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LICENSE-CODE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 DeepSeek
+
+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.

LICENSE-MODEL

@@ -0,0 +1,91 @@
+DEEPSEEK LICENSE AGREEMENT
+
+Version 1.0, 23 October 2023
+
+Copyright (c) 2023 DeepSeek
+
+Section I: PREAMBLE 
+
+Large generative models are being widely adopted and used, and have the potential to transform the way individuals conceive and benefit from AI or ML technologies. 
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations. 
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for content generation. 
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this agreement aims to strike a balance between both in order to enable responsible open-science in the field of AI. 
+
+This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model. 
+
+NOW THEREFORE, You and DeepSeek agree as follows: 
+
+1. Definitions 
+"License" means the terms and conditions for use, reproduction, and Distribution as defined in this document. 
+"Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
+"Output" means the results of operating a Model as embodied in informational content resulting therefrom. 
+"Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material. 
+"Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model. 
+"Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any. 
+"Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access. 
+"DeepSeek" (or "we") means Beijing DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd., Hangzhou DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd. and/or any of their affiliates.
+"You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, etc. 
+"Third Parties" means individuals or legal entities that are not under common control with DeepSeek or You. 
+
+Section II: INTELLECTUAL PROPERTY RIGHTS 
+
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III. 
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model. 
+
+3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by DeepSeek that are necessarily infringed by its contribution(s). If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or works shall terminate as of the date such litigation is asserted or filed. 
+
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions: 
+a. Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material. 
+b. You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License; 
+c. You must cause any modified files to carry prominent notices stating that You changed the files; 
+d. You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model. 
+e. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. – for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License. 
+
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5). 
+
+6. The Output You Generate. Except as set forth herein, DeepSeek claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS 
+
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, DeepSeek reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License. 
+
+8. Trademarks and related. Nothing in this License permits You to make use of DeepSeek’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by DeepSeek. 
+
+9. Personal information, IP rights and related. This Model may contain personal information and works with IP rights. You commit to complying with applicable laws and regulations in the handling of personal information and the use of such works. Please note that DeepSeek's license granted to you to use the Model does not imply that you have obtained a legitimate basis for processing the related information or works. As an independent personal information processor and IP rights user, you need to ensure full compliance with relevant legal and regulatory requirements when handling personal information and works with IP rights that may be contained in the Model, and are willing to assume solely any risks and consequences that may arise from that.
+
+10. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, DeepSeek provides the Model and the Complementary Material on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License. 
+
+11. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall DeepSeek be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if DeepSeek has been advised of the possibility of such damages. 
+
+12. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material 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 DeepSeek, and only if You agree to indemnify, defend, and hold DeepSeek harmless for any liability incurred by, or claims asserted against, DeepSeek by reason of your accepting any such warranty or additional liability. 
+
+13. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+
+14. Governing Law and Jurisdiction. This agreement will be governed and construed under PRC laws without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this agreement. The courts located in the domicile of Hangzhou DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd. shall have exclusive jurisdiction of any dispute arising out of this agreement.
+
+END OF TERMS AND CONDITIONS
+
+Attachment A 
+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+
+-	In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party; 
+-	For military use in any way;
+-	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; 
+-	To generate or disseminate verifiably false information and/or content with the purpose of harming others; 
+-	To generate or disseminate inappropriate content subject to applicable regulatory requirements;
+-	To generate or disseminate personal identifiable information without due authorization or for unreasonable use; 
+-	To defame, disparage or otherwise harass others; 
+-	For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; 
+-	For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; 
+-	To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; 
+-	For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.

