Release DeepSeek-V4

.gitattributes

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+*.png filter=lfs diff=lfs merge=lfs -text

DeepSeek_V4.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6d62e4a2bfd00049abc23a8e025eeccad68e8436cbb0515e0bb6e432b1883fdc
+size 4476057

LICENSE

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+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.

README.md

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+---
+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>
+  <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>
+</div>
+<div align="center" style="line-height: 1;">
+  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
+    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&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="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** |
+
+</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) 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=?"}
+]
+
+# messages -> string
+prompt = encode_messages(messages, thinking_mode="thinking")
+
+# string -> tokens
+import transformers
+tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V4-Pro")
+tokens = tokenizer.encode(prompt)
+```
+
+## How to Run Locally
+
+Please refer to the [inference](inference) folder for detailed instructions on running DeepSeek-V4 locally, including model weight conversion and interactive chat demos.
+
+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.
+
+## License
+
+This repository and the model weights are licensed under the [MIT License](LICENSE).
+
+## Citation
+
+```
+@misc{deepseekai2026deepseekv4,
+      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
+      author={DeepSeek-AI},
+      year={2026},
+}
+```
+
+## Contact
+
+If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).

encoding/README.md

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+# DeepSeek-V4 Encoding
+
+This document describes the prompt encoding format used by DeepSeek-V4 series models. The encoding handles multi-turn conversations, tool calling, extended thinking (reasoning), and quick instruction tasks.
+
+A self-contained reference implementation is provided in `encoding_dsv4.py`.
+
+## Quick Start
+
+```python
+from encoding_dsv4 import encode_messages, parse_message_from_completion_text
+
+# Encode a conversation
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What is 2+2?"},
+]
+prompt = encode_messages(messages, thinking_mode="thinking")
+# => "<｜begin▁of▁sentence｜>You are a helpful assistant.<｜User｜>What is 2+2?<｜Assistant｜><think>"
+
+# Parse model output back to structured message
+completion = "Simple arithmetic.</think>2 + 2 = 4.<｜end▁of▁sentence｜>"
+parsed = parse_message_from_completion_text(completion, thinking_mode="thinking")
+# => {"role": "assistant", "reasoning_content": "Simple arithmetic.", "content": "2 + 2 = 4.", "tool_calls": []}
+```
+
+> **Note:** The `parse_message_from_completion_text` function is designed to handle well-formatted model output only. It does not attempt to correct or recover from malformed output that the model might occasionally generate. For production use, additional error handling is recommended.
+
+## Message Format
+
+### Special Tokens
+
+| Token | Purpose |
+|-------|---------|
+| `<｜begin▁of▁sentence｜>` | Beginning of sequence (BOS) |
+| `<｜end▁of▁sentence｜>` | End of assistant turn (EOS) |
+| `<｜User｜>` | User turn prefix |
+| `<｜Assistant｜>` | Assistant turn prefix |
+| `<｜latest_reminder｜>` | Latest reminder (date, locale, etc.) |
+| `<think>` / `</think>` | Reasoning block delimiters |
+| `｜DSML｜` | DSML markup token |
+
+### Roles
+
+The encoding supports the following message roles: `system`, `user`, `assistant`, `tool`, `latest_reminder`, and `developer`.
+
+> **Note on the `developer` role:** The `developer` role is used exclusively in the internal search agent pipeline. It is not needed for general-purpose chat or tool-calling tasks, and the official API does not accept messages with this role.
+
+### Basic Chat
+
+A simple multi-turn conversation is encoded as:
+
+```
+<｜begin▁of▁sentence｜>{system_prompt}
+<｜User｜>{user_message}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>
+<｜User｜>{user_message_2}<｜Assistant｜></think>{response_2}<｜end▁of▁sentence｜>
+```
+
+- The BOS token is prepended at the very beginning of the conversation.
+- In **chat mode** (`thinking_mode="chat"`), `</think>` is placed right after `<｜Assistant｜>` to immediately close the thinking block, so the model generates content directly.
+
+### Interleaved Thinking Mode
+
+In **thinking mode** (`thinking_mode="thinking"`), the model produces explicit reasoning inside `<think>...</think>` blocks before responding.
+
+```
+<｜begin▁of▁sentence｜>{system_prompt}
+<｜User｜>{message}<｜Assistant｜><think>{reasoning}</think>{response}<｜end▁of▁sentence｜>
+```
+
+The `drop_thinking` parameter (default `True`) controls whether reasoning from earlier turns is preserved:
+
+- **Without tools**: `drop_thinking` takes effect. Reasoning content from assistant turns **before** the last user message is stripped. Only the final assistant turn retains its `<think>...</think>` block.
+- **With tools** (on system or developer message): `drop_thinking` is automatically disabled. All turns retain their reasoning, because tool-calling conversations require full context for the model to track multi-step reasoning across tool calls.
+
+### Tool Calling (DSML Format)
+
+Tools are defined on the `system` or `developer` message via the `tools` field (OpenAI-compatible format). When tools are present, the following schema block is injected into the system/user prompt:
+
+```
+## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<｜DSML｜tool_calls>" block like the following:
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="$TOOL_NAME">
+<｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter>
+...
+</｜DSML｜invoke>
+<｜DSML｜invoke name="$TOOL_NAME2">
+...
+</｜DSML｜invoke>
+</｜DSML｜tool_calls>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.
+
+Otherwise, output directly after </think> with tool calls or final response.
+
+### Available Tool Schemas
+
+{tool_definitions_json}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+```
+
+An actual tool call in the assistant turn looks like:
+
+```xml
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="function_name">
+<｜DSML｜parameter name="param" string="true">string_value</｜DSML｜parameter>
+<｜DSML｜parameter name="count" string="false">5</｜DSML｜parameter>
+</｜DSML｜invoke>
+</｜DSML｜tool_calls><｜end▁of▁sentence｜>
+```
+
+- `string="true"`: the parameter value is a raw string.
+- `string="false"`: the parameter value is JSON (number, boolean, array, object).
+
+Tool execution results are wrapped in `<tool_result>` tags within user messages:
+
+```
+<｜User｜><tool_result>{result_json}</tool_result><｜Assistant｜><think>...
+```
+
+When multiple tool results are present, they are sorted by the order of the corresponding `tool_calls` in the preceding assistant message.
+
+### Reasoning Effort
+
+When `reasoning_effort="max"` is set, a special prefix is prepended at the very beginning of the prompt (before the system message) to instruct the model to maximize its reasoning depth:
+
+```
+Reasoning Effort: Absolute maximum with no shortcuts permitted.
+You MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.
+Explicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.
+```
+
+### Quick Instruction Special Tokens
+
+Quick instruction tokens are used for auxiliary classification and generation tasks. They are appended to messages via the `"task"` field to trigger specialized model behavior for a single-token or short-form output.
+
+| Special Token | Description | Format |
+|:---|:---|:---|
+| `<｜action｜>` | Determines whether the user prompt requires a web search or can be answered directly. | `...<｜User｜>{prompt}<｜Assistant｜><think><｜action｜>` |
+| `<｜title｜>` | Generates a concise conversation title after the first assistant response. | `...<｜Assistant｜>{response}<｜end▁of▁sentence｜><｜title｜>` |
+| `<｜query｜>` | Generates search queries for the user prompt. | `...<｜User｜>{prompt}<｜query｜>` |
+| `<｜authority｜>` | Classifies the user prompt's demand for source authoritativeness. | `...<｜User｜>{prompt}<｜authority｜>` |
+| `<｜domain｜>` | Identifies the domain of the user prompt. | `...<｜User｜>{prompt}<｜domain｜>` |
+| `<｜extracted_url｜>` `<｜read_url｜>` | Determines whether each URL in the user prompt should be fetched and read. | `...<｜User｜>{prompt}<｜extracted_url｜>{url}<｜read_url｜>` |
+
+Usage in message format:
+
+- **`action`** on a user message: the `<｜action｜>` token is placed after the assistant prefix and thinking token, triggering a routing decision (e.g., "Search" or "Answer").
+- **Other tasks** (`query`, `authority`, `domain`, `read_url`) on a user message: the task token is appended directly after the user content.
+- **`title`** on an assistant message: the `<｜title｜>` token is appended after the assistant's EOS. The next assistant message provides the generated title.

