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.gitignore

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+.joycode/
+venv/

README.md

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+---
+language:
+- zh
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+<div align="center">
+  <picture>
+      <img src="figures/joyai-logo.png" width="30%" alt="JoyAI-LLM Flash">
+  </picture>
+</div>
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/jdopensource" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-JD-ffc107?color=ffc107&logoColor=white"/></a>
+  <a href="https://huggingface.co/jdopensource/JoyAI-LLM-Flash/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Modified_MIT-f5de53?&color=f5de53"/></a>
+</div>
+
+
+
+
+## 1. Model Introduction
+
+JoyAI-LLM-Flash is a state-of-the-art medium-sized instruct language model with 3 billion activated parameters and 48 billion total parameters. JoyAI-LLM-Flash was pretrained on 20 trillion text tokens using Muon optimizer, followed by large-scale supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL) across diverse environments. JoyAI-LLM-Flash achieves strong performance across frontier knowledge, reasoning, coding tasks and agentic capabilities.
+
+### Key Features
+
+- Fiber Bundle RL: Introduces fiber bundle theory into reinforcement learning, proposing a novel optimization framework, FiberPO. This method is specifically designed to handle the challenges of large-scale and heterogeneous agent training, improving stability and robustness under complex data distributions.
+- Training-Inference Collaboration: apply Muon optimizer with dense MTP, develop novel optimization techniques to resolve instabilities while scaling up, delivering 1.3× to 1.7× the throughput of the non-MTP version.
+- Agentic Intelligence: designed for tool use, reasoning, and autonomous problem-solving.
+
+## 2. Model Summary
+
+|                                             |                          |
+| :-----------------------------------------: | :----------------------: |
+|              **Architecture**               | Mixture-of-Experts (MoE) |
+|            **Total Parameters**             |           48B            |
+|          **Activated Parameters**           |            3B            |
+| **Number of Layers** (Dense layer included) |            40            |
+|         **Number of Dense Layers**          |            1             |
+|       **Attention Hidden Dimension**        |           2048           |
+|    **MoE Hidden Dimension** (per Expert)    |           768            |
+|        **Number of Attention Heads**        |            32            |
+|            **Number of Experts**            |           256            |
+|       **Selected Experts per Token**        |            8             |
+|        **Number of Shared Experts**         |            1             |
+|             **Vocabulary Size**             |           129K           |
+|             **Context Length**              |           128K           |
+|           **Attention Mechanism**           |           MLA            |
+|           **Activation Function**           |          SwiGLU          |
+|                   </div>                    |                          |
+
+
+## 3. Evaluation Results
+
+<table>
+<thead>
+<tr>
+<th align="center">Benchmark</th>
+<th align="center"><sup>JoyAI-LLM Flash</sup></th>
+<th align="center"><sup>Qwen3-30B-A3B-Instuct-2507</sup></th>
+<th align="center"><sup>GLM-4.7-Flash<br>(Non-thinking)</sup></th>
+</tr>
+</thead>
+<tbody>
+
+
+<tr>
+<td align="center" colspan=8><strong>Knowledge &amp; Alignment</strong></td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">MMLU</td>
+<td align="center" style="vertical-align: middle"><strong>89.50</strong></td>
+<td align="center" style="vertical-align: middle">86.87</td>
+<td align="center" style="vertical-align: middle">80.53</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">MMLU-Pro</td>
+<td align="center" style="vertical-align: middle"><strong>81.02</strong></td>
+<td align="center" style="vertical-align: middle">73.88</td>
+<td align="center" style="vertical-align: middle">63.62</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">CMMLU</td>
+<td align="center" style="vertical-align: middle"><strong>87.03</strong></td>
+<td align="center" style="vertical-align: middle">85.88</td>
+<td align="center" style="vertical-align: middle">75.85</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">GPQA-Diamond</td>