Modelfile

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+FROM huihui-ai/DeepSeek-V3-Pruned-Coder-411B/ggml-model-Q4_K_M.gguf
+TEMPLATE """{{- range $i, $_ := .Messages }}
+{{- if eq .Role "user" }}<｜User｜>
+{{- else if eq .Role "assistant" }}<｜Assistant｜>
+{{- end }}{{ .Content }}
+{{- if eq (len (slice $.Messages $i)) 1 }}
+{{- if eq .Role "user" }}<｜Assistant｜>
+{{- end }}
+{{- else if eq .Role "assistant" }}<｜end▁of▁sentence｜><｜begin▁of▁sentence｜>
+{{- end }}
+{{- end }}"""
+PARAMETER stop <｜begin▁of▁sentence｜>
+PARAMETER stop <｜end▁of▁sentence｜>
+PARAMETER stop <｜User｜>
+PARAMETER stop <｜Assistant｜>
+PARAMETER num_gpu 1
+LICENSE """DEEPSEEK LICENSE AGREEMENT
+
+Version 1.0, 23 October 2023
+
+Copyright (c) 2023 DeepSeek
+
+Section I: PREAMBLE 
+
+Large generative models are being widely adopted and used, and have the potential to transform the way individuals conceive and benefit from AI or ML technologies. 
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations. 
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for content generation. 
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this agreement aims to strike a balance between both in order to enable responsible open-science in the field of AI. 
+
+This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model. 
+
+NOW THEREFORE, You and DeepSeek agree as follows: 
+
+1. Definitions 
+"License" means the terms and conditions for use, reproduction, and Distribution as defined in this document. 
+"Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
+"Output" means the results of operating a Model as embodied in informational content resulting therefrom. 
+"Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material. 
+"Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model. 
+"Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any. 
+"Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access. 
+"DeepSeek" (or "we") means Beijing DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd., Hangzhou DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd. and/or any of their affiliates.
+"You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, etc. 
+"Third Parties" means individuals or legal entities that are not under common control with DeepSeek or You. 
+
+Section II: INTELLECTUAL PROPERTY RIGHTS 
+
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III. 
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model. 
+
+3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by DeepSeek that are necessarily infringed by its contribution(s). If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or works shall terminate as of the date such litigation is asserted or filed. 
+
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions: 
+a. Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material. 
+b. You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License; 
+c. You must cause any modified files to carry prominent notices stating that You changed the files; 
+d. You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model. 
+e. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. – for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License. 
+
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5). 
+
+6. The Output You Generate. Except as set forth herein, DeepSeek claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS 
+
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, DeepSeek reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License. 
+
+8. Trademarks and related. Nothing in this License permits You to make use of DeepSeek’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by DeepSeek. 
+
+9. Personal information, IP rights and related. This Model may contain personal information and works with IP rights. You commit to complying with applicable laws and regulations in the handling of personal information and the use of such works. Please note that DeepSeek's license granted to you to use the Model does not imply that you have obtained a legitimate basis for processing the related information or works. As an independent personal information processor and IP rights user, you need to ensure full compliance with relevant legal and regulatory requirements when handling personal information and works with IP rights that may be contained in the Model, and are willing to assume solely any risks and consequences that may arise from that.
+10. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, DeepSeek provides the Model and the Complementary Material on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License. 
+11. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall DeepSeek be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if DeepSeek has been advised of the possibility of such damages. 
+12. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material 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 DeepSeek, and only if You agree to indemnify, defend, and hold DeepSeek harmless for any liability incurred by, or claims asserted against, DeepSeek by reason of your accepting any such warranty or additional liability. 
+13. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+14. Governing Law and Jurisdiction. This agreement will be governed and construed under PRC laws without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this agreement. The courts located in the domicile of Hangzhou DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd. shall have exclusive jurisdiction of any dispute arising out of this agreement.
