Update README.md

README.md

@@ -1,240 +1,146 @@
 ---
-license: mit
-library_name: transformers
----
-# DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
-
-<!-- markdownlint-disable first-line-h1 -->
-<!-- markdownlint-disable html -->
-<!-- markdownlint-disable no-duplicate-header -->
-
-<div align="center">
-  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V4" />
-</div>
-<hr>
-<div align="center" style="line-height: 1;">
-  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V4-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<div align="center" style="line-height: 1;">
-  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
-    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<div align="center" style="line-height: 1;">
-  <a href="LICENSE" style="margin: 2px;">
-    <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-
-<p align="center">
-  <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf"><b>Technical Report</b>👁️</a>
-</p>
-
-## Introduction
-
-We present a preview version of **DeepSeek-V4** series, including two strong Mixture-of-Experts (MoE) language models — **DeepSeek-V4-Pro** with 1.6T parameters (49B activated) and **DeepSeek-V4-Flash** with 284B parameters (13B activated) — both supporting a context length of **one million tokens**.
-
-DeepSeek-V4 series incorporate several key upgrades in architecture and optimization:
-
-1. **Hybrid Attention Architecture:** We design a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to dramatically improve long-context efficiency. In the 1M-token context setting, DeepSeek-V4-Pro requires only **27% of single-token inference FLOPs** and **10% of KV cache** compared with DeepSeek-V3.2.
-2. **Manifold-Constrained Hyper-Connections (mHC):** We incorporate mHC to strengthen conventional residual connections, enhancing stability of signal propagation across layers while preserving model expressivity.
-3. **Muon Optimizer:** We employ the Muon optimizer for faster convergence and greater training stability.
-
-We pre-train both models on more than **32T** diverse and high-quality tokens, followed by a comprehensive post-training pipeline. The post-training features a two-stage paradigm: independent cultivation of domain-specific experts (through SFT and RL with GRPO), followed by unified model consolidation via on-policy distillation, integrating distinct proficiencies across diverse domains into a single model.
-
-**DeepSeek-V4-Pro-Max**, the maximum reasoning effort mode of DeepSeek-V4-Pro, significantly advances the knowledge capabilities of open-source models, firmly establishing itself as the best open-source model available today. It achieves top-tier performance in coding benchmarks and significantly bridges the gap with leading closed-source models on reasoning and agentic tasks. Meanwhile, **DeepSeek-V4-Flash-Max** achieves comparable reasoning performance to the Pro version when given a larger thinking budget, though its smaller parameter scale naturally places it slightly behind on pure knowledge tasks and the most complex agentic workflows.
-
-<div align="center">
- <img src="assets/dsv4_performance.png" >
-</div>
-
-## Model Downloads
-
-<div align="center">
-
-| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Precision** | **Download** |
-| :---: | :---: | :---: | :---: | :---: | :---: |
-| DeepSeek-V4-Flash-Base | 284B | 13B | 1M | FP8 Mixed | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Flash-Base) |
-| DeepSeek-V4-Flash | 284B | 13B | 1M | FP4 + FP8 Mixed* | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Flash) |
-| DeepSeek-V4-Pro-Base | 1.6T | 49B | 1M | FP8 Mixed | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Pro-Base) |
-| DeepSeek-V4-Pro | 1.6T | 49B | 1M | FP4 + FP8 Mixed* | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Pro) |
-
-</div>
-
-*\*FP4 + FP8 Mixed: MoE expert parameters use FP4 precision; most other parameters use FP8.*
-
-## Evaluation Results
-
-### Base Model
-
-<div align="center">
-
-| Benchmark (Metric) | # Shots | DeepSeek-V3.2-Base | DeepSeek-V4-Flash-Base | DeepSeek-V4-Pro-Base |
-| :--- | :---: | :---: | :---: | :---: |
-| Architecture | - | MoE | MoE | MoE |
-| # Activated Params | - | 37B | 13B | 49B |
-| # Total Params | - | 671B | 284B | 1.6T |
-| **World Knowledge** | | | | |
-| AGIEval (EM) | 0-shot | 80.1 | 82.6 | **83.1** |
-| MMLU (EM) | 5-shot | 87.8 | 88.7 | **90.1** |
-| MMLU-Redux (EM) | 5-shot | 87.5 | 89.4 | **90.8** |
-| MMLU-Pro (EM) | 5-shot | 65.5 | 68.3 | **73.5** |
-| MMMLU (EM) | 5-shot | 87.9 | 88.8 | **90.3** |
-| C-Eval (EM) | 5-shot | 90.4 | 92.1 | **93.1** |
-| CMMLU (EM) | 5-shot | 88.9 | 90.4 | **90.8** |
-| MultiLoKo (EM) | 5-shot | 38.7 | 42.2 | **51.1** |
-| Simple-QA verified (EM) | 25-shot | 28.3 | 30.1 | **55.2** |
-| SuperGPQA (EM) | 5-shot | 45.0 | 46.5 | **53.9** |
-| FACTS Parametric (EM) | 25-shot | 27.1 | 33.9 | **62.6** |
-| TriviaQA (EM) | 5-shot | 83.3 | 82.8 | **85.6** |
-| **Language & Reasoning** | | | | |
-| BBH (EM) | 3-shot | **87.6** | 86.9 | 87.5 |