encoding/encoding_dsv4.py

@@ -0,0 +1,744 @@
+"""
+DeepSeek-V4 Encoding
+
+A self-contained implementation for encoding/decoding DeepSeek-V4 chat messages
+with tool calling, thinking mode, and quick instruction task support.
+"""
+
+from typing import Any, Dict, List, Union, Optional, Tuple
+import copy
+import json
+import re
+
+# ============================================================
+# Special Tokens
+# ============================================================
+
+bos_token: str = "<｜begin▁of▁sentence｜>"
+eos_token: str = "<｜end▁of▁sentence｜>"
+thinking_start_token: str = "<think>"
+thinking_end_token: str = "</think>"
+dsml_token: str = "｜DSML｜"
+
+USER_SP_TOKEN = "<｜User｜>"
+ASSISTANT_SP_TOKEN = "<｜Assistant｜>"
+LATEST_REMINDER_SP_TOKEN = "<｜latest_reminder｜>"
+
+# Task special tokens for internal classification tasks
+DS_TASK_SP_TOKENS = {
+    "action": "<｜action｜>",
+    "query": "<｜query｜>",
+    "authority": "<｜authority｜>",
+    "domain": "<｜domain｜>",
+    "title": "<｜title｜>",
+    "read_url": "<｜read_url｜>",
+}
+VALID_TASKS = set(DS_TASK_SP_TOKENS.keys())
+
+# ============================================================
+# Templates
+# ============================================================
+
+system_msg_template: str = "{content}"
+user_msg_template: str = "{content}"
+latest_reminder_msg_template: str = "{content}"
+assistant_msg_template: str = "{reasoning}{content}{tool_calls}" + eos_token
+assistant_msg_wo_eos_template: str = "{reasoning}{content}{tool_calls}"
+thinking_template: str = "{reasoning_content}"
+
+response_format_template: str = (
+    "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{schema}"
+)
+tool_call_template: str = (
+    "<{dsml_token}invoke name=\"{name}\">\n{arguments}\n</{dsml_token}invoke>"
+)
+tool_calls_template = (
+    "<{dsml_token}{tc_block_name}>\n{tool_calls}\n</{dsml_token}{tc_block_name}>"
+)
+tool_calls_block_name: str = "tool_calls"
+
+tool_output_template: str = (
+    "<tool_result>{content}</tool_result>"
+)
+
+REASONING_EFFORT_MAX = (
+    "Reasoning Effort: Absolute maximum with no shortcuts permitted.\n"
+    "You MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.\n"
+    "Explicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.\n\n"
+)
+
+TOOLS_TEMPLATE = """## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<{dsml_token}tool_calls>" block like the following:
+
+<{dsml_token}tool_calls>
+<{dsml_token}invoke name="$TOOL_NAME">
+<{dsml_token}parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</{dsml_token}parameter>
+...
+</{dsml_token}invoke>
+<{dsml_token}invoke name="$TOOL_NAME2">
+...
+</{dsml_token}invoke>
+</{dsml_token}tool_calls>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by {thinking_start_token}), you MUST output your complete reasoning inside {thinking_start_token}...{thinking_end_token} BEFORE any tool calls or final response.
+
+Otherwise, output directly after {thinking_end_token} with tool calls or final response.
+
+### Available Tool Schemas
+
+{tool_schemas}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+"""
+
+# ============================================================
+# Utility Functions
+# ============================================================
+
+def to_json(value: Any) -> str:
+    """Serialize a value to JSON string."""
+    try:
+        return json.dumps(value, ensure_ascii=False)
+    except:
+        return json.dumps(value, ensure_ascii=True)
+
+
+def tools_from_openai_format(tools):
+    """Extract function definitions from OpenAI-format tool list."""
+    return [tool["function"] for tool in tools]
+
+
+def tool_calls_from_openai_format(tool_calls):
+    """Convert OpenAI-format tool calls to internal format."""
+    return [
+        {
+            "name": tool_call["function"]["name"],
+            "arguments": tool_call["function"]["arguments"],
+        }
+        for tool_call in tool_calls
+    ]
+
+
+def tool_calls_to_openai_format(tool_calls):
+    """Convert internal tool calls to OpenAI format."""
+    return [
+        {
+            "type": "function",
+            "function": {
+                "name": tool_call["name"],
+                "arguments": tool_call["arguments"],
+            }
+        }
+        for tool_call in tool_calls
+    ]
+
+
+def encode_arguments_to_dsml(tool_call: Dict[str, str]) -> str:
+    """
+    Encode tool call arguments into DSML parameter format.
+
+    Args:
+        tool_call: Dict with "name" and "arguments" (JSON string) keys.
+
+    Returns:
+        DSML-formatted parameter string.
+    """
+    p_dsml_template = '<{dsml_token}parameter name="{key}" string="{is_str}">{value}</{dsml_token}parameter>'
+    P_dsml_strs = []
+
+    try:
+        arguments = json.loads(tool_call["arguments"])
+    except Exception as err:
+        arguments = {"arguments": tool_call["arguments"]}
+
+    for k, v in arguments.items():
+        p_dsml_str = p_dsml_template.format(
+            dsml_token=dsml_token,
+            key=k,
+            is_str="true" if isinstance(v, str) else "false",
+            value=v if isinstance(v, str) else to_json(v),
+        )
+        P_dsml_strs.append(p_dsml_str)
+
+    return "\n".join(P_dsml_strs)
+
+
+def decode_dsml_to_arguments(tool_name: str, tool_args: Dict[str, Tuple[str, str]]) -> Dict[str, str]:
+    """
+    Decode DSML parameters back to a tool call dict.
+
+    Args:
+        tool_name: Name of the tool.
+        tool_args: Dict mapping param_name -> (value, is_string_flag).
+
+    Returns:
+        Dict with "name" and "arguments" (JSON string) keys.
+    """
+    def _decode_value(key: str, value: str, string: str):
+        if string == "true":
+            value = to_json(value)
+        return f"{to_json(key)}: {value}"
+
+    tool_args_json = "{" + ", ".join([_decode_value(k, v, string=is_str) for k, (v, is_str) in tool_args.items()]) + "}"
+    return dict(name=tool_name, arguments=tool_args_json)
+
+
+def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str:
+    """
+    Render tool schemas into the system prompt format.
+
+    Args:
+        tools: List of tool schema dicts (each with name, description, parameters).
+
+    Returns:
+        Formatted tools section string.
+    """
+    tools_json = [to_json(t) for t in tools]
+
+    return TOOLS_TEMPLATE.format(
+        tool_schemas="\n".join(tools_json),
+        dsml_token=dsml_token,
+        thinking_start_token=thinking_start_token,
+        thinking_end_token=thinking_end_token,
+    )
+
+
+def find_last_user_index(messages: List[Dict[str, Any]]) -> int:
+    """Find the index of the last user/developer message."""
+    last_user_index = -1
+    for idx in range(len(messages) - 1, -1, -1):
+        if messages[idx].get("role") in ["user", "developer"]:
+            last_user_index = idx
+            break
+    return last_user_index
+
+
+# ============================================================
+# Message Rendering