+END OF TERMS AND CONDITIONS
+Attachment A 
+Use Restrictions
+You agree not to use the Model or Derivatives of the Model:
+-	In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party; 
+-	For military use in any way;
+-	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; 
+-	To generate or disseminate verifiably false information and/or content with the purpose of harming others; 
+-	To generate or disseminate inappropriate content subject to applicable regulatory requirements;
+-	To generate or disseminate personal identifiable information without due authorization or for unreasonable use; 
+-	To defame, disparage or otherwise harass others; 
+-	For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; 
+-	For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; 
+-	To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; 
+-	For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
+"""
+

README.md

@@ -1,3 +1,111 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model:
+- deepseek-ai/DeepSeek-V3
+tags:
+- deepseek_v3
+- bf16
+- Safetensors
+- custom_code
+- Pruned
+---
+
+# huihui-ai/DeepSeek-V3-Pruned-Coder-411B
+
+
+
+
+This is a pruned version of the [deepseek-ai/DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3), 
+reduced from 256 experts to 160 experts. The pruned model is mainly used for [code](https://huggingface.co/huihui-ai/DeepSeek-V3-Pruned-Coder-411B/blob/main/coding_problems.py) generation.
+
+
+This is a test validation to see if we can prune the model according to professional requirements and still maintain acceptable performance. The model size has been reduced by about half, and no distortion has occurred.
+
+This allows the model to be pruned according to one's needs.
+
+This pruned model has a total parameter is equivalent to 441B.
+
+
+This is a test validation to see if we can prune the model according to professional requirements and still maintain acceptable performance. The model size has been reduced by about half, and no distortion has occurred.
+
+
+## Use with transformers
+
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+# Load the model and tokenizer
+NEW_MODEL_ID = "huihui-ai/DeepSeek-V3-Pruned-Coder-411B"
+quant_config_4 = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    llm_int8_enable_fp32_cpu_offload=True,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    NEW_MODEL_ID, 
+    device_map="auto", 
+    trust_remote_code=True,
+    quantization_config=quant_config_4,
+    torch_dtype=torch.bfloat16
+)
+tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+tokenizer.pad_token_id = tokenizer.eos_token_id
+
+# Initialize conversation context
+initial_messages = [
+    {"role": "system", "content": "You are a helpful assistant provided by Moonshot-AI."}
+]
+messages = initial_messages.copy()  # Copy the initial conversation context
+
+# Enter conversation loop
+while True:
+    # Get user input
+    user_input = input("User: ").strip()  # Strip leading and trailing spaces
+
+    # If the user types '/exit', end the conversation
+    if user_input.lower() == "/exit":
+        print("Exiting chat.")
+        break
+
+    # If the user types '/clean', reset the conversation context
+    if user_input.lower() == "/clear":
+        messages = initial_messages.copy()  # Reset conversation context
+        print("Chat history cleared. Starting a new conversation.")
+        continue
+
+    # If input is empty, prompt the user and continue
+    if not user_input:
+        print("Input cannot be empty. Please enter something.")
+        continue
+
+    # Add user input to the conversation
+    messages.append({"role": "user", "content": user_input})
+
+    tokenized_message = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
+    response_token_ids = model.generate(tokenized_message['input_ids'].to("cuda:0"), use_cache=False, pad_token_id=tokenizer.pad_token_id, max_new_tokens=8192)
+    generated_tokens =response_token_ids[:, len(tokenized_message['input_ids'][0]):]
+    response = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+
+    # Add the model's response to the conversation
+    messages.append({"role": "assistant", "content": response})
+
+    # Print the model's response
+    print(f"Response: {response}")
+```
+
+### Donation
+
+If you like it, please click 'like' and follow us for more updates.  
