Initial commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
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+*model.safetensors.index.json* filter=lfs diff=lfs merge=lfs -text

LICENSE

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+Modified MIT License
+
+Copyright (c) 2025 Nvidia
+Copyright (c) 2025 Moonshot AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the “Software”), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Our only modification part is that, if the Software (or any derivative works
+thereof) is used for any of your commercial products or services that have
+more than 100 million monthly active users, or more than 20 million US dollars
+(or equivalent in other currencies) in monthly revenue, you shall prominently
+display "Kimi K2" on the user interface of such product or service.

README.md

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+---
+pipeline_tag: text-generation
+base_model:
+- moonshotai/Kimi-K2-Thinking
+license: other
+license_name: modified-mit
+library_name: Model Optimizer
+tags:
+- nvidia
+- ModelOpt
+- Kimi-K2
+- quantized
+- FP4
+- fp4
+---
+# Model Overview
+
+## Description:
+The NVIDIA Kimi-K2-Thinking-NVFP4 model is the quantized version of the Moonshot AI's Kimi-K2-Thinking model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/moonshotai/Kimi-K2-Thinking). The NVIDIA Kimi-K2-Thinking NVFP4 model is quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+
+This model is ready for commercial/non-commercial use.  <br>
+
+## Third-Party Community Consideration
+This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [(Kimi-K2-Thinking) Model Card](https://huggingface.co/moonshotai/Kimi-K2-Thinking).
+
+### License/Terms of Use:
+[Modified MIT](https://huggingface.co/nvidia/Kimi-K2-Thinking-NVFP4/blob/main/LICENSE)
+
+### Deployment Geography
+Global
+
+### Use Case
+This model is intended for developers and researchers building LLMs
+
+### Release Date
+Huggingface 12/04/2025 via https://huggingface.co/nvidia/Kimi-K2-Thinking-NVFP4
+
+
+## Model Architecture:
+**Architecture Type:** Transformers  <br>
+**Network Architecture:** DeepSeek V3 <br>
+**Number of Model Parameters:** 1T
+
+## Input:
+**Input Type(s):** Text <br>
+**Input Format(s):** String <br>
+**Input Parameters:** 1D (One Dimensional): Sequences <br>
+
+## Output:
+**Output Type(s):** Text <br>
+**Output Format:** String <br>
+**Output Parameters:** 1D (One Dimensional): Sequences <br>
+
+Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
+
+## Software Integration:
+**Supported Runtime Engine(s):** <br>
+* vLLM <br>
+
+**Supported Hardware Microarchitecture Compatibility:** <br>
+* NVIDIA Blackwell <br>
+
+**Preferred Operating System(s):** <br>
+* Linux <br>
+The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
+
+## Model Version(s):
+** The model is quantized with nvidia-modelopt **v0.39.0**  <br>
+
+## Calibration Datasets:
+* Calibration Dataset: [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) <br>
+** Data collection method: Automated. <br>
+** Labeling method: Automated. <br>
+
+## Training Dataset: <br>
+** Data Collection Method by dataset: Hybrid: Human, Automated <br>
+** Labeling Method by dataset: Hybrid: Human, Automated <br>
+** Data Modality: [Text] <br>
+** Text Training Data Size: undisclosed. <br>
+
+## Testing Dataset: <br>
+** Data Collection Method by dataset: Hybrid: Human, Automated <br>
+** Labeling Method by dataset: Hybrid: Human, Automated <br>
+
+## Evaluation Dataset: <br>
+** Data Collection Method by dataset: Hybrid: Human, Automated <br>
+** Labeling Method by dataset: Hybrid: Human, Automated <br>
+
+
+## Inference:
+**Engine:** vLLM <br>
+**Test Hardware:** B200 <br>
+
+## Post Training Quantization
+This model was obtained by converting and quantizing the weights and activations of Kimi-K2-Thinking from INT4 to BF16 to NVFP4 data type, ready for inference with vLLM. Only the weights and activations of the linear operators within transformer blocks in MoE are quantized.
+
+## Usage
+
+
+To serve this checkpoint with [vLLM](https://github.com/vllm-project/vllm), you can start the docker `vllm/vllm-openai:v0.11.2` and run the sample command below:
+
+```sh
+python3 -m vllm.entrypoints.openai.api_server --model nvidia/Kimi-K2-Thinking-NVFP4 --trust-remote-code --tensor-parallel-size 4
+```
+
+
+## Model Limitations:
+The base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
+
+## Ethical Considerations
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+