+# ============================================================
+
+def render_message(index: int, messages: List[Dict[str, Any]], thinking_mode: str, drop_thinking: bool = True, reasoning_effort: Optional[str] = None) -> str:
+    """
+    Render a single message at the given index into its encoded string form.
+
+    This is the core function that converts each message in the conversation
+    into the DeepSeek-V4 format.
+
+    Args:
+        index: Index of the message to render.
+        messages: Full list of messages in the conversation.
+        thinking_mode: Either "chat" or "thinking".
+        drop_thinking: Whether to drop reasoning content from earlier turns.
+        reasoning_effort: Optional reasoning effort level ("max", "high", or None).
+
+    Returns:
+        Encoded string for this message.
+    """
+    assert 0 <= index < len(messages)
+    assert thinking_mode in ["chat", "thinking"], f"Invalid thinking_mode `{thinking_mode}`"
+
+    prompt = ""
+    msg = messages[index]
+    last_user_idx = find_last_user_index(messages)
+
+    role = msg.get("role")
+    content = msg.get("content")
+    tools = msg.get("tools")
+    response_format = msg.get("response_format")
+    tool_calls = msg.get("tool_calls")
+    reasoning_content = msg.get("reasoning_content")
+    wo_eos = msg.get("wo_eos", False)
+
+    if tools:
+        tools = tools_from_openai_format(tools)
+    if tool_calls:
+        tool_calls = tool_calls_from_openai_format(tool_calls)
+
+    # Reasoning effort prefix (only at index 0 in thinking mode with max effort)
+    assert reasoning_effort in ['max', None, 'high'], f"Invalid reasoning effort: {reasoning_effort}"
+    if index == 0 and thinking_mode == "thinking" and reasoning_effort == 'max':
+        prompt += REASONING_EFFORT_MAX
+
+    if role == "system":
+        prompt += system_msg_template.format(content=content or "")
+        if tools:
+            prompt += "\n\n" + render_tools(tools)
+        if response_format:
+            prompt += "\n\n" + response_format_template.format(schema=to_json(response_format))
+
+    elif role == "developer":
+        assert content, f"Invalid message for role `{role}`: {msg}"
+
+        content_developer = USER_SP_TOKEN
+        content_developer += content
+
+        if tools:
+            content_developer += "\n\n" + render_tools(tools)
+        if response_format:
+            content_developer += "\n\n" + response_format_template.format(schema=to_json(response_format))
+
+        prompt += user_msg_template.format(content=content_developer)
+
+    elif role == "user":
+        prompt += USER_SP_TOKEN
+
+        # Handle content blocks (tool results mixed with text)
+        content_blocks = msg.get("content_blocks")
+        if content_blocks:
+            parts = []
+            for block in content_blocks:
+                block_type = block.get("type")
+                if block_type == "text":
+                    parts.append(block.get("text", ""))
+                elif block_type == "tool_result":
+                    tool_content = block.get("content", "")
+                    if isinstance(tool_content, list):
+                        text_parts = []
+                        for b in tool_content:
+                            if b.get("type") == "text":
+                                text_parts.append(b.get("text", ""))
+                            else:
+                                text_parts.append(f"[Unsupported {b.get('type')}]")
+                        tool_content = "\n\n".join(text_parts)
+                    parts.append(tool_output_template.format(content=tool_content))
+                else:
+                    parts.append(f"[Unsupported {block_type}]")
+            prompt += "\n\n".join(parts)
+        else:
+            prompt += content or ""
+
+    elif role == "latest_reminder":
+        prompt += LATEST_REMINDER_SP_TOKEN + latest_reminder_msg_template.format(content=content)
+
+    elif role == "tool":
+        raise NotImplementedError("deepseek_v4 merges tool messages into user; please preprocess with merge_tool_messages()")
+
+    elif role == "assistant":
+        thinking_part = ""
+        tc_content = ""
+
+        if tool_calls:
+            tc_list = [
+                tool_call_template.format(
+                    dsml_token=dsml_token,
+                    name=tc.get("name"),
+                    arguments=encode_arguments_to_dsml(tc)
+                )
+                for tc in tool_calls
+            ]
+            tc_content += '\n\n' + tool_calls_template.format(
+                dsml_token=dsml_token,
+                tool_calls="\n".join(tc_list),
+                tc_block_name=tool_calls_block_name,
+            )
+
+        summary_content = content or ""
+        rc = reasoning_content or ""
+
+        # Check if previous message has a task - if so, this is a task output (no thinking)
+        prev_has_task = index - 1 >= 0 and messages[index - 1].get("task") is not None
+
+        if thinking_mode == "thinking" and not prev_has_task:
+            if not drop_thinking or index > last_user_idx:
+                thinking_part = thinking_template.format(reasoning_content=rc) + thinking_end_token
+            else:
+                thinking_part = ""
+
+        if wo_eos:
+            prompt += assistant_msg_wo_eos_template.format(
+                reasoning=thinking_part,
+                content=summary_content,
+                tool_calls=tc_content,
+            )
+        else:
+            prompt += assistant_msg_template.format(
+                reasoning=thinking_part,
+                content=summary_content,
+                tool_calls=tc_content,
+            )
+    else:
+        raise NotImplementedError(f"Unknown role: {role}")
+
+    # Append transition tokens based on what follows
+    if index + 1 < len(messages) and messages[index + 1].get("role") not in ["assistant", "latest_reminder"]:
+        return prompt
+
+    task = messages[index].get("task")
+    if task is not None:
+        # Task special token for internal classification tasks
+        assert task in VALID_TASKS, f"Invalid task: '{task}'. Valid tasks are: {list(VALID_TASKS)}"
+        task_sp_token = DS_TASK_SP_TOKENS[task]
+
+        if task != "action":
+            # Non-action tasks: append task sp token directly after the message
+            prompt += task_sp_token
+        else:
+            # Action task: append Assistant + thinking token + action sp token
+            prompt += ASSISTANT_SP_TOKEN
+            prompt += thinking_end_token if thinking_mode != "thinking" else thinking_start_token
+            prompt += task_sp_token
+
+    elif messages[index].get("role") in ["user", "developer"]:
+        # Normal generation: append Assistant + thinking token
+        prompt += ASSISTANT_SP_TOKEN
+        if not drop_thinking and thinking_mode == "thinking":
+            prompt += thinking_start_token