+You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
+
+##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
+- bitcoin:
+```
+  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
+```

coding_problems.py

@@ -0,0 +1,201 @@
+coding_problems = {
+    "Python": "In Python, write a function to reverse a string, for example, turning input 'hello' into 'olleh'.",
+    "Java": "In Java, implement a simple calculator class that supports addition, subtraction, multiplication, and division.",
+    "C++": "In C++, implement an efficient sorting algorithm (e.g., quicksort or mergesort) to sort an array of integers.",
+    "JavaScript": "In JavaScript, create a simple web page interaction with a button that, when clicked, displays the current time.",
+    "C#": "In C#, implement a simple Windows Forms application featuring a text box and a button that displays the text box content when clicked.",
+    "Ruby": "In Ruby, implement a simple web server using Ruby on Rails that returns 'Hello, World!'",
+    "PHP": "In PHP, create a dynamic web page that displays the current date and time.",
+    "Swift": "In Swift, implement a simple iOS app interface with a label and a button that updates the label to 'Clicked!' when pressed.",
+    "Go": "In Go, implement a concurrent program using goroutines to calculate the square and cube of two numbers simultaneously.",
+    "R": "In R, read data from a CSV file, calculate the average, and plot a bar chart.",
+    "SQL": "In SQL, write a query to retrieve the total number of orders for each user from 'users' and 'orders' tables.",
+    "Kotlin": "In Kotlin, implement a simple Android app feature with an input field and a button that displays the input content when clicked.",
+    "Scala": "In Scala, implement a functional programming example with a function that takes a list of integers and returns their sum.",
+    "Perl": "In Perl, implement a text processing script to extract all email addresses from a text file.",
+    "Haskell": "In Haskell, implement a pure functional program to calculate the nth Fibonacci number.",
+    "Lua": "In Lua, implement a game script to control a character's simple movements (e.g., up, down, left, right).",
+    "MATLAB": "In MATLAB, implement matrix multiplication and visualize the result.",
+    "Objective-C": "In Objective-C, implement a simple iOS app feature with a button that shows an alert with 'Welcome!' when clicked.",
+    "Rust": "In Rust, implement a safe concurrent program to calculate the sum of all elements in an array using multiple threads.",
+    "TypeScript": "In TypeScript, implement a type-safe function that takes an array of numbers and returns the maximum value.",
+    "Bash": "In Bash, write a shell script to backup a specified directory to a compressed file.",
+    "PowerShell": "In PowerShell, implement a system management script to list all running processes.",
+    "Dart": "In Dart, implement a simple Flutter app interface with a counter button.",
+    "Groovy": "In Groovy, implement a simple script to read and print the content of a JSON file.",
+    "F#": "In F#, implement a functional programming example with a function to calculate the product of all elements in an integer list.",
+    "Erlang": "In Erlang, implement a simple distributed chat server that supports multiple client connections.",
+    "COBOL": "In COBOL, implement a business calculation program to compute an employee's total salary (base salary + bonus).",
+    "Fortran": "In Fortran, implement a scientific computing program to calculate a definite integral (e.g., using the rectangle method).",
+    "Ada": "In Ada, implement an embedded system program to simulate controlling an LED light (on/off).",
+    "Prolog": "In Prolog, implement a logic reasoning program to solve a simple puzzle, such as 'Who is the murderer?'",
+    "Lisp": "In Lisp, implement a symbolic computation program to evaluate a simple mathematical expression, e.g., '(add 2 3)'.",
+    "Scheme": "In Scheme, implement a recursive function to calculate the factorial of n.",
+    "Smalltalk": "In Smalltalk, implement an object-oriented program to simulate bank account deposits and withdrawals.",
+    "VHDL": "In VHDL, design a simple digital circuit, such as an adder.",
+    "Verilog": "In Verilog, implement a hardware module for a simple sequential logic circuit, like a counter.",
+    "Assembly": "In Assembly, write a low-level program to compute the sum of two numbers.",