+<td align="center" style="vertical-align: middle"><strong>74.43</strong></td>
+<td align="center" style="vertical-align: middle">68.69</td>
+<td align="center" style="vertical-align: middle">39.90</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">SuperGPQA</td>
+<td align="center" style="vertical-align: middle"><strong>55.00</strong></td>
+<td align="center" style="vertical-align: middle">52.00</td>
+<td align="center" style="vertical-align: middle">32.00</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">LiveBench</td>
+<td align="center" style="vertical-align: middle"><strong>72.90</strong></td>
+<td align="center" style="vertical-align: middle">59.70</td>
+<td align="center" style="vertical-align: middle">43.10</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">IFEval</td>
+<td align="center" style="vertical-align: middle"><strong>86.69</strong></td>
+<td align="center" style="vertical-align: middle">83.18</td>
+<td align="center" style="vertical-align: middle">82.44</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">AlignBench</td>
+<td align="center" style="vertical-align: middle"><strong>8.24</strong></td>
+<td align="center" style="vertical-align: middle">8.07</td>
+<td align="center" style="vertical-align: middle">6.85</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">HellaSwag</td>
+<td align="center" style="vertical-align: middle"><strong>91.79</strong></td>
+<td align="center" style="vertical-align: middle">89.90</td>
+<td align="center" style="vertical-align: middle">60.84</td>
+</tr>
+
+<tr>
+<td align="center" colspan=8><strong>Coding</strong></td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">HumanEval</td>
+<td align="center" style="vertical-align: middle"><strong>96.34</strong></td>
+<td align="center" style="vertical-align: middle">95.12</td>
+<td align="center" style="vertical-align: middle">74.39</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">LiveCodeBench</td>
+<td align="center" style="vertical-align: middle"><strong>65.60</strong></td>
+<td align="center" style="vertical-align: middle">39.71</td>
+<td align="center" style="vertical-align: middle">27.43</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">SciCode</td>
+<td align="center" style="vertical-align: middle"><strong>3.08/22.92</strong></td>
+<td align="center" style="vertical-align: middle"><strong>3.08/22.92</strong></td>
+<td align="center" style="vertical-align: middle">3.08/15.11</td>
+</tr>
+<tr>
+<td align="center" colspan=8><strong>Mathematics</strong></td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">GSM8K</td>
+<td align="center" style="vertical-align: middle"><strong>95.83</strong></td>
+<td align="center" style="vertical-align: middle">79.83</td>
+<td align="center" style="vertical-align: middle">81.88</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">AIME2025</td>
+<td align="center" style="vertical-align: middle"><strong>65.83</strong></td>
+<td align="center" style="vertical-align: middle">62.08</td>
+<td align="center" style="vertical-align: middle">24.17</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">MATH 500</td>
+<td align="center" style="vertical-align: middle"><strong>97.10</strong></td>
+<td align="center" style="vertical-align: middle">89.80</td>
+<td align="center" style="vertical-align: middle">90.90</td>
+</tr>
+
+<tr>
+<td align="center" colspan=8><strong>Agentic</strong></td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">SWE-bench Verified</td>
+<td align="center" style="vertical-align: middle"><strong>60.60</strong></td>
+<td align="center" style="vertical-align: middle">24.44</td>
+<td align="center" style="vertical-align: middle">51.60</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">Tau2-Retail</td>
+<td align="center" style="vertical-align: middle"><strong>67.55</strong></td>
+<td align="center" style="vertical-align: middle">53.51</td>
+<td align="center" style="vertical-align: middle">62.28</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">Tau2-Airline</td>
+<td align="center" style="vertical-align: middle"><strong>54.00</strong></td>
+<td align="center" style="vertical-align: middle">32.00</td>
+<td align="center" style="vertical-align: middle">52.00</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">Tau2-Telecom</td>
+<td align="center" style="vertical-align: middle">79.83</td>
+<td align="center" style="vertical-align: middle">4.39</td>
+<td align="center" style="vertical-align: middle"><strong>88.60</strong></td>
+</tr>
+
+<tr>
