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LICENSE

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+Tencent is pleased to support the open source community by making Tencent Hy3 available.
+
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README.md

@@ -0,0 +1,149 @@
+---
+license: apache-2.0
+base_model: tencent/Hy3
+base_model_relation: quantized
+pipeline_tag: text-generation
+library_name: vllm
+tags:
+  - hunyuan
+  - hy_v3
+  - mixture-of-experts
+  - moe
+  - 4-bit
+  - int4
+  - w4a16
+  - compressed-tensors
+  - quantized
+  - vllm
+  - long-context
+  - yarn
+---
+
+# Hy3-W4A16 — 4-bit (INT4) quantization of tencent/Hy3
+
+A **4-bit weight-only (W4A16)** quantization of **[tencent/Hy3](https://huggingface.co/tencent/Hy3)**
+(`HYV3ForCausalLM`, `hy_v3`) — a 295B-parameter / 21B-active Mixture-of-Experts model.
+Packaged in the **`compressed-tensors`** `pack-quantized` format so it loads directly in **vLLM**.
+
+## Why this model
+
+- **Small.** **~146 GB** vs **~557 GB** for the original BF16 (~3.8× smaller). The whole
+  295B MoE now **fits on a single ≥180 GB GPU** (e.g. one B200 192 GB / B300 ~288 GB)
+  with KV-cache headroom — no tensor-parallel sharding required just to load it.
+  (Note: it does **not** fit a 141 GB H200 without offload/TP.)
+- **vLLM-native.** Loads out of the box with vLLM (recent build with `hy_v3` support) using the
+  Marlin INT4 MoE + Linear kernels. Fast tensor-core prefill.
+- **Long context via YaRN.** With YaRN RoPE scaling the context extends from the native
+  **262,144** up to **1,048,576 (1M)** tokens (configurable). Dense needle-in-a-haystack
+  retrieval is **verified past native (318K, PASS)** on a single GPU; see *Long context* below.
+
+## Quantization details
+
+| | |
+|---|---|
+| Scheme | **W4A16** — 4-bit **int**, **symmetric**, **group_size=128**, RTN (round-to-nearest, data-free) |
+| Format | `compressed-tensors` **`pack-quantized`** (`quant_method: compressed-tensors`) |
+| Quantized | attention `q/k/v/o_proj`, dense-layer FFN, **all 192 routed experts** + shared expert (`gate/up/down_proj`) |
+| Kept in original precision | `lm_head`, router gate (`mlp.router.gate`), `eh_proj` (MTP), all norms, `embed_tokens` |
+| Base dtype | bf16 (scales stored bf16) |
+
+Produced by a direct tensor-by-tensor RTN packer (no calibration dataset). RTN keeps the
+pipeline simple and lossless-format-correct; for maximum quality at 4-bit, a calibrated
+GPTQ/AWQ pass would be marginally better.
+
+## Running with vLLM
+
+Requires a vLLM build new enough to include the `hy_v3` architecture (vLLM `main`/nightly at time of
+writing). Example on a single GPU:
+
+```bash
+vllm serve /path/to/Hy3-W4A16 \
+  --max-model-len 262144 \
+  --gpu-memory-utilization 0.9 \
+  --trust-remote-code
+```
+
+**NVIDIA Blackwell (sm_100/sm_103, e.g. B200/B300) note:** at the time of testing, FlashInfer's
+runtime JIT could not compile for `compute_103a` with the bundled CUDA toolkit, which crashed the
+default sampler/attention. Work around it with the Triton attention backend + native sampler:
+
+```bash
+VLLM_USE_FLASHINFER_SAMPLER=0 vllm serve /path/to/Hy3-W4A16 \
+  --attention-backend TRITON_ATTN \
+  --kv-cache-dtype fp8 \
+  --max-model-len 262144 \
+  --gpu-memory-utilization 0.9 \
+  --trust-remote-code --enforce-eager
+```
+
+`--kv-cache-dtype fp8` halves KV memory (recommended for long context). On Hopper/Ada or with a
+FlashInfer build that supports your GPU, you can drop the two workaround flags.
+
+## Long context (YaRN)
+
+The base model is `rope_type: "default"` with `max_position_embeddings: 262144`. To go beyond,
+enable YaRN in `config.json`:
+
+```json
+"rope_parameters": {
+  "rope_theta": 11158840.0,
+  "rope_type": "yarn",
+  "factor": 4.0,
+  "original_max_position_embeddings": 262144
+}
+```
+
+and raise `--max-model-len` (up to `262144 * factor = 1048576`).