+    "Clojure": "In Clojure, implement a concurrent program using concurrency primitives to compute the sum of squares of an array.",
+    "Elixir": "In Elixir, implement a simple distributed counter.",
+    "Julia": "In Julia, implement a high-performance computing program for matrix multiplication and measure execution time.",
+    "Crystal": "In Crystal, implement a web application that returns a JSON response.",
+    "Nim": "In Nim, implement a command-line tool that accepts arguments and prints the content of a file.",
+    "D": "In D, implement a system-level program to list all files in a directory.",
+    "Pascal": "In Pascal, implement a teaching program demonstrating if-else and loop structures.",
+    "Forth": "In Forth, implement a stack-based addition calculator.",
+    "APL": "In APL, implement an array processing program to sort an array and calculate its average.",
+    "J": "In J, implement a data analysis program to compute the standard deviation of a dataset.",
+    "K": "In K, implement a high-performance data processing program to compute the sum of a large array.",
+    "Q": "In Q, implement a database query to retrieve data from a time-series database for a specific period.",
+    "Tcl": "In Tcl, implement a script to automate file renaming.",
+    "Awk": "In Awk, implement a text processing script to extract the second field from each line of a log file.",
+    "Sed": "In Sed, implement a text replacement script to replace 'cat' with 'dog' in a text file.",
+    "Grep": "In Grep, implement a pattern matching script to find lines containing 'error' in a file.",
+    "Makefile": "In Makefile, write a build script to compile a C program and generate an executable file.",
+    "CMake": "In CMake, implement a cross-platform build system configuration.",
+    "Dockerfile": "In Dockerfile, write a container configuration file to build a Docker image with a Python environment.",
+    "YAML": "In YAML, implement a configuration file for a web application, specifying port and database connection.",
+    "JSON": "In JSON, implement a data exchange format to represent a user's personal information.",
+    "XML": "In XML, implement a markup language document to describe a book's title and author.",
+    "HTML": "In HTML, implement a simple web page with a title and a paragraph.",
+    "CSS": "In CSS, implement a style sheet to add background color and font styles to an HTML page.",
+    "Sass": "In Sass, implement a CSS preprocessor script to generate nested style sheets.",
+    "Less": "In Less, implement a CSS preprocessor script using variables to define colors and generate CSS.",
+    "PostScript": "In PostScript, implement a print script to generate a circle graphic.",
+    "TeX": "In TeX, implement a typesetting document that includes mathematical formulas.",
+    "ABAP": "In ABAP, implement a report program to retrieve and display customer data from an SAP database.",
+    "ActionScript": "In ActionScript, create an interactive animation where a ball moves across the screen when a button is clicked.",
+    "ALGOL": "In ALGOL, implement a procedure to calculate the greatest common divisor (GCD) of two numbers.",
+    "Ballerina": "In Ballerina, implement a simple REST API service that returns a greeting message.",
+    "Basic": "In Basic, write a program to calculate and display the area of a rectangle based on user input.",
+    "BCPL": "In BCPL, implement a simple memory allocation function for dynamic arrays.",
+    "Chapel": "In Chapel, implement a parallel program to compute the dot product of two large vectors.",
+    "ColdFusion": "In ColdFusion, create a dynamic webpage that displays a list of products from a database.",
+    "Datalog": "In Datalog, write a query to determine family relationships (e.g., 'Who is the grandparent?') from a set of facts.",
+    "Elm": "In Elm, implement a simple counter web app with increment and decrement buttons.",
+    "Hack": "In Hack, implement a type-safe function to validate and process a user registration form.",
+    "Icon": "In Icon, write a program to generate all permutations of a given string.",
+    "Io": "In Io, implement a prototype-based object system to simulate a simple car with start and stop methods.",
+    "J#": "In J#, implement a console application to sort a list of names alphabetically.",
+    "LabVIEW": "In LabVIEW, create a virtual instrument to measure and display the voltage from a simulated sensor.",
+    "Ladder Logic": "In Ladder Logic, design a PLC program to control a traffic light sequence.",