+<td align="center" colspan=8><strong>Long Context</strong></td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">RULER</td>
+<td align="center" style="vertical-align: middle"><strong>95.60</strong></td>
+<td align="center" style="vertical-align: middle">89.66</td>
+<td align="center" style="vertical-align: middle">56.12</td>
+</tr>
+</tbody>
+</table>
+
+
+## 4. Deployment
+
+> [!Note]
+> You can access JoyAI-LLM Flash API on https://docs.jdcloud.com/cn/jdaip/chat and we provide OpenAI/Anthropic-compatible API for you. 
+> Currently, JoyAI-LLM-Flash-INT4 is recommended to run on the following inference engines:
+
+* vLLM
+* SGLang
+
+The minimum version requirement for `transformers` is `4.57.1`.
+
+Deployment examples can be found in the [Model Deployment Guide](docs/deploy_guidance.md).
+
+
+
+## 5. Model Usage
+
+The usage demos below demonstrate how to call our official API.
+
+For third-party APIs deployed with vLLM or SGLang, please note that:
+
+> [!Note] Recommended sampling parameters: `temperature=0.6`, `top_p=1.0`
+
+### Chat Completion
+
+This is a simple chat completion script which shows how to call JoyAI-Flash API.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://IP:PORT/v1", api_key="EMPTY")
+
+
+def simple_chat(client: OpenAI):
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "which one is bigger, 9.11 or 9.9? think carefully.",
+                }
+            ],
+        },
+    ]
+    model_name = client.models.list().data[0].id
+    response = client.chat.completions.create(
+        model=model_name, messages=messages, stream=False, max_tokens=4096
+    )
+    print(f"response: {response.choices[0].message.content}")
+
+
+if __name__ == "__main__":
+    simple_chat(client)
+```
+
+
+### Tool call Completion
+
+This is a simple toll call completion script which shows how to call JoyAI-Flash API.
+
+```python
+import json
+
+from openai import OpenAI
+
+client = OpenAI(base_url="http://IP:PORT/v1", api_key="EMPTY")
+
+
+def my_calculator(expression: str) -> str:
+    return str(eval(expression))
+
+
+def rewrite(expression: str) -> str:
+    return str(expression)
+
+
+def simple_tool_call(client: OpenAI):
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "use my functions to compute the results for the equations: 6+1",
+                },
+            ],
+        },
+    ]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "my_calculator",
+                "description": "A calculator that can evaluate a mathematical equation and compute its results.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "expression": {
+                            "type": "string",
+                            "description": "The mathematical expression to evaluate.",
+                        },
+                    },
+                    "required": ["expression"],
+                },
+            },
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "rewrite",
+                "description": "Rewrite a given text for improved clarity",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "text": {
+                            "type": "string",
+                            "description": "The input text to rewrite",
+                        }
+                    },
+                },
+            },
+        },
+    ]
+    model_name = client.models.list().data[0].id
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        temperature=1.0,
+        max_tokens=1024,
+        tools=tools,
+        tool_choice="auto",
+    )
+    tool_calls = response.choices[0].message.tool_calls
+
+    results = []
+    for tool_call in tool_calls:
+        function_name = tool_call.function.name
+        function_args = tool_call.function.arguments
+        if function_name == "my_calculator":
+            result = my_calculator(**json.loads(function_args))
+            results.append(result)
+    messages.append({"role": "assistant", "tool_calls": tool_calls})
+    for tool_call, result in zip(tool_calls, results):
+        messages.append(
+            {
+                "role": "tool",
+                "tool_call_id": tool_call.id,
+                "name": tool_call.function.name,
+                "content": result,
+            }
+        )
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        temperature=1.0,
+        max_tokens=1024,
+    )
+    print(response.choices[0].message.content)
+
+
+if __name__ == "__main__":
+    simple_tool_call(client)
+
+```
+
+---
+
+## 6. License
+
+Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).