chat_template.jinja

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+{%- macro render_content(msg) -%}
+    {%- set c = msg.get('content') -%}
+    {%- if c is string -%}
+      {{ c }}
+    {%- elif c is not none -%}
+      {% for content in c -%}
+        {% if content['type'] == 'image' or 'image' in content or 'image_url' in content -%}
+          <|media_start|>image<|media_content|><|media_pad|><|media_end|>
+        {% else -%}
+          {{ content['text'] }}
+        {%- endif -%}
+      {%- endfor -%}
+    {%- endif -%}
+{%- endmacro -%}
+
+{% macro set_roles(message) -%}
+  {%- set role_name =  message.get('name') or  message['role'] -%}
+  {%- if message['role'] == 'user' -%}
+    <|im_user|>{{role_name}}<|im_middle|>
+  {%- elif message['role'] == 'assistant' -%}
+    <|im_assistant|>{{role_name}}<|im_middle|>
+  {%- else -%}
+    <|im_system|>{{role_name}}<|im_middle|>
+  {%- endif -%}
+{%- endmacro -%}
+
+
+{%- macro render_toolcalls(message) -%}
+  <|tool_calls_section_begin|>
+  {%- for tool_call in message['tool_calls'] -%}
+    {%- set formatted_id = tool_call['id'] -%}
+    <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|>
+  {%- endfor -%}
+  <|tool_calls_section_end|>
+{%- endmacro -%}
+
+
+{# Find last non-tool-call assisitant message #}
+{%- set ns = namespace(last_non_tool_call_assistant_msg=-1) -%}
+{%- for idx in range(messages|length-1, -1, -1) -%}
+    {%- if messages[idx]['role'] == 'assistant' and not messages[idx].get('tool_calls') -%}
+        {%- set ns.last_non_tool_call_assistant_msg = idx -%}
+        {%- break -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{# split all messages into history & suffix, reasoning_content in suffix should be reserved.#}
+{%- set hist_msgs = messages[:ns.last_non_tool_call_assistant_msg+1] -%}
+{%- set suffix_msgs = messages[ns.last_non_tool_call_assistant_msg+1:] -%}
+
+{%- if tools -%}
+  <|im_system|>tool_declare<|im_middle|>{{ tools | tojson(separators=(',', ':')) }}<|im_end|>
+{%- endif -%}
+
+{%- for message in hist_msgs -%}
+  {%- if loop.first and messages[0]['role'] != 'system' -%}
+  <|im_system|>system<|im_middle|>You are Kimi, an AI assistant created by Moonshot AI.<|im_end|>
+  {%- endif -%}
+  {{set_roles(message)}}
+  {%- if message['role'] == 'assistant' -%}
+    <think></think>{{render_content(message)}}
+    {%- if message.get('tool_calls') -%}
+      {{render_toolcalls(message)}}
+    {%- endif -%}
+  {%- elif message['role'] == 'tool' -%}
+    {%- set tool_call_id = message.tool_call_id -%}
+    ## Return of {{ tool_call_id }}
+{{render_content(message)}}
+  {%- elif message['content'] is not none -%}
+    {{render_content(message)}}
+  {%- endif -%}
+  <|im_end|>
+{%- endfor -%}
+
+{%- for message in suffix_msgs -%}
+  {{set_roles(message)}}
+  {%- if message['role'] == 'assistant' -%}
+    {%- set rc = message.get('reasoning_content', '') -%}
+    <think>{{rc}}</think>{{render_content(message)}}
+    {%- if message.get('tool_calls') -%}
+     {{render_toolcalls(message)}}
+    {%- endif -%}
+  {%- elif message['role'] == 'tool' -%}
+    {%- set tool_call_id = message.tool_call_id -%}
+    ## Return of {{ tool_call_id }}
+{{render_content(message)}}
+  {%- elif message['content'] is not none -%}
+    {{render_content(message)}}
+  {%- endif -%}
+  <|im_end|>
+{%- endfor -%}
+
+
+{%- if add_generation_prompt -%}
+  <|im_assistant|>assistant<|im_middle|>
+{%- endif -%}