+    "LiveScript": "In LiveScript, implement a functional pipeline to process and filter an array of numbers.",
+    "Logo": "In Logo, write a script to draw a square using turtle graphics.",
+    "Mercury": "In Mercury, implement a type-safe program to solve a Sudoku puzzle.",
+    "Modula-2": "In Modula-2, implement a module to manage a simple inventory system.",
+    "OCaml": "In OCaml, implement a function to balance a binary search tree.",
+    "PL/I": "In PL/I, write a program to calculate monthly loan payments based on principal and interest rate.",
+    "Processing": "In Processing, create a visual sketch where circles follow the mouse cursor.",
+    "Puppet": "In Puppet, write a manifest to configure a web server with specific ports and packages.",
+    "Racket": "In Racket, implement a simple interpreter for a custom arithmetic language.",
+    "REXX": "In REXX, write a script to parse and reformat a log file into a report.",
+    "SAS": "In SAS, implement a data analysis program to compute summary statistics from a dataset.",
+    "Scratch": "In Scratch, create a simple game where a sprite moves to avoid obstacles.",
+    "Solidity": "In Solidity, implement a smart contract for a voting system on the Ethereum blockchain.",
+    "SPARK": "In SPARK, implement a verified program to control an elevator��s floor selection safely.",
+    "Squirrel": "In Squirrel, write a script to manage a simple game character��s health and inventory.",
+    "Stan": "In Stan, implement a Bayesian model to fit a linear regression to sample data.",
+    "Vala": "In Vala, implement a GTK application with a window and a button that changes color when clicked.",
+    "VBA": "In VBA, automate an Excel task to sum a column and highlight cells above a threshold.",
+    "Zig": "In Zig, implement a memory-safe program to reverse an array without using extra space.",
+    "Apex": "In Apex, implement a Salesforce trigger to automatically update a contact��s status when an opportunity is closed.",
+    "B": "In B, write a low-level function to swap two integer values without using a temporary variable.",
+    "Brainfuck": "In Brainfuck, implement a program to print 'Hello, World!' using its minimalistic syntax.",
+    "Ceylon": "In Ceylon, implement a type-safe class hierarchy for a library system with books and borrowers.",
+    "CFML": "In CFML, create a dynamic page to display a user��s profile fetched from a database.",
+    "Chisel": "In Chisel, design a hardware module for a simple 4-bit counter.",
+    "Clarion": "In Clarion, implement a database application to manage customer orders and invoices.",
+    "Clean": "In Clean, implement a purely functional program to compute the greatest common divisor (GCD) of two numbers.",
+    "Clipper": "In Clipper, write a program to manage a simple inventory database with add and delete functions.",
+    "Common Lisp": "In Common Lisp, implement a symbolic differentiation function for basic algebraic expressions.",
+    "Dafny": "In Dafny, implement a verified program to sort an array with provable correctness.",
+    "Delphi": "In Delphi, create a desktop application with a form to calculate and display a factorial.",
+    "Eiffel": "In Eiffel, implement a class with design-by-contract to manage a stack with push and pop operations.",
+    "Elm": "In Elm, build a simple web app with a text input that displays the character count in real-time.",
+    "Factor": "In Factor, implement a stack-based program to calculate the sum of numbers from 1 to n.",
+    "Falcon": "In Falcon, write a script to process and format a CSV file into a readable report.",
+    "Frink": "In Frink, implement a unit conversion program to convert between miles and kilometers.",
+    "GAMS": "In GAMS, model an optimization problem to minimize the cost of a production process.",
+    "GAP": "In GAP, implement a group theory computation to find the order of a permutation group.",
+    "Genie": "In Genie, create a simple GTK application with a button that toggles a label��s text.",
+    "Haxe": "In Haxe, implement a cross-platform program to draw a animated bouncing ball.",
+    "Hy": "In Hy, write a Lisp-style program to filter even numbers from a list.",
+    "Idris": "In Idris, implement a dependently-typed function to append two lists with length preservation.",
+    "Inform": "In Inform, create a simple text adventure game where the player navigates a room.",
+    "JCL": "In JCL, write a job control script to execute a COBOL program on a mainframe.",