chat_template.jinja

@@ -0,0 +1,103 @@
+{%- macro render_extra_keys(json_dict, handled_keys) -%}
+    {%- if json_dict is mapping -%}
+        {%- for json_key in json_dict if json_key not in handled_keys -%}
+            {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) -%}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' -}}
+            {%- else -%}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' -}}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- if not add_generation_prompt is defined -%}{%- set add_generation_prompt = false -%}{%- endif -%}
+
+{%- set ns = namespace(system_prompt='', is_first_sp=true, is_last_user=false) -%}
+{%- set default_system = "You are JoyAI , a large language model trained by JD（京东）that can interact with a computer to solve tasks. Answer as concisely as possible." -%}
+{%- set ns.system_prompt = default_system -%}
+
+{%- for message in messages -%}
+    {%- if message['role'] == 'system' -%}
+        {%- if ns.is_first_sp -%}
+            {%- set ns.system_prompt = message['content'] -%}
+            {%- set ns.is_first_sp = false -%}
+        {%- else -%}
+            {%- set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] -%}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{{- bos_token -}}{{- ns.system_prompt -}}
+{%- if tools is iterable and tools | length > 0 -%}
+    {{- "\n\n# Tools\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
+        {%- if tool.description is defined %}
+            {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
+        {%- endif %}
+        {{- '\n<parameters>' }}
+        {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
+            {%- for param_name, param_fields in tool.parameters.properties|items %}
+                {{- '\n<parameter>' }}
+                {{- '\n<name>' ~ param_name ~ '</name>' }}
+                {%- if param_fields.type is defined %}
+                    {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+                {%- endif %}
+                {%- if param_fields.description is defined %}
+                    {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
+                {%- endif %}
+                {%- set handled_keys = ['name', 'type', 'description'] %}
+                {{- render_extra_keys(param_fields, handled_keys) }}
+                {{- '\n</parameter>' }}
+            {%- endfor %}
+        {%- endif %}
+        {% set handled_keys = ['type', 'properties'] %}
+        {{- render_extra_keys(tool.parameters, handled_keys) }}
+        {{- '\n</parameters>' }}
+        {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+        {{- render_extra_keys(tool, handled_keys) }}
+        {{- '\n</function>' }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+{%- for message in messages -%}
+    {%- if message['role'] == 'user' -%}
+        {%- set ns.is_last_user = true -%}
+        {{- '<|User|>' + message['content'] -}}
+    {%- elif message['role'] == 'assistant' -%}
+        {%- if ns.is_last_user -%}
+            {{ '<|Assistant|>' }}
+        {%- endif -%}
+        {%- set ns.is_last_user = false -%}
+        {%- set content = message.get('content') | default('', true) -%}
+        {{ '<|end_of_thought|>' + content }}
+        {%- if message['tool_calls'] is defined and message['tool_calls'] is not none -%}
+            {%- for tool in message['tool_calls'] -%}
+                {%- if tool.function is defined %}{% set tool = tool.function %}{% endif -%}
+                {{- '\n<tool_call>\n<function=' + tool.name + '>\n' -}}
+                {%- if tool.arguments is defined -%}
+                    {%- if tool.arguments is string -%}{%- set args_data = tool.arguments | from_json -%}{%- else -%}{%- set args_data = tool.arguments -%}{%- endif -%}
+                    {%- for args_name, args_value in args_data.items() -%}
+                        {{- '<parameter=' + args_name + '>\n' -}}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string -%}
+                        {{- args_value -}}{{- '\n</parameter>\n' -}}
+                    {%- endfor -%}
+                {%- endif -%}
+                {{- '</function>\n</tool_call>' -}}
+            {%- endfor -%}
+        {%- endif -%}
+        {{ '<|end▁of▁sentence|>' }}
+    {%- elif message['role'] == 'tool' -%}
+        {%- set ns.is_last_user = true -%}
+        {{ '\n<tool_response>\n' + message['content'] + '\n</tool_response>' }}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {{ '<|Assistant|>' }}{{ '<|end_of_thought|>' }}
+{%- endif -%}

config.json

@@ -0,0 +1,82 @@
+{
+    "architectures": [
+        "DeepseekV3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "auto_map": {
+        "AutoConfig": "configuration_deepseek.DeepseekV3Config",
+        "AutoModel": "modeling_deepseek.DeepseekV3Model",
+        "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
+    },
+    "bos_token_id": 0,
+    "eos_token_id": 1,
+    "ep_size": 1,
+    "first_k_dense_replace": 1,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 7168,
+    "kv_lora_rank": 512,
+    "max_position_embeddings": 131072,
+    "model_type": "joyai_llm_flash",
+    "moe_intermediate_size": 768,
+    "moe_layer_freq": 1,
+    "n_group": 1,
+    "n_routed_experts": 256,
+    "n_shared_experts": 1,
+    "norm_topk_prob": true,
+    "num_attention_heads": 32,
+    "num_experts_per_tok": 8,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 32,
+    "num_nextn_predict_layers": 1,
+    "q_lora_rank": 1536,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 32000000,
+    "routed_scaling_factor": 2.5,
+    "scoring_func": "sigmoid",
+    "tie_word_embeddings": false,
+    "topk_group": 1,
+    "topk_method": "noaux_tc",
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.44.2",
+    "use_cache": true,
+    "v_head_dim": 128,
+    "vocab_size": 129280,
+    "quantization_config": {
+        "config_groups": {
+            "group_0": {
+                "input_activations": null,
+                "output_activations": null,
+                "targets": [
+                    "Linear"
+                ],
+                "weights": {
+                    "actorder": null,
+                    "block_structure": null,
+                    "dynamic": false,
+                    "group_size": 32,
+                    "num_bits": 4,
+                    "observer": "minmax",
+                    "observer_kwargs": {},
+                    "strategy": "group",
+                    "symmetric": true,
+                    "type": "int"
+                }
+            }
+        },
+        "format": "pack-quantized",
+        "ignore": [
+            "lm_head",
+            "re:.*self_attn.*",
+            "re:.*shared_experts.*",
+            "re:.*mlp\\.(gate|up|gate_up|down)_proj.*"
+        ],
+        "kv_cache_scheme": null,
+        "quant_method": "compressed-tensors",
+        "quantization_status": "compressed"
+    }
+}