-| DROP (F1) | 1-shot | 88.2 | 88.6 | **88.7** |
-| HellaSwag (EM) | 0-shot | 86.4 | 85.7 | **88.0** |
-| WinoGrande (EM) | 0-shot | 78.9 | 79.5 | **81.5** |
-| CLUEWSC (EM) | 5-shot | 83.5 | 82.2 | **85.2** |
-| **Code & Math** | | | | |
-| BigCodeBench (Pass@1) | 3-shot | **63.9** | 56.8 | 59.2 |
-| HumanEval (Pass@1) | 0-shot | 62.8 | 69.5 | **76.8** |
-| GSM8K (EM) | 8-shot | 91.1 | 90.8 | **92.6** |
-| MATH (EM) | 4-shot | 60.5 | 57.4 | **64.5** |
-| MGSM (EM) | 8-shot | 81.3 | **85.7** | 84.4 |
-| CMath (EM) | 3-shot | 92.6 | **93.6** | 90.9 |
-| **Long Context** | | | | |
-| LongBench-V2 (EM) | 1-shot | 40.2 | 44.7 | **51.5** |
-
-</div>
-
-### Instruct Model
-
-DeepSeek-V4-Pro and DeepSeek-V4-Flash both support three reasoning effort modes:
-
-| Reasoning Mode | Characteristics | Typical Use Cases | Response Format |
-| :--- | :--- | :--- | :--- |
-| Non-think | Fast, intuitive responses | Routine daily tasks, low-risk decisions | `</think>` summary |
-| Think High | Conscious logical analysis, slower but more accurate | Complex problem-solving, planning | `<think>` thinking `</think>` summary |
-| Think Max | Push reasoning to its fullest extent | Exploring the boundary of model reasoning capability | Special system prompt + `<think>` thinking `</think>` summary |
-
-#### DeepSeek-V4-Pro-Max vs Frontier Models
-
-<div align="center">
-
-| Benchmark (Metric) | Opus-4.6 Max | GPT-5.4 xHigh | Gemini-3.1-Pro High | K2.6 Thinking | GLM-5.1 Thinking | DS-V4-Pro Max |
-| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
-| **Knowledge & Reasoning** | | | | | | |
-| MMLU-Pro (EM) | 89.1 | 87.5 | **91.0** | 87.1 | 86.0 | 87.5 |
-| SimpleQA-Verified (Pass@1) | 46.2 | 45.3 | **75.6** | 36.9 | 38.1 | 57.9 |
-| Chinese-SimpleQA (Pass@1) | 76.4 | 76.8 | **85.9** | 75.9 | 75.0 | 84.4 |
-| GPQA Diamond (Pass@1) | 91.3 | 93.0 | **94.3** | 90.5 | 86.2 | 90.1 |
-| HLE (Pass@1) | 40.0 | 39.8 | **44.4** | 36.4 | 34.7 | 37.7 |
-| LiveCodeBench (Pass@1) | 88.8 | - | 91.7 | 89.6 | - | **93.5** |
-| Codeforces (Rating) | - | 3168 | 3052 | - | - | **3206** |
-| HMMT 2026 Feb (Pass@1) | 96.2 | **97.7** | 94.7 | 92.7 | 89.4 | 95.2 |
-| IMOAnswerBench (Pass@1) | 75.3 | **91.4** | 81.0 | 86.0 | 83.8 | 89.8 |
-| Apex (Pass@1) | 34.5 | 54.1 | **60.9** | 24.0 | 11.5 | 38.3 |
-| Apex Shortlist (Pass@1) | 85.9 | 78.1 | 89.1 | 75.5 | 72.4 | **90.2** |
-| **Long Context** | | | | | | |
-| MRCR 1M (MMR) | **92.9** | - | 76.3 | - | - | 83.5 |
-| CorpusQA 1M (ACC) | **71.7** | - | 53.8 | - | - | 62.0 |
-| **Agentic** | | | | | | |
-| Terminal Bench 2.0 (Acc) | 65.4 | **75.1** | 68.5 | 66.7 | 63.5 | 67.9 |
-| SWE Verified (Resolved) | **80.8** | - | 80.6 | 80.2 | - | 80.6 |
-| SWE Pro (Resolved) | 57.3 | 57.7 | 54.2 | **58.6** | 58.4 | 55.4 |
-| SWE Multilingual (Resolved) | **77.5** | - | - | 76.7 | 73.3 | 76.2 |
-| BrowseComp (Pass@1) | 83.7 | 82.7 | **85.9** | 83.2 | 79.3 | 83.4 |
-| HLE w/ tools (Pass@1) | 53.1 | 52.0 | 51.6 | **54.0** | 50.4 | 48.2 |
-| GDPval-AA (Elo) | 1619 | **1674** | 1314 | 1482 | 1535 | 1554 |
-| MCPAtlas Public (Pass@1) | **73.8** | 67.2 | 69.2 | 66.6 | 71.8 | 73.6 |
-| Toolathlon (Pass@1) | 47.2 | **54.6** | 48.8 | 50.0 | 40.7 | 51.8 |
-
-</div>
-
-#### Comparison across Modes
-
-<div align="center">
-
-| Benchmark (Metric) | V4-Flash Non-Think | V4-Flash High | V4-Flash Max | V4-Pro Non-Think | V4-Pro High | V4-Pro Max |
-| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
-| **Knowledge & Reasoning** | | | | | | |
-| MMLU-Pro (EM) | 83.0 | 86.4 | 86.2 | 82.9 | 87.1 | **87.5** |
-| SimpleQA-Verified (Pass@1) | 23.1 | 28.9 | 34.1 | 45.0 | 46.2 | **57.9** |
-| Chinese-SimpleQA (Pass@1) | 71.5 | 73.2 | 78.9 | 75.8 | 77.7 | **84.4** |
-| GPQA Diamond (Pass@1) | 71.2 | 87.4 | 88.1 | 72.9 | 89.1 | **90.1** |
-| HLE (Pass@1) | 8.1 | 29.4 | 34.8 | 7.7 | 34.5 | **37.7** |
-| LiveCodeBench (Pass@1) | 55.2 | 88.4 | 91.6 | 56.8 | 89.8 | **93.5** |
-| Codeforces (Rating) | - | 2816 | 3052 | - | 2919 | **3206** |
-| HMMT 2026 Feb (Pass@1) | 40.8 | 91.9 | 94.8 | 31.7 | 94.0 | **95.2** |
-| IMOAnswerBench (Pass@1) | 41.9 | 85.1 | 88.4 | 35.3 | 88.0 | **89.8** |
-| Apex (Pass@1) | 1.0 | 19.1 | 33.0 | 0.4 | 27.4 | **38.3** |
-| Apex Shortlist (Pass@1) | 9.3 | 72.1 | 85.7 | 9.2 | 85.5 | **90.2** |
-| **Long Context** | | | | | | |
-| MRCR 1M (MMR) | 37.5 | 76.9 | 78.7 | 44.7 | 83.3 | **83.5** |
-| CorpusQA 1M (ACC) | 15.5 | 59.3 | 60.5 | 35.6 | 56.5 | **62.0** |
-| **Agentic** | | | | | | |
-| Terminal Bench 2.0 (Acc) | 49.1 | 56.6 | 56.9 | 59.1 | 63.3 | **67.9** |
-| SWE Verified (Resolved) | 73.7 | 78.6 | 79.0 | 73.6 | 79.4 | **80.6** |
-| SWE Pro (Resolved) | 49.1 | 52.3 | 52.6 | 52.1 | 54.4 | **55.4** |
-| SWE Multilingual (Resolved) | 69.7 | 70.2 | 73.3 | 69.8 | 74.1 | **76.2** |
-| BrowseComp (Pass@1) | - | 53.5 | 73.2 | - | 80.4 | **83.4** |
-| HLE w/ tools (Pass@1) | - | 40.3 | 45.1 | - | 44.7 | **48.2** |
-| MCPAtlas (Pass@1) | 64.0 | 67.4 | 69.0 | 69.4 | **74.2** | 73.6 |
-| GDPval-AA (Elo) | - | - | 1395 | - | - | **1554** |
-| Toolathlon (Pass@1) | 40.7 | 43.5 | 47.8 | 46.3 | 49.0 | **51.8** |
 