+
+> **This shipped `config.json` already has YaRN factor 4 enabled** (context up to 1,048,576).
+> Set `rope_type` back to `"default"` if you want the native-only 262,144 behavior.
+
+Memory on a single ~275 GB GPU (146 GB weights):
+- **fp8 KV** (`--kv-cache-dtype fp8`): comfortably fits **~500K** dense tokens; fast tensor-core prefill.
+- **int4 KV** (`--kv-cache-dtype int4_per_token_head`): fits **~1M** dense tokens, but its kernel is
+  compute-bound and much slower for long prefill.
+- Full **dense 1M** is best served with **multi-GPU** (tensor-parallel) for both memory and speed.
+
+## Verified results (single B300, this checkpoint)
+
+| Test | Result |
+|---|---|
+| **HumanEval pass@1 (greedy)** | ✅ **150/164 = 91.5%** — coding ability well-preserved at 4-bit |
+| Chat sanity (`11+22+33` → 66; capital of France → Paris; first 5 primes) | ✅ correct — quality preserved at 4-bit |
+| Needle-in-a-haystack 4K / 16K / 64K / 137K (in-range) | ✅ all PASS |
+| **Needle-in-a-haystack 318K dense (YaRN ×4, fp8 KV)** | ✅ **PASS** — retrieval works past the native 262,144 |
+| 1M-*distance* retrieval (sparse position probe, YaRN ×4) | ✅ retrieves where raw RoPE fails |
+
+<details>
+<summary><b>How HumanEval was measured</b> (for reproducibility)</summary>
+
+- **Engine/config:** this W4A16 checkpoint served by vLLM on a single B300, **as shipped**
+  (YaRN factor 4 enabled), `--attention-backend TRITON_ATTN --kv-cache-dtype fp8
+  --enforce-eager`, `VLLM_USE_FLASHINFER_SAMPLER=0`.
+- **Data:** the 164 problems from `openai_humaneval` (`human-eval` package).
+- **Decoding:** **greedy** (`temperature=0`), `max_tokens=768`, **pass@1** (single sample per problem).
+- **Prompting:** chat template with the instruction *"Complete the following Python function.
+  Return the COMPLETE function in a single ```python code block. No tests, no explanations."*
+  The first ```python block is extracted; if it lacks the `entry_point` def, the original stub
+  is prepended.
+- **Scoring:** each candidate is run as `candidate + test + check(entry_point)` in a subprocess
+  (15s timeout); exit-code 0 = pass.
+- **Result:** **150/164 = 91.5%**. Note this uses a chat+extraction harness (not the canonical
+  raw-completion protocol), so a few of the 14 misses may be extraction artifacts — treat 91.5%
+  as a conservative figure.
+
+</details>
+
+## Caveats & honesty
+
+- **4-bit RTN is lossy.** Chat/reasoning quality is well-preserved in our checks, but expect small
+  degradation vs the BF16 original, especially on exact-match/coding tasks.
+- **`>262144` is extrapolation.** The base model was trained to 262,144; YaRN is applied at inference.
+  Dense retrieval is verified to **318K**; full dense-1M quality across depths is **not** exhaustively
+  validated here — evaluate for your use case.
+- This is a **community derivative**, not affiliated with or endorsed by Tencent.
+
+## License
+
+Apache-2.0, inheriting the license of the base model
+[`tencent/Hy3`](https://huggingface.co/tencent/Hy3) (see `LICENSE`).