config.json

@@ -0,0 +1,219 @@
+{
+    "_attn_implementation_autoset": false,
+    "architectures": [
+        "DeepseekV3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "auto_map": {
+        "AutoConfig": "configuration_deepseek.DeepseekV3Config",
+        "AutoModel": "modeling_deepseek.DeepseekV3Model",
+        "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
+    },
+    "aux_loss_alpha": 0.001,
+    "bos_token_id": 163584,
+    "dtype": "bfloat16",
+    "eos_token_id": 163586,
+    "ep_size": 1,
+    "first_k_dense_replace": 1,
+    "hidden_act": "silu",
+    "hidden_size": 7168,
+    "initializer_range": 0.02,
+    "intermediate_size": 18432,
+    "kv_lora_rank": 512,
+    "max_position_embeddings": 262144,
+    "model_type": "deepseek_v3",
+    "moe_intermediate_size": 2048,
+    "moe_layer_freq": 1,
+    "n_group": 1,
+    "n_routed_experts": 384,
+    "n_shared_experts": 1,
+    "norm_topk_prob": true,
+    "num_attention_heads": 64,
+    "num_experts_per_tok": 8,
+    "num_hidden_layers": 61,
+    "num_key_value_heads": 64,
+    "num_nextn_predict_layers": 0,
+    "pad_token_id": 163839,
+    "pretraining_tp": 1,
+    "q_lora_rank": 1536,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+        "beta_fast": 1.0,
+        "beta_slow": 1.0,
+        "factor": 64.0,
+        "mscale": 1.0,
+        "mscale_all_dim": 1.0,
+        "original_max_position_embeddings": 4096,
+        "type": "yarn"
+    },
+    "rope_theta": 50000.0,
+    "routed_scaling_factor": 2.827,
+    "scoring_func": "sigmoid",
+    "seq_aux": true,
+    "tie_word_embeddings": false,
+    "topk_group": 1,
+    "topk_method": "noaux_tc",
+    "transformers_version": "4.57.1",
+    "use_cache": true,
+    "v_head_dim": 128,
+    "vocab_size": 163840,
+    "quantization_config": {
+        "config_groups": {
+            "group_0": {
+                "input_activations": {
+                    "dynamic": false,
+                    "num_bits": 4,
+                    "type": "float",
+                    "group_size": 16
+                },
+                "weights": {
+                    "dynamic": false,
+                    "num_bits": 4,
+                    "type": "float",
+                    "group_size": 16
+                },
+                "targets": [
+                    "Linear"
+                ]
+            }
+        },
+        "ignore": [
+            "lm_head",
+            "model.layers.0*",
+            "model.layers.1.mlp.shared_experts*",
+            "model.layers.1.self_attn*",
+            "model.layers.10.mlp.shared_experts*",
+            "model.layers.10.self_attn*",
+            "model.layers.11.mlp.shared_experts*",
+            "model.layers.11.self_attn*",
+            "model.layers.12.mlp.shared_experts*",
+            "model.layers.12.self_attn*",
+            "model.layers.13.mlp.shared_experts*",
+            "model.layers.13.self_attn*",
+            "model.layers.14.mlp.shared_experts*",
+            "model.layers.14.self_attn*",
+            "model.layers.15.mlp.shared_experts*",
+            "model.layers.15.self_attn*",
+            "model.layers.16.mlp.shared_experts*",
+            "model.layers.16.self_attn*",
+            "model.layers.17.mlp.shared_experts*",
+            "model.layers.17.self_attn*",
+            "model.layers.18.mlp.shared_experts*",
+            "model.layers.18.self_attn*",
+            "model.layers.19.mlp.shared_experts*",
+            "model.layers.19.self_attn*",
+            "model.layers.2.mlp.shared_experts*",
+            "model.layers.2.self_attn*",
+            "model.layers.20.mlp.shared_experts*",
+            "model.layers.20.self_attn*",
+            "model.layers.21.mlp.shared_experts*",
+            "model.layers.21.self_attn*",
+            "model.layers.22.mlp.shared_experts*",
+            "model.layers.22.self_attn*",
+            "model.layers.23.mlp.shared_experts*",
+            "model.layers.23.self_attn*",
+            "model.layers.24.mlp.shared_experts*",
+            "model.layers.24.self_attn*",
+            "model.layers.25.mlp.shared_experts*",
+            "model.layers.25.self_attn*",