+        elif drop_thinking and thinking_mode == "thinking" and index >= last_user_idx:
+            prompt += thinking_start_token
+        else:
+            prompt += thinking_end_token
+
+    return prompt
+
+
+# ============================================================
+# Preprocessing
+# ============================================================
+
+def merge_tool_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Merge tool messages into the preceding user message using content_blocks format.
+
+    DeepSeek-V4 does not have a standalone "tool" role; instead, tool results
+    are encoded as <tool_result> blocks within user messages.
+
+    This function converts a standard OpenAI-format conversation (with separate
+    "tool" role messages) into V4 format where tool results are merged into
+    user messages.
+
+    Args:
+        messages: List of message dicts in OpenAI format.
+
+    Returns:
+        Processed message list with tool messages merged into user messages.
+    """
+    merged: List[Dict[str, Any]] = []
+
+    for msg in messages:
+        msg = copy.deepcopy(msg)
+        role = msg.get("role")
+
+        if role == "tool":
+            # Convert tool message to a user message with tool_result block
+            tool_block = {
+                "type": "tool_result",
+                "tool_use_id": msg.get("tool_call_id", ""),
+                "content": msg.get("content", ""),
+            }
+            # Merge into previous message if it's already a user (merged tool)
+            if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1]:
+                merged[-1]["content_blocks"].append(tool_block)
+            else:
+                merged.append({
+                    "role": "user",
+                    "content_blocks": [tool_block],
+                })
+        elif role == "user":
+            text_block = {"type": "text", "text": msg.get("content", "")}
+            if merged and merged[-1].get("role") == "user" and "content_blocks" in merged[-1] and merged[-1].get("task") is None:
+                merged[-1]["content_blocks"].append(text_block)
+            else:
+                new_msg = {
+                    "role": "user",
+                    "content": msg.get("content", ""),
+                    "content_blocks": [text_block],
+                }
+                # Preserve extra fields (task, wo_eos, mask, etc.)
+                for key in ("task", "wo_eos", "mask"):
+                    if key in msg:
+                        new_msg[key] = msg[key]
+                merged.append(new_msg)
+        else:
+            merged.append(msg)
+
+    return merged
+
+
+def sort_tool_results_by_call_order(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Sort tool_result blocks within user messages by the order of tool_calls
+    in the preceding assistant message.
+
+    Args:
+        messages: Preprocessed message list (after merge_tool_messages).
+
+    Returns:
+        Message list with sorted tool result blocks.
+    """
+    last_tool_call_order: Dict[str, int] = {}
+
+    for msg in messages:
+        role = msg.get("role")
+        if role == "assistant" and msg.get("tool_calls"):
+            last_tool_call_order = {}
+            for idx, tc in enumerate(msg["tool_calls"]):
+                tc_id = tc.get("id") or tc.get("function", {}).get("id", "")
+                if tc_id:
+                    last_tool_call_order[tc_id] = idx
+
+        elif role == "user" and msg.get("content_blocks"):
+            tool_blocks = [b for b in msg["content_blocks"] if b.get("type") == "tool_result"]
+            if len(tool_blocks) > 1 and last_tool_call_order:
+                sorted_blocks = sorted(
+                    tool_blocks,
+                    key=lambda b: last_tool_call_order.get(b.get("tool_use_id", ""), 0)
+                )
+                sorted_idx = 0
+                new_blocks = []
+                for block in msg["content_blocks"]:
+                    if block.get("type") == "tool_result":
+                        new_blocks.append(sorted_blocks[sorted_idx])
+                        sorted_idx += 1
+                    else:
+                        new_blocks.append(block)
+                msg["content_blocks"] = new_blocks
+
+    return messages
+
+
+# ============================================================
+# Main Encoding Function
+# ============================================================
+
+def encode_messages(
+    messages: List[Dict[str, Any]],
+    thinking_mode: str,
+    context: Optional[List[Dict[str, Any]]] = None,
+    drop_thinking: bool = True,
+    add_default_bos_token: bool = True,
+    reasoning_effort: Optional[str] = None,
+) -> str:
+    """
+    Encode a list of messages into the DeepSeek-V4 prompt format.
+
+    This is the main entry point for encoding conversations. It handles:
+    - BOS token insertion
+    - Thinking mode with optional reasoning content dropping
+    - Tool message merging into user messages
+    - Multi-turn conversation context
+
+    Args:
+        messages: List of message dicts to encode.
+        thinking_mode: Either "chat" or "thinking".
+        context: Optional preceding context messages (already encoded prefix).
+        drop_thinking: If True, drop reasoning_content from earlier assistant turns
+                      (only keep reasoning for messages after the last user message).
+        add_default_bos_token: Whether to prepend BOS token at conversation start.
+        reasoning_effort: Optional reasoning effort level ("max", "high", or None).
+
+    Returns:
+        The encoded prompt string.
+    """
+    context = context if context else []
+
+    # Preprocess: merge tool messages and sort tool results
+    messages = merge_tool_messages(messages)
+    messages = sort_tool_results_by_call_order(context + messages)[len(context):]
+    if context:
+        context = merge_tool_messages(context)
+        context = sort_tool_results_by_call_order(context)
+
+    full_messages = context + messages
+
+    prompt = bos_token if add_default_bos_token and len(context) == 0 else ""
+
+    # Resolve drop_thinking: if any message has tools defined, don't drop thinking
+    effective_drop_thinking = drop_thinking
+    if any(m.get("tools") for m in full_messages):
+        effective_drop_thinking = False
+
+    if thinking_mode == "thinking" and effective_drop_thinking:
+        full_messages = _drop_thinking_messages(full_messages)
+        # After dropping, recalculate how many messages to render
+        # (context may have shrunk too)
+        num_to_render = len(full_messages) - len(_drop_thinking_messages(context))
+        context_len = len(full_messages) - num_to_render
+    else:
+        num_to_render = len(messages)
+        context_len = len(context)
+
+    for idx in range(num_to_render):
+        prompt += render_message(
+            idx + context_len,
+            full_messages,
+            thinking_mode=thinking_mode,