+    "Jython": "In Jython, implement a Python script to interact with a Java library and print system properties.",
+    "KRL": "In KRL, design a rule-based system to send a notification when a sensor value exceeds a threshold.",
+    "LFE": "In LFE, implement a Lisp-based concurrent program to calculate factorials using actors.",
+    "MQL4": "In MQL4, write a trading script for MetaTrader to calculate moving averages of stock prices.",
+    "Nemerle": "In Nemerle, implement a macro-based program to generate boilerplate code for a class.",
+    "Oberon": "In Oberon, write a modular program to simulate a simple file system.",
+    "Opa": "In Opa, implement a full-stack web app with a chat interface running on both client and server.",
+    "OpenCL": "In OpenCL, write a parallel program to perform matrix addition on a GPU.",
+    "Oz": "In Oz, implement a multi-paradigm program to solve a constraint satisfaction problem like Sudoku.",
+    "Pike": "In Pike, create a simple network server that echoes back client messages.",
+    "Pony": "In Pony, implement an actor-based program to simulate a bank with concurrent transactions.",
+    "Pure": "In Pure, write a term-rewriting program to simplify algebraic expressions.",
+    "Q#": "In Q#, implement a quantum algorithm to generate a random bit using superposition.",
+    "Red": "In Red, create a lightweight GUI app with a button that changes the window��s title.",
+    "Ring": "In Ring, implement a simple game loop to move a character across the screen.",
+    "RPG": "In RPG, write a business program to calculate employee payroll with tax deductions.",
+    "S-Lang": "In S-Lang, implement a script to process astronomical data and compute averages.",
+    "Simula": "In Simula, simulate a queueing system for customers in a bank.",
+    "Standard ML": "In Standard ML, implement a functional program to reverse a list using pattern matching.",
+    "SuperCollider": "In SuperCollider, create a synthesizer script to generate a sine wave sound.",
+    "Turing": "In Turing, write an educational program to draw a spiral using turtle graphics.",
+    "UnrealScript": "In UnrealScript, implement a simple game mechanic to spawn an enemy in Unreal Engine.",
+    "Wolfram Language": "In Wolfram Language, create a visualization of a 3D mathematical function.",
+    "X10": "In X10, implement a distributed program to compute the sum of an array across multiple nodes.",
+    "ZPL": "In ZPL, write a parallel program to perform a stencil computation on a 2D grid.",
+    "A#": "In A#, implement a simple audio processing script to generate a square wave tone.",
+    "ABC": "In ABC, write a program to calculate and display the first 10 numbers of the Fibonacci sequence.",
+    "Accent": "In Accent, implement a reactive system to update a counter based on incoming events.",
+    "Agora": "In Agora, create an object-oriented program to simulate a simple online auction.",
+    "Alef": "In Alef, implement a concurrent program to manage multiple threads printing numbers.",
+    "Alice": "In Alice, design a 3D animation where a character walks across a scene.",
+    "Amiga E": "In Amiga E, write a program to draw a colorful pattern on an Amiga-style display.",
+    "AngelScript": "In AngelScript, implement a game scripting function to handle player movement in a 2D game.",
+    "APL2": "In APL2, write an array-based program to compute the transpose of a matrix.",
+    "Arc": "In Arc, implement a minimal web server that serves a custom HTML page.",
+    "AspectJ": "In AspectJ, add logging functionality to an existing Java class using aspects.",
+    "Ateji PX": "In Ateji PX, implement a parallel program to compute the sum of a large array.",
+    "AutoHotkey": "In AutoHotkey, create a script to automate repetitive keyboard and mouse actions.",
+    "Befunge": "In Befunge, write a 2D program to print the numbers from 1 to 10.",
+    "BLISS": "In BLISS, implement a low-level routine to optimize bit manipulation in a system program.",
+    "Boo": "In Boo, create a simple scripting program to process and sort a list of strings.",
+    "C--": "In C--, implement an intermediate language function to multiply two integers.",
+    "Caml": "In Caml, write a functional program to compute the depth of a binary tree.",
+    "Charity": "In Charity, implement a purely functional program to process a stream of integers.",
+    "ChucK": "In ChucK, create a real-time audio program to play a repeating drum pattern.",