+<!DOCTYPE html>
+<html lang="id">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+  <title>Chatbot Sederhana</title>
+
+  <style>
+    body {
+      font-family: Arial, sans-serif;
+      background: #f0f2f5;
+      display: flex;
+      justify-content: center;
+      align-items: center;
+      height: 100vh;
+    }
+
+    .chat-container {
+      width: 400px;
+      background: white;
+      border-radius: 10px;
+      overflow: hidden;
+      box-shadow: 0 4px 10px rgba(0,0,0,0.2);
+    }
+
+    .chat-header {
+      background: #4CAF50;
+      color: white;
+      padding: 15px;
+      font-size: 20px;
+      text-align: center;
+    }
+
+    .chat-box {
+      height: 400px;
+      overflow-y: auto;
+      padding: 10px;
+    }
+
+    .message {
+      margin: 10px 0;
+      padding: 10px;
+      border-radius: 10px;
+      max-width: 80%;
+    }
+
+    .user {
+      background: #DCF8C6;
+      margin-left: auto;
+    }
+
+    .bot {
+      background: #eee;
+    }
+
+    .input-area {
+      display: flex;
+      border-top: 1px solid #ccc;
+    }
+
+    input {
+      flex: 1;
+      padding: 10px;
+      border: none;
+      outline: none;
+    }
+
+    button {
+      padding: 10px 15px;
+      border: none;
+      background: #4CAF50;
+      color: white;
+      cursor: pointer;
+    }
+
+    button:hover {
+      background: #45a049;
+    }
+  </style>
+</head>
+<body>
+
+<div class="chat-container">
+  <div class="chat-header">
+    Chatbot AI
+  </div>
+
+  <div class="chat-box" id="chatBox"></div>
+
+  <div class="input-area">
+    <input type="text" id="userInput" placeholder="Ketik pesan..." />
+    <button onclick="sendMessage()">Kirim</button>
+  </div>
 </div>
 