+            drop_thinking=effective_drop_thinking,
+            reasoning_effort=reasoning_effort,
+        )
+
+    return prompt
+
+
+def _drop_thinking_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Drop reasoning_content and non-essential messages before the last user message.
+
+    Behavior:
+    - Messages with role in ["user", "system", "tool", "latest_reminder"] are always kept.
+    - Messages at or after the last user index are always kept.
+    - Assistant messages before the last user get reasoning_content removed.
+    - Developer messages before the last user are dropped entirely.
+    """
+    last_user_idx = find_last_user_index(messages)
+    result = []
+    keep_roles = {"user", "system", "tool", "latest_reminder", "direct_search_results"}
+
+    for idx, msg in enumerate(messages):
+        role = msg.get("role")
+        if role in keep_roles or idx >= last_user_idx:
+            result.append(msg)
+        elif role == "assistant":
+            msg = copy.copy(msg)
+            msg.pop("reasoning_content", None)
+            result.append(msg)
+        # developer and other roles before last_user_idx are dropped
+
+    return result
+
+
+# ============================================================
+# Parsing (Decoding model output)
+# ============================================================
+
+def _read_until_stop(index: int, text: str, stop: List[str]) -> Tuple[int, str, Optional[str]]:
+    """
+    Read text from index until one of the stop strings is found.
+
+    Returns:
+        Tuple of (new_index, content_before_stop, matched_stop_string_or_None).
+    """
+    min_pos = len(text)
+    matched_stop = None
+
+    for s in stop:
+        pos = text.find(s, index)
+        if pos != -1 and pos < min_pos:
+            min_pos = pos
+            matched_stop = s
+
+    if matched_stop:
+        content = text[index:min_pos]
+        return min_pos + len(matched_stop), content, matched_stop
+    else:
+        content = text[index:]
+        return len(text), content, None
+
+
+def parse_tool_calls(index: int, text: str) -> Tuple[int, Optional[str], List[Dict[str, str]]]:
+    """
+    Parse DSML tool calls from text starting at the given index.
+
+    Args:
+        index: Starting position in text.
+        text: The full text to parse.
+
+    Returns:
+        Tuple of (new_index, last_stop_token, list_of_tool_call_dicts).
+        Each tool call dict has "name" and "arguments" keys.
+    """
+    tool_calls: List[Dict[str, Any]] = []
+    stop_token = None
+    tool_calls_end_token = f"</{dsml_token}{tool_calls_block_name}>"
+
+    while index < len(text):
+        index, _, stop_token = _read_until_stop(index, text, [f"<{dsml_token}invoke", tool_calls_end_token])
+        if _ != ">\n":
+            raise ValueError(f"Tool call format error: expected '>\\n' but got '{_}'")
+
+        if stop_token == tool_calls_end_token:
+            break
+
+        if stop_token is None:
+            raise ValueError("Missing special token in tool calls")
+
+        index, tool_name_content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
+
+        p_tool_name = re.findall(r'^\s*name="(.*?)">\n$', tool_name_content, flags=re.DOTALL)
+        if len(p_tool_name) != 1:
+            raise ValueError(f"Tool name format error: '{tool_name_content}'")
+        tool_name = p_tool_name[0]
+
+        tool_args: Dict[str, Tuple[str, str]] = {}
+        while stop_token == f"<{dsml_token}parameter":
+            index, param_content, stop_token = _read_until_stop(index, text, [f"/{dsml_token}parameter"])
+
+            param_kv = re.findall(r'^ name="(.*?)" string="(true|false)">(.*?)<$', param_content, flags=re.DOTALL)
+            if len(param_kv) != 1:
+                raise ValueError(f"Parameter format error: '{param_content}'")
+            param_name, string, param_value = param_kv[0]
+
+            if param_name in tool_args:
+                raise ValueError(f"Duplicate parameter name: '{param_name}'")
+            tool_args[param_name] = (param_value, string)
+
+            index, content, stop_token = _read_until_stop(index, text, [f"<{dsml_token}parameter", f"</{dsml_token}invoke"])
+            if content != ">\n":
+                raise ValueError(f"Parameter format error: expected '>\\n' but got '{content}'")
+
+        tool_call = decode_dsml_to_arguments(tool_name=tool_name, tool_args=tool_args)
+        tool_calls.append(tool_call)
+
+    return index, stop_token, tool_calls
+
+
+def parse_message_from_completion_text(text: str, thinking_mode: str) -> Dict[str, Any]:
+    """
+    Parse a model completion text into a structured assistant message.
+
+    This function takes the raw text output from the model (a single assistant turn)
+    and extracts:
+    - reasoning_content (thinking block)
+    - content (summary/response)
+    - tool_calls (if any)
+
+    NOTE: This function is designed to parse only correctly formatted strings and
+    will raise ValueError for malformed output.
+
+    Args:
+        text: The raw completion text (including EOS token).
+        thinking_mode: Either "chat" or "thinking".
+
+    Returns:
+        Dict with keys: "role", "content", "reasoning_content", "tool_calls".
+        tool_calls are in OpenAI format.
+    """
+    summary_content, reasoning_content, tool_calls = "", "", []
+    index, stop_token = 0, None
+    tool_calls_start_token = f"\n\n<{dsml_token}{tool_calls_block_name}"
+
+    is_thinking = thinking_mode == "thinking"
+    is_tool_calling = False
+
+    if is_thinking:
+        index, content_delta, stop_token = _read_until_stop(index, text, [thinking_end_token, tool_calls_start_token])
+        reasoning_content = content_delta
+        assert stop_token == thinking_end_token, "Invalid thinking format: missing </think>"
+
+    index, content_delta, stop_token = _read_until_stop(index, text, [eos_token, tool_calls_start_token])
+    summary_content = content_delta
+    if stop_token == tool_calls_start_token:
+        is_tool_calling = True
+    else:
+        assert stop_token == eos_token, "Invalid format: missing EOS token"
+
+    if is_tool_calling:
+        index, stop_token, tool_calls = parse_tool_calls(index, text)
+
+        index, tool_ends_text, stop_token = _read_until_stop(index, text, [eos_token])
+        assert not tool_ends_text, "Unexpected content after tool calls"
+
+    assert len(text) == index and stop_token in [eos_token, None], "Unexpected content at end"
+
+    for sp_token in [bos_token, eos_token, thinking_start_token, thinking_end_token, dsml_token]:
+        assert sp_token not in summary_content and sp_token not in reasoning_content, \
+            f"Unexpected special token '{sp_token}' in content"
+
+    return {
+        "role": "assistant",
+        "content": summary_content,
+        "reasoning_content": reasoning_content,
+        "tool_calls": tool_calls_to_openai_format(tool_calls)
+    }