+            "model.layers.26.mlp.shared_experts*",
+            "model.layers.26.self_attn*",
+            "model.layers.27.mlp.shared_experts*",
+            "model.layers.27.self_attn*",
+            "model.layers.28.mlp.shared_experts*",
+            "model.layers.28.self_attn*",
+            "model.layers.29.mlp.shared_experts*",
+            "model.layers.29.self_attn*",
+            "model.layers.3.mlp.shared_experts*",
+            "model.layers.3.self_attn*",
+            "model.layers.30.mlp.shared_experts*",
+            "model.layers.30.self_attn*",
+            "model.layers.31.mlp.shared_experts*",
+            "model.layers.31.self_attn*",
+            "model.layers.32.mlp.shared_experts*",
+            "model.layers.32.self_attn*",
+            "model.layers.33.mlp.shared_experts*",
+            "model.layers.33.self_attn*",
+            "model.layers.34.mlp.shared_experts*",
+            "model.layers.34.self_attn*",
+            "model.layers.35.mlp.shared_experts*",
+            "model.layers.35.self_attn*",
+            "model.layers.36.mlp.shared_experts*",
+            "model.layers.36.self_attn*",
+            "model.layers.37.mlp.shared_experts*",
+            "model.layers.37.self_attn*",
+            "model.layers.38.mlp.shared_experts*",
+            "model.layers.38.self_attn*",
+            "model.layers.39.mlp.shared_experts*",
+            "model.layers.39.self_attn*",
+            "model.layers.4.mlp.shared_experts*",
+            "model.layers.4.self_attn*",
+            "model.layers.40.mlp.shared_experts*",
+            "model.layers.40.self_attn*",
+            "model.layers.41.mlp.shared_experts*",
+            "model.layers.41.self_attn*",
+            "model.layers.42.mlp.shared_experts*",
+            "model.layers.42.self_attn*",
+            "model.layers.43.mlp.shared_experts*",
+            "model.layers.43.self_attn*",
+            "model.layers.44.mlp.shared_experts*",
+            "model.layers.44.self_attn*",
+            "model.layers.45.mlp.shared_experts*",
+            "model.layers.45.self_attn*",
+            "model.layers.46.mlp.shared_experts*",
+            "model.layers.46.self_attn*",
+            "model.layers.47.mlp.shared_experts*",
+            "model.layers.47.self_attn*",
+            "model.layers.48.mlp.shared_experts*",
+            "model.layers.48.self_attn*",
+            "model.layers.49.mlp.shared_experts*",
+            "model.layers.49.self_attn*",
+            "model.layers.5.mlp.shared_experts*",
+            "model.layers.5.self_attn*",
+            "model.layers.50.mlp.shared_experts*",
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+            "model.layers.52.mlp.shared_experts*",
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+            "model.layers.53.mlp.shared_experts*",
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+            "model.layers.54.mlp.shared_experts*",
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+            "model.layers.55.mlp.shared_experts*",
+            "model.layers.55.self_attn*",
+            "model.layers.56.mlp.shared_experts*",
+            "model.layers.56.self_attn*",
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+            "model.layers.58.self_attn*",
+            "model.layers.59.mlp.shared_experts*",
+            "model.layers.59.self_attn*",
+            "model.layers.6.mlp.shared_experts*",
+            "model.layers.6.self_attn*",
+            "model.layers.60.mlp.shared_experts*",
+            "model.layers.60.self_attn*",
+            "model.layers.7.mlp.shared_experts*",
+            "model.layers.7.self_attn*",
+            "model.layers.8.mlp.shared_experts*",
+            "model.layers.8.self_attn*",
+            "model.layers.9.mlp.shared_experts*",
+            "model.layers.9.self_attn*"
+        ],
+        "quant_algo": "NVFP4",
+        "kv_cache_scheme": {
+            "dynamic": false,
+            "num_bits": 8,
+            "type": "float"
+        },
+        "producer": {
+            "name": "modelopt",
+            "version": "0.40.0.dev66+gbe64f6b1d.d20251119"
+        },
+        "quant_method": "modelopt"
+    }
+}