-## Chat Template
-
-This release does not include a Jinja-format chat template. Instead, we provide a dedicated `encoding` folder with Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model, and how to parse the model's text output. Please refer to the [`encoding`](encoding/README.md) folder for full documentation.
-
-A brief example:
-
-```python
-from encoding_dsv4 import encode_messages, parse_message_from_completion_text
-
-messages = [
-    {"role": "user", "content": "hello"},
-    {"role": "assistant", "content": "Hello! I am DeepSeek.", "reasoning_content": "thinking..."},
-    {"role": "user", "content": "1+1=?"}
-]
+<script>
+  function addMessage(text, sender) {
+    const chatBox = document.getElementById("chatBox");
 
-# messages -> string
-prompt = encode_messages(messages, thinking_mode="thinking")
+    const message = document.createElement("div");
+    message.classList.add("message", sender);
+    message.innerText = text;
 
-# string -> tokens
-import transformers
-tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V4-Pro")
-tokens = tokenizer.encode(prompt)
-```
+    chatBox.appendChild(message);
+    chatBox.scrollTop = chatBox.scrollHeight;
+  }
 
-## How to Run Locally
+  function botReply(message) {
+    message = message.toLowerCase();
 
-Please refer to the [inference](inference/README.md) folder for detailed instructions on running DeepSeek-V4 locally, including model weight conversion and interactive chat demos.
+    if (message.includes("halo")) {
+      return "Halo juga 👋";
+    } 
+    else if (message.includes("nama")) {
+      return "Saya chatbot sederhana.";
+    } 
+    else if (message.includes("apa kabar")) {
+      return "Saya baik 😊";
+    } 
+    else {
+      return "Maaf, saya belum mengerti.";
+    }
+  }
 
-For local deployment, we recommend setting the sampling parameters to `temperature = 1.0, top_p = 1.0`. For the Think Max reasoning mode, we recommend setting the context window to at least **384K** tokens.
+  function sendMessage() {
+    const input = document.getElementById("userInput");
+    const text = input.value.trim();
 
-## License
+    if (text === "") return;
 
-This repository and the model weights are licensed under the [MIT License](LICENSE).
+    addMessage(text, "user");
 
-## Citation
+    const reply = botReply(text);
 
-```
-@misc{deepseekai2026deepseekv4,
-      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
-      author={DeepSeek-AI},
-      year={2026},
-}
-```
+    setTimeout(() => {
+      addMessage(reply, "bot");
+    }, 500);
 
-## Contact
+    input.value = "";
+  }
+</script>
 
-If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
+</body>
+</html>