encoding/test_encoding_dsv4.py

@@ -0,0 +1,89 @@
+"""
+Test suite for DeepSeek-V4 Encoding.
+
+Run: python test_encoding_dsv4.py
+"""
+
+import json
+import os
+
+from encoding_dsv4 import encode_messages, parse_message_from_completion_text
+
+TESTS_DIR = os.path.join(os.path.dirname(__file__), "tests")
+
+
+def test_case_1():
+    """Thinking mode with tool calls (multi-turn, tool results merged into user)."""
+    with open(os.path.join(TESTS_DIR, "test_input_1.json")) as f:
+        td = json.load(f)
+        messages = td["messages"]
+        messages[0]["tools"] = td["tools"]
+    gold = open(os.path.join(TESTS_DIR, "test_output_1.txt")).read()
+    prompt = encode_messages(messages, thinking_mode="thinking")
+    assert prompt == gold
+
+    # Parse: assistant turn with tool call
+    marker = "<｜Assistant｜><think>"
+    first_start = prompt.find(marker) + len(marker)
+    first_end = prompt.find("<｜User｜>", first_start)
+    parsed_tc = parse_message_from_completion_text(prompt[first_start:first_end], thinking_mode="thinking")
+    assert parsed_tc["reasoning_content"] == "The user wants to know the weather in Beijing. I should use the get_weather tool."
+    assert parsed_tc["content"] == ""
+    assert len(parsed_tc["tool_calls"]) == 1
+    assert parsed_tc["tool_calls"][0]["function"]["name"] == "get_weather"
+    assert json.loads(parsed_tc["tool_calls"][0]["function"]["arguments"]) == {"location": "Beijing", "unit": "celsius"}
+
+    # Parse: final assistant turn with content
+    last_start = prompt.rfind(marker) + len(marker)
+    parsed_final = parse_message_from_completion_text(prompt[last_start:], thinking_mode="thinking")
+    assert parsed_final["reasoning_content"] == "Got the weather data. Let me format a nice response."
+    assert "22°C" in parsed_final["content"]
+    assert parsed_final["tool_calls"] == []
+
+    print("  [PASS] case 1: thinking with tools (encode + parse)")
+
+
+def test_case_2():
+    """Thinking mode without tools (drop_thinking removes earlier reasoning)."""
+    messages = json.load(open(os.path.join(TESTS_DIR, "test_input_2.json")))
+    gold = open(os.path.join(TESTS_DIR, "test_output_2.txt")).read()
+    prompt = encode_messages(messages, thinking_mode="thinking")
+    assert prompt == gold
+
+    # Parse: last assistant turn
+    marker = "<｜Assistant｜><think>"
+    last_start = prompt.rfind(marker) + len(marker)
+    parsed = parse_message_from_completion_text(prompt[last_start:], thinking_mode="thinking")
+    assert parsed["reasoning_content"] == "The user asks about the capital of France. It is Paris."
+    assert parsed["content"] == "The capital of France is Paris."
+    assert parsed["tool_calls"] == []
+
+    # Verify drop_thinking: first assistant's reasoning should be absent
+    assert "The user said hello" not in prompt
+
+    print("  [PASS] case 2: thinking without tools (encode + parse)")
+
+
+def test_case_3():
+    """Interleaved thinking + search (developer with tools, latest_reminder)."""
+    messages = json.load(open(os.path.join(TESTS_DIR, "test_input_3.json")))
+    gold = open(os.path.join(TESTS_DIR, "test_output_3.txt")).read()
+    assert encode_messages(messages, thinking_mode="thinking") == gold
+    print("  [PASS] case 3: interleaved thinking + search")
+
+
+def test_case_4():
+    """Quick instruction task with latest_reminder (chat mode, action task)."""
+    messages = json.load(open(os.path.join(TESTS_DIR, "test_input_4.json")))
+    gold = open(os.path.join(TESTS_DIR, "test_output_4.txt")).read()
+    assert encode_messages(messages, thinking_mode="chat") == gold
+    print("  [PASS] case 4: quick instruction task")
+
+
+if __name__ == "__main__":
+    print("Running DeepSeek-V4 Encoding Tests...\n")
+    test_case_1()
+    test_case_2()
+    test_case_3()
+    test_case_4()
+    print("\nAll 4 tests passed!")

encoding/tests/test_input_1.json

@@ -0,0 +1,81 @@
+{
+    "tools": [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get the weather for a specific location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city name"
+                        },
+                        "unit": {
+                            "type": "string",
+                            "enum": ["celsius", "fahrenheit"],
+                            "description": "Temperature unit"
+                        }
+                    },
+                    "required": ["location"]
+                }
+            }
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "search",
+                "description": "Search the web for information",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "Search query"
+                        },
+                        "num_results": {
+                            "type": "integer",
+                            "description": "Number of results to return"
+                        }
+                    },
+                    "required": ["query"]
+                }
+            }
+        }
+    ],
+    "messages": [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant."
+        },
+        {
+            "role": "user",
+            "content": "What's the weather in Beijing?"
+        },
+        {
+            "role": "assistant",
+            "reasoning_content": "The user wants to know the weather in Beijing. I should use the get_weather tool.",
+            "tool_calls": [
+                {
+                    "id": "call_001",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": "{\"location\": \"Beijing\", \"unit\": \"celsius\"}"
+                    }
+                }
+            ]
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_001",
+            "content": "{\"temperature\": 22, \"condition\": \"sunny\", \"humidity\": 45}"
+        },
+        {
+            "role": "assistant",
+            "reasoning_content": "Got the weather data. Let me format a nice response.",
+            "content": "The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity."
+        }
+    ]
+}

encoding/tests/test_input_2.json

@@ -0,0 +1,24 @@
+[
+  {
+    "role": "system",
+    "content": "You are a helpful assistant."
+  },
+  {
+    "role": "user",
+    "content": "Hello"
+  },
+  {
+    "role": "assistant",
+    "reasoning_content": "The user said hello, I should greet back.",
+    "content": "Hi there! How can I help you?"
+  },
+  {
+    "role": "user",
+    "content": "What is the capital of France?"
+  },
+  {
+    "role": "assistant",
+    "reasoning_content": "The user asks about the capital of France. It is Paris.",
+    "content": "The capital of France is Paris."
+  }
+]