+    "Cilk": "In Cilk, implement a parallel program to compute the factorial of a number using spawn and sync.",
+    "COMAL": "In COMAL, write an educational program to teach loops by printing a multiplication table.",
+    "Curl": "In Curl, implement a rich internet application with a clickable button that changes text.",
+    "DASL": "In DASL, design a dataflow program to filter and transform a stream of sensor data.",
+    "DIBOL": "In DIBOL, write a business application to calculate total sales from a list of transactions.",
+    "E": "In E, implement a secure distributed system to exchange encrypted messages between two nodes.",
+    "Ease": "In Ease, create a visual programming script to animate a bouncing ball.",
+    "Euphoria": "In Euphoria, write a simple interpreter for a custom command language.",
+    "Fantom": "In Fantom, implement a cross-platform program to read and display a configuration file.",
+    "Ferite": "In Ferite, create a lightweight script to simulate a dice-rolling game.",
+    "FL": "In FL, implement a functional logic program to solve a simple puzzle like Towers of Hanoi.",
+    "Focal": "In Focal, write a scientific calculator program to compute trigonometric functions.",
+    "GameMaker Language": "In GameMaker Language, implement a simple platformer mechanic for jumping.",
+    "GML": "In GML, design a graph manipulation program to find the shortest path between nodes.",
+    "Hope": "In Hope, implement a lazy functional program to generate an infinite list of primes.",
+    "ICI": "In ICI, write a script to process and reformat a text file into a table.",
+    "INTERCAL": "In INTERCAL, create a humorous program to compute '2 + 2' with unnecessary complexity.",
+    "Joy": "In Joy, implement a stack-based program to reverse a sequence of numbers.",
+    "Kodu": "In Kodu, design a simple game where a character collects objects in a 3D world.",
+    "Leda": "In Leda, write a multi-paradigm program combining imperative and functional styles to sort a list.",
+    "Limbo": "In Limbo, implement a concurrent program for a distributed system running on Inferno OS.",
+    "Miranda": "In Miranda, create a lazy functional program to compute the sum of squares up to n.",
+    "MUMPS": "In MUMPS, implement a database application to store and retrieve patient records.",
+    "Nial": "In Nial, write an array-oriented program to compute the average of nested arrays.",
+    "Obliq": "In Obliq, implement a distributed GUI application with a button that updates remotely.",
+    "OmniMark": "In OmniMark, create a text-processing script to convert XML to HTML.",
+    "Opal": "In Opal, implement a functional program to manipulate algebraic data types like lists.",
+    "PILOT": "In PILOT, write an educational script to quiz users on basic math problems.",
+    "Self": "In Self, implement a prototype-based object system to model a simple car with behaviors.",
+    "TOM": "In TOM, create a pattern-matching program to transform a string based on rules."    	    	
+}

config.json

@@ -0,0 +1,61 @@
+{
+  "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": 0,
+  "eos_token_id": 1,
+  "ep_size": 1,
+  "first_k_dense_replace": 3,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 18432,
+  "kv_lora_rank": 512,
+  "max_position_embeddings": 163840,
+  "model_type": "deepseek_v3",
+  "moe_intermediate_size": 2048,
+  "moe_layer_freq": 1,
+  "n_group": 8,
+  "n_routed_experts": 160,
+  "n_shared_experts": 1,
+  "norm_topk_prob": true,
+  "num_attention_heads": 128,
+  "num_experts_per_tok": 6,
+  "num_hidden_layers": 61,
+  "num_key_value_heads": 128,
+  "num_nextn_predict_layers": 1,
+  "pretraining_tp": 1,
+  "q_lora_rank": 1536,
+  "qk_nope_head_dim": 128,
+  "qk_rope_head_dim": 64,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "factor": 40,
+    "mscale": 1.0,
+    "mscale_all_dim": 1.0,
+    "original_max_position_embeddings": 4096,
+    "type": "yarn"
+  },
+  "rope_theta": 10000,
+  "routed_scaling_factor": 2.5,
+  "scoring_func": "sigmoid",
+  "seq_aux": true,
+  "tie_word_embeddings": false,
+  "topk_group": 4,
+  "topk_method": "noaux_tc",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.33.1",
+  "use_cache": true,
+  "v_head_dim": 128,
+  "vocab_size": 129280
+}

configuration_deepseek.py

@@ -0,0 +1,210 @@
+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,
+        )

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+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "transformers_version": "4.50.0.dev0"
+}

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