configuration_deepseek.py

@@ -0,0 +1,212 @@
+# Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+class DeepseekV3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the DeepSeek-V3.
+
+    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 129280):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`DeepseekV3Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1407):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_nextn_predict_layers (`int`, *optional*, defaults to 1):
+            Number of nextn predict layers in the DeepSeekV3 Model.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        n_shared_experts (`int`, *optional*, defaults to None):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to None):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `gready`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to None):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to None):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        num_experts_per_tok (`int`, *optional*, defaults to None):
+            Number of selected experts, None means dense model.
+        moe_layer_freq (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+        first_k_dense_replace (`int`, *optional*, defaults to 0):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to False):
+            Whether to normalize the weights of the routed experts.
+        scoring_func (`str`, *optional*, defaults to 'softmax'):
+            Method of computing expert weights.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        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 2048):
+            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`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import DeepseekV3Model, DeepseekV3Config
+
+    >>> # Initializing a Deepseek-V3 style configuration
+    >>> configuration = DeepseekV3Config()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_v3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=129280,
+        hidden_size=7168,
+        intermediate_size=18432,
+        moe_intermediate_size = 2048,
+        num_hidden_layers=61,
+        num_nextn_predict_layers=1,
+        num_attention_heads=128,
+        num_key_value_heads=128,
+        n_shared_experts = 1,
+        n_routed_experts = 256,
+        ep_size = 1,
+        routed_scaling_factor = 2.5,
+        kv_lora_rank = 512,
+        q_lora_rank = 1536,
+        qk_rope_head_dim = 64,
+        v_head_dim = 128,
+        qk_nope_head_dim = 128,
+        topk_method = 'noaux_tc',
+        n_group = 8,
+        topk_group = 4,
+        num_experts_per_tok = 8,
+        moe_layer_freq = 1,
+        first_k_dense_replace = 3,
+        norm_topk_prob = True,
+        scoring_func = 'sigmoid',
+        aux_loss_alpha = 0.001,
+        seq_aux = True,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.ep_size = ep_size
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.topk_method = topk_method
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_layer_freq = moe_layer_freq
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.scoring_func = scoring_func
+        self.aux_loss_alpha = aux_loss_alpha
+        self.seq_aux = seq_aux
+        # 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.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

@@ -0,0 +1,5 @@
+{
+  "eos_token_id": 163586,
+  "max_length": 262144,
+  "transformers_version": "4.57.1"
+}

hf_quant_config.json

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+    "producer": {
+        "name": "modelopt",
+        "version": "0.40.0.dev66+gbe64f6b1d.d20251119"
+    },
+    "quantization": {
+        "quant_algo": "NVFP4",
+        "kv_cache_quant_algo": "FP8",
+        "group_size": 16,
+        "exclude_modules": [
+            "lm_head",
+            "model.layers.0*",
+            "model.layers.1.mlp.shared_experts*",
+            "model.layers.1.self_attn*",
+            "model.layers.10.mlp.shared_experts*",
+            "model.layers.10.self_attn*",
+            "model.layers.11.mlp.shared_experts*",
+            "model.layers.11.self_attn*",
+            "model.layers.12.mlp.shared_experts*",
+            "model.layers.12.self_attn*",
+            "model.layers.13.mlp.shared_experts*",
+            "model.layers.13.self_attn*",
+            "model.layers.14.mlp.shared_experts*",
+            "model.layers.14.self_attn*",
+            "model.layers.15.mlp.shared_experts*",
+            "model.layers.15.self_attn*",
+            "model.layers.16.mlp.shared_experts*",
+            "model.layers.16.self_attn*",
+            "model.layers.17.mlp.shared_experts*",
+            "model.layers.17.self_attn*",
+            "model.layers.18.mlp.shared_experts*",
+            "model.layers.18.self_attn*",
+            "model.layers.19.mlp.shared_experts*",
+            "model.layers.19.self_attn*",
+            "model.layers.2.mlp.shared_experts*",
+            "model.layers.2.self_attn*",
+            "model.layers.20.mlp.shared_experts*",
+            "model.layers.20.self_attn*",
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+            "model.layers.21.self_attn*",
+            "model.layers.22.mlp.shared_experts*",
+            "model.layers.22.self_attn*",
+            "model.layers.23.mlp.shared_experts*",
+            "model.layers.23.self_attn*",
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+            "model.layers.24.self_attn*",
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+            "model.layers.25.self_attn*",
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+            "model.layers.29.mlp.shared_experts*",
+            "model.layers.29.self_attn*",
+            "model.layers.3.mlp.shared_experts*",
+            "model.layers.3.self_attn*",
+            "model.layers.30.mlp.shared_experts*",
+            "model.layers.30.self_attn*",
+            "model.layers.31.mlp.shared_experts*",
+            "model.layers.31.self_attn*",
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+            "model.layers.32.self_attn*",
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+            "model.layers.34.self_attn*",
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+            "model.layers.35.self_attn*",
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+            "model.layers.38.self_attn*",
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+            "model.layers.39.self_attn*",
+            "model.layers.4.mlp.shared_experts*",
+            "model.layers.4.self_attn*",
+            "model.layers.40.mlp.shared_experts*",
+            "model.layers.40.self_attn*",
+            "model.layers.41.mlp.shared_experts*",
+            "model.layers.41.self_attn*",
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+            "model.layers.5.self_attn*",
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+            "model.layers.52.self_attn*",
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+        ]
+    }
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