encoding/tests/test_input_3.json

@@ -0,0 +1,159 @@
+[
+  {
+    "role": "system",
+    "content": "该助手为DeepSeek，由深度求索公司创造。"
+  },
+  {
+    "role": "latest_reminder",
+    "content": "2026-02-21,星期六,广州,App,中文"
+  },
+  {
+    "role": "developer",
+    "content": "小柴胡冲剂和布洛芬能一起吃吗？\n\nCITATION FORMAT: 【{cursor_id}†L{start_line_id}(-L{end_line_id})?】",
+    "tools": [
+      {
+        "type": "function",
+        "function": {
+          "name": "search",
+          "description": "Web search. Split multiple queries with '||'.",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "queries": {
+                "type": "string",
+                "description": "query1||query2"
+              }
+            },
+            "required": [
+              "queries"
+            ],
+            "additionalProperties": false,
+            "$schema": "http://json-schema.org/draft-07/schema#"
+          }
+        }
+      },
+      {
+        "type": "function",
+        "function": {
+          "name": "open",
+          "description": "Batch open IDs (format 【{id}†...】) or URLs.",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "open_list": {
+                "type": "array",
+                "items": {
+                  "type": "object",
+                  "properties": {
+                    "id": {
+                      "description": "ID or URL",
+                      "anyOf": [
+                        {
+                          "type": "integer"
+                        },
+                        {
+                          "type": "string"
+                        }
+                      ],
+                      "default": -1
+                    },
+                    "cursor": {
+                      "type": "integer",
+                      "description": "",
+                      "default": -1
+                    },
+                    "loc": {
+                      "type": "integer",
+                      "description": "Start line",
+                      "default": -1
+                    },
+                    "num_lines": {
+                      "type": "integer",
+                      "description": "",
+                      "default": -1
+                    },
+                    "view_source": {
+                      "type": "boolean",
+                      "description": "",
+                      "default": false
+                    }
+                  },
+                  "additionalProperties": false
+                },
+                "description": ""
+              }
+            },
+            "required": [
+              "open_list"
+            ],
+            "additionalProperties": false,
+            "$schema": "http://json-schema.org/draft-07/schema#"
+          }
+        }
+      },
+      {
+        "type": "function",
+        "function": {
+          "name": "find",
+          "description": "Find exact text pattern in pages.",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "find_list": {
+                "type": "array",
+                "items": {
+                  "type": "object",
+                  "properties": {
+                    "pattern": {
+                      "type": "string",
+                      "description": ""
+                    },
+                    "cursor": {
+                      "type": "integer",
+                      "description": "",
+                      "default": -1
+                    }
+                  },
+                  "required": [
+                    "pattern"
+                  ],
+                  "additionalProperties": false
+                },
+                "description": ""
+              }
+            },
+            "required": [
+              "find_list"
+            ],
+            "additionalProperties": false,
+            "$schema": "http://json-schema.org/draft-07/schema#"
+          }
+        }
+      }
+    ]
+  },
+  {
+    "role": "assistant",
+    "content": "",
+    "reasoning_content": "用户想知道小柴胡冲剂和布洛芬能否一起服用。",
+    "tool_calls": [
+      {
+        "type": "function",
+        "function": {
+          "name": "search",
+          "arguments": "{\"queries\": \"小柴胡冲剂 布洛芬 相互作用 一起吃\"}"
+        }
+      }
+    ]
+  },
+  {
+    "role": "tool",
+    "content": "[0]"
+  },
+  {
+    "role": "assistant",
+    "content": "请及时就医。",
+    "reasoning_content": "现在开始组织回答。",
+    "tool_calls": []
+  }
+]

encoding/tests/test_input_4.json

@@ -0,0 +1,28 @@
+[
+  {
+    "role": "system",
+    "content": "该助手为DeepSeek-V3，由深度求索公司创造。\n今天是2025年10月17日，星期五。"
+  },
+  {
+    "role": "latest_reminder",
+    "content": "2024-11-15,上海市,App,中文"
+  },
+  {
+    "role": "user",
+    "content": "热海大滚锅是世界著名温泉吗"
+  },
+  {
+    "role": "assistant",
+    "content": "关于热海大滚锅是否是世界著名温泉，可以这样总结：它在**中国乃至全球的地热奇观中占有重要地位**，但“世界著名”的称号更侧重于它作为**独特的地质现象和旅游景点**，而非普遍的温泉疗养体验。\n\n为了让你快速了解，我整理了一个简要的表格：\n\n| 维度 | 热海大滚锅的地位与特点 |\n| :--- | :--- |\n| **地质奇观** | **世界罕见**的高温地热系统，泉眼多、水温高、形态多样。 |\n| **国内声誉** | **中国三大地热区之一**，**国家5A级旅游景区**，自古闻名（徐霞客曾游历并记载）。 |\n| **国际知名度** | 在一些旅游平台被提及为“世界六大温泉”之一，但此说法流传不广，其国际声誉更多建立在地质独特性上。 |\n| **核心体验** | **观赏地热奇观**（如97℃沸腾的“大滚锅”）、**体验温泉煮鸡蛋**。 |\n\n### 💡 游玩攻略与温馨提示\n\n如果你计划前往热海大滚锅，这里有一些实用信息供你参考：\n\n- **门票与开放时间**：\n    - **门票**：景区门票约为**50元/人**。如果选择包含温泉沐浴的套餐，价格会更高，例如约**288元**。\n    - **开放时间**：景区一般**08:00-18:00**开放，但具体时间可能变动，建议提前核实。\n\n- **特色体验**：\n    - **温泉煮鸡蛋**：这几乎是必试项目。可以在景区门口购买用草绳串起的生鸡蛋（约5-8元/串），然后到“大滚锅”旁的指定区域蒸煮，几分钟便可熟食，趣味十足。\n    - **金汤足浴**：可以直接用从“大滚锅”流出的温泉水泡脚，缓解旅途疲劳。\n\n- **注意事项**：\n    - **安全第一**：“大滚锅”水温极高，务必遵守游览规则，在指定区域内观赏，切勿随意触碰泉水。\n    - **规划行程**：建议为热海景区预留**3-4小时**的游览时间。景区内步道不走回头路，出入口有观光车接送。\n\n希望这些信息能帮助你更好地了解热海大滚锅。如果你对腾冲的其他景点或者行程规划有更多疑问，我很乐意提供进一步的信息。",
+    "mask": 1
+  },
+  {
+    "role": "user",
+    "content": "世界著名温泉有哪些",
+    "task": "action"
+  },
+  {
+    "role": "assistant",
+    "content": "Search"
+  }
+]

encoding/tests/test_output_1.txt

@@ -0,0 +1,36 @@
+<｜begin▁of▁sentence｜>You are a helpful assistant.
+
+## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<｜DSML｜tool_calls>" block like the following:
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="$TOOL_NAME">
+<｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter>
+...
+</｜DSML｜invoke>
+<｜DSML｜invoke name="$TOOL_NAME2">
+...
+</｜DSML｜invoke>
+</｜DSML｜tool_calls>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.
+
+Otherwise, output directly after </think> with tool calls or final response.
+
+### Available Tool Schemas
+
+{"name": "get_weather", "description": "Get the weather for a specific location", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city name"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "Temperature unit"}}, "required": ["location"]}}
+{"name": "search", "description": "Search the web for information", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query"}, "num_results": {"type": "integer", "description": "Number of results to return"}}, "required": ["query"]}}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+<｜User｜>What's the weather in Beijing?<｜Assistant｜><think>The user wants to know the weather in Beijing. I should use the get_weather tool.</think>
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="get_weather">
+<｜DSML｜parameter name="location" string="true">Beijing</｜DSML｜parameter>
+<｜DSML｜parameter name="unit" string="true">celsius</｜DSML｜parameter>
+</｜DSML｜invoke>
+</｜DSML｜tool_calls><｜end▁of▁sentence｜><｜User｜><tool_result>{"temperature": 22, "condition": "sunny", "humidity": 45}</tool_result><｜Assistant｜><think>Got the weather data. Let me format a nice response.</think>The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity.<｜end▁of▁sentence｜>