configuration.json

@@ -0,0 +1 @@
+{"framework":"Pytorch","task":"text-generation"}

configuration_deepseek.py

@@ -0,0 +1,247 @@
+# coding=utf-8
+# Copyright 2025 bzantium and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on the DeepSeekV3 implementations from the DeepSeek AI team. (https://huggingface.co/deepseek-ai/DeepSeek-V3)
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""DeepSeekV3 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+
+
+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.
+    e.g. [bzantium/tiny-deepseek-v3](https://huggingface.co/bzantium/tiny-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 7168):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 18432):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 2048):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 61):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 128):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 128):
+            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`.
+        n_shared_experts (`int`, *optional*, defaults to 1):
+            Number of shared experts.
+        n_routed_experts (`int`, *optional*, defaults to 256):
+            Number of routed experts.
+        routed_scaling_factor (`float`, *optional*, defaults to 2.5):
+            Scaling factor or routed experts.
+        kv_lora_rank (`int`, *optional*, defaults to 512):
+            Rank of the LoRA matrices for key and value projections.
+        q_lora_rank (`int`, *optional*, defaults to 1536):
+            Rank of the LoRA matrices for query projections.
+        qk_rope_head_dim (`int`, *optional*, defaults to 64):
+            Dimension of the query/key heads that use rotary position embeddings.
+        v_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of the value heads.
+        qk_nope_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of the query/key heads that don't use rotary position embeddings.
+        n_group (`int`, *optional*, defaults to 8):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to 4):
+            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 8):
+            Number of selected experts, None means dense model.
+        first_k_dense_replace (`int`, *optional*, defaults to 3):
+            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 `True`):
+            Whether to normalize the weights of the routed experts.
+        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 4096):
+            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 0):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 1):
+            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.
+        rope_interleave (`bool`, *optional*, defaults to `True`):
+            Whether to interleave the rotary position embeddings.
+        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"]
+    base_model_tp_plan = {  # TODO: only replicate attention layers when > first_k_dense_replace
+        "layers.*.mlp.experts.*.gate_proj": "local_colwise",
+        "layers.*.mlp.experts.*.up_proj": "local_colwise",
+        "layers.*.mlp.experts.*.down_proj": "local_rowwise",
+        "layers.*.mlp.experts.*": "local",  # each expert is wrapped in a module list
+        "layers.*.mlp.shared_experts.gate_proj": "local_colwise",
+        "layers.*.mlp.shared_experts.up_proj": "local_colwise",
+        "layers.*.mlp.shared_experts.down_proj": "local_rowwise",
+        "layers.*.mlp.shared_experts": "local",
+        "layers.*.mlp.gate_proj": "local_colwise",
+        "layers.*.mlp.up_proj": "local_colwise",
+        "layers.*.mlp.down_proj": "local_rowwise",
+        "layers.*.mlp": "gather",  # This is the only moment where results are gathered
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=129280,
+        hidden_size=7168,
+        intermediate_size=18432,
+        moe_intermediate_size=2048,
+        num_hidden_layers=61,
+        num_attention_heads=128,
+        num_key_value_heads=128,
+        n_shared_experts=1,
+        n_routed_experts=256,
+        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,
+        n_group=8,
+        topk_group=4,
+        num_experts_per_tok=8,
+        first_k_dense_replace=3,
+        norm_topk_prob=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,
+        rope_interleave=True,
+        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_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        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.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.head_dim = qk_rope_head_dim
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.rope_interleave = rope_interleave
+
+        # 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
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, copy it it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        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,
+        )
+
+
+__all__ = ["DeepseekV3Config"]

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