass
+
+
+class DeepseekV32ForTokenClassification(GenericForTokenClassification, DeepseekV32PreTrainedModel):
+ pass
+
+
+__all__ = [
+ "DeepseekV32PreTrainedModel",
+ "DeepseekV32Model",
+ "DeepseekV32ForCausalLM",
+ "DeepseekV32ForSequenceClassification",
+ "DeepseekV32ForTokenClassification",
+]
\ No newline at end of file
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..aba40197a4cdb5607f4ab7a05fb0a4ee8054fd6d
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d
+size 20217442
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1723f7d90e3fb497303ec7b18f88cf5d05928f37
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,33 @@
+{
+ "backend": "tokenizers",
+ "clean_up_tokenization_spaces": false,
+ "do_lower_case": false,
+ "eos_token": "<|endoftext|>",
+ "extra_special_tokens": [
+ "<|endoftext|>",
+ "[MASK]",
+ "[gMASK]",
+ "[sMASK]",
+ "",
+ "",
+ "<|system|>",
+ "<|user|>",
+ "<|assistant|>",
+ "<|observation|>",
+ "<|begin_of_image|>",
+ "<|end_of_image|>",
+ "<|begin_of_video|>",
+ "<|end_of_video|>",
+ "<|begin_of_audio|>",
+ "<|end_of_audio|>",
+ "<|begin_of_transcription|>",
+ "<|end_of_transcription|>"
+ ],
+ "is_local": true,
+ "model_max_length": 202752,
+ "model_specific_special_tokens": {},
+ "pad_token": "<|endoftext|>",
+ "padding_side": "left",
+ "remove_space": false,
+ "tokenizer_class": "TokenizersBackend"
+}