encoding/tests/test_output_2.txt

@@ -0,0 +1 @@
+<｜begin▁of▁sentence｜>You are a helpful assistant.<｜User｜>Hello<｜Assistant｜></think>Hi there! How can I help you?<｜end▁of▁sentence｜><｜User｜>What is the capital of France?<｜Assistant｜><think>The user asks about the capital of France. It is Paris.</think>The capital of France is Paris.<｜end▁of▁sentence｜>

encoding/tests/test_output_3.txt

@@ -0,0 +1,38 @@
+<｜begin▁of▁sentence｜>该助手为DeepSeek，由深度求索公司创造。<｜latest_reminder｜>2026-02-21,星期六,广州,App,中文<｜User｜>小柴胡冲剂和布洛芬能一起吃吗？
+
+CITATION FORMAT: 【{cursor_id}†L{start_line_id}(-L{end_line_id})?】
+
+## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<｜DSML｜tool_calls>" block like the following:
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="$TOOL_NAME">
+<｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter>
+...
+</｜DSML｜invoke>
+<｜DSML｜invoke name="$TOOL_NAME2">
+...
+</｜DSML｜invoke>
+</｜DSML｜tool_calls>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.
+
+Otherwise, output directly after </think> with tool calls or final response.
+
+### Available Tool Schemas
+
+{"name": "search", "description": "Web search. Split multiple queries with '||'.", "parameters": {"type": "object", "properties": {"queries": {"type": "string", "description": "query1||query2"}}, "required": ["queries"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
+{"name": "open", "description": "Batch open IDs (format 【{id}†...】) or URLs.", "parameters": {"type": "object", "properties": {"open_list": {"type": "array", "items": {"type": "object", "properties": {"id": {"description": "ID or URL", "anyOf": [{"type": "integer"}, {"type": "string"}], "default": -1}, "cursor": {"type": "integer", "description": "", "default": -1}, "loc": {"type": "integer", "description": "Start line", "default": -1}, "num_lines": {"type": "integer", "description": "", "default": -1}, "view_source": {"type": "boolean", "description": "", "default": false}}, "additionalProperties": false}, "description": ""}}, "required": ["open_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
+{"name": "find", "description": "Find exact text pattern in pages.", "parameters": {"type": "object", "properties": {"find_list": {"type": "array", "items": {"type": "object", "properties": {"pattern": {"type": "string", "description": ""}, "cursor": {"type": "integer", "description": "", "default": -1}}, "required": ["pattern"], "additionalProperties": false}, "description": ""}}, "required": ["find_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+<｜Assistant｜><think>用户想知道小柴胡冲剂和布洛芬能否一起服用。</think>
+
+<｜DSML｜tool_calls>
+<｜DSML｜invoke name="search">
+<｜DSML｜parameter name="queries" string="true">小柴胡冲剂 布洛芬 相互作用 一起吃</｜DSML｜parameter>
+</｜DSML｜invoke>
+</｜DSML｜tool_calls><｜end▁of▁sentence｜><｜User｜><tool_result>[0]</tool_result><｜Assistant｜><think>现在开始组织回答。</think>请及时就医。<｜end▁of▁sentence｜>

encoding/tests/test_output_4.txt

@@ -0,0 +1,29 @@
+<｜begin▁of▁sentence｜>该助手为DeepSeek-V3，由深度求索公司创造。
+今天是2025年10月17日，星期五。<｜latest_reminder｜>2024-11-15,上海市,App,中文<｜User｜>热海大滚锅是世界著名温泉吗<｜Assistant｜></think>关于热海大滚锅是否是世界著名温泉，可以这样总结：它在**中国乃至全球的地热奇观中占有重要地位**，但“世界著名”的称号更侧重于它作为**独特的地质现象和旅游景点**，而非普遍的温泉疗养体验。
+
+为了让你快速了解，我整理了一个简要的表格：
+
+| 维度 | 热海大滚锅的地位与特点 |
+| :--- | :--- |
+| **地质奇观** | **世界罕见**的高温地热系统，泉眼多、水温高、形态多样。 |
+| **国内声誉** | **中国三大地热区之一**，**国家5A级旅游景区**，自古闻名（徐霞客曾游历并记载）。 |
+| **国际知名度** | 在一些旅游平台被提及为“世界六大温泉”之一，但此说法流传不广，其国际声誉更多建立在地质独特性上。 |
+| **核心体验** | **观赏地热奇观**（如97℃沸腾的“大滚锅”）、**体验温泉煮鸡蛋**。 |
+
+### 💡 游玩攻略与温馨提示
+
+如果你计划前往热海大滚锅，这里有一些实用信息供你参考：
+
+- **门票与开放时间**：
+    - **门票**：景区门票约为**50元/人**。如果选择包含温泉沐浴的套餐，价格会更高，例如约**288元**。
+    - **开放时间**：景区一般**08:00-18:00**开放，但具体时间可能变动，建议提前核实。
+
+- **特色体验**：
+    - **温泉煮鸡蛋**：这几乎是必试项目。可以在景区门口购买用草绳串起的生鸡蛋（约5-8元/串），然后到“大滚锅”旁的指定区域蒸煮，几分钟便可熟食，趣味十足。
+    - **金汤足浴**：可以直接用从“大滚锅”流出的温泉水泡脚，缓解旅途疲劳。
+
+- **注意事项**：
+    - **安全第一**：“大滚锅”水温极高，务必遵守游览规则，在指定区域内观赏，切勿随意触碰泉水。
+    - **规划行程**：建议为热海景区预留**3-4小时**的游览时间。景区内步道不走回头路，出入口有观光车接送。
+
+希望这些信息能帮助你更好地了解热海大滚锅。如果你对腾冲的其他景点或者行程规划有更多疑问，我很乐意提供进一步的信息。<｜end▁of▁sentence｜><｜User｜>世界著名温泉有哪些<｜Assistant｜></think><｜action｜>Search<｜end▁of▁sentence｜>

generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "do_sample": true,
+  "temperature": 1.0,
+  "top_p": 1.0,
+  "transformers_version": "4.46.3"
+}

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
+  "model_max_length": 1048576,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sp_model_kwargs": {},
+  "unk_token": null,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}