README_CN.md

@@ -0,0 +1,227 @@
+<p align="left">
+   <a href="https://huggingface.co/tencent/Hy3">English</a>&nbsp;｜&nbsp;中文
+</p>
+<br>
+
+<p align="center">
+ <img src="assets/logo-zh.png" width="400"/> <br>
+</p>
+
+<div align="center" style="line-height: 1;">
+
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue)](#许可证)
+&nbsp;&nbsp;
+[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Tencent%20Hy-ffc107?color=ffc107&logoColor=white)](https://huggingface.co/tencent/Hy3)
+&nbsp;&nbsp;
+[![ModelScope](https://img.shields.io/badge/ModelScope-Tencent%20Hy-624aff)](https://modelscope.cn/models/Tencent-Hunyuan/Hy3)
+&nbsp;&nbsp;
+[![cnb.cool](https://img.shields.io/badge/cnb.cool-Tencent%20Hy-blue?logoColor=white)](https://cnb.cool/ai-models/tencent/Hy3)
+&nbsp;&nbsp;
+[![GitCode](https://img.shields.io/badge/GitCode-Tencent%20Hy-red?logoColor=white)](https://ai.gitcode.com/tencent_hunyuan/Hy3)
+
+</div>
+
+<p align="center">
+    🖥️&nbsp;<a href="https://aistudio.tencent.com/"><b>官方网站</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    💬&nbsp;<a href="https://github.com/Tencent-Hunyuan/Hy3"><b>GitHub</b></a></p>
+
+---
+
+## 目录
+
+- [模型介绍](#模型介绍)
+- [更强大的智能体能力](#更强大的智能体能力)
+- [更可靠的产品体验](#更可靠的产品体验)
+- [Benchmark 附录](#benchmark-附录)
+- [新闻](#新闻)
+- [模型链接](#模型链接)
+- [快速开始](#快速开始)
+- [推理和部署](#推理和部署)
+  - [vLLM](#使用-vllm-推理)
+  - [SGLang](#使用-sglang-推理)
+- [模型微调](#模型微调)
+- [量化工具](#量化工具)
+- [许可证](#许可证)
+- [联系我们](#联系我们)
+
+---
+
+## 模型介绍
+
+**Hy3** 是由腾讯混元团队研发的快慢思考融合的混合专家模型，总参数量 295B，激活参数 21B，MTP 层参数 3.8B。4 月底发布 Hy3 Preview 后，我们在 50 多个业务中获得了广泛的反馈，修复了各种体验问题，进一步提升了后训练的质量和规模。今天，我们正式发布 Hy3。它展现出显著强于同尺寸模型的智能水平，并比肩更大尺寸旗舰模型的效果，大幅提升了在各类产品和生产力任务中的实用价值。
+
+
+| 属性 | 值 |
+|:---|:---|
+| 架构 | 混合专家（MoE） |
+| 总参数量 | 295B |
+| 激活参数量 | 21B |
+| MTP层参数量 | 3.8B |
+| 层数（不含MTP层） | 80 |
+| MTP层数 | 1 |
+| 注意力头 | 64（GQA，8 个 KV 头，head dim 128） |
+| 隐藏层维度 | 4096 |
+| FFN 中间层维度 | 13312 |
+| 上下文长度 | 256K |
+| 词表大小 | 120832 |
+| 专家数量 | 192 个专家，top-8 激活 |
+| 支持精度 | BF16 |
+
+## 更强大的智能体能力
+
+Hy3 基于 Preview 进一步提升了后训练数据的质量和多样性，扩大了 RL 算力规模，在推理、智能体、长上下文等任务上显著进步，取得了比肩国内外更大尺寸旗舰模型（参数规模往往是 Hy3 的 2～5 倍）的效果。
+
+<p align="center">
+  <img src="assets/benchmark.png" width="100%"/>
+</p>
+
+Hy3 在软件开发、办公生产、金融建模、前端设计、游戏制作等生产力任务上的进步尤其显著，可以成为高性价比的可靠选择。在内部组织的 270 位专家基于真实工作的模型盲测中，Hy3（均分 2.67 / 4）展现出优于 GLM5.1（均分 2.51 / 4）的表现，尤其在前端、数据与存储、CI/CD 等类别优势显著。
+
+## 更可靠的产品体验
+
+模型的实用体验不完全与榜单成绩挂钩。基于广泛的用户反馈和分析，我们定位并优化了一系列体验向能力，获得了产品侧一致且积极的评价。
+
+**输出格式和工具调用稳定性**：我们显著改善了一系列基础底线问题，确保模型在各种工具设置和输出要求下达到生产级标准，工具调用的错误恢复能力和效率大幅提升。另外，Hy3 还增强了跨脚手架泛化性，不同脚手架（如 CodeBuddy、Cline、KiloCode）在 SWE Bench Verified 上的分数标准差控制在 4 个百分点以内。
+
+**知识常识和抗幻觉能力**：基于“有依据才回答、无依据明示缺失，多来源信息不乱拼，数据和状态不乱编”的理想态，我们进行了细粒度的数据清洗和训练约束。在基于真实产品的内部评测中，Hy3 的幻觉率从 12.5% 降至 5.4%，常识错误率从 25.4% 降至 12.7%，显著改善了“张冠李戴”、无中生有、逻辑矛盾等问题。
+
+**复杂上下文承接与多轮意图保持能力**：Hy3 在 SFT 与 RL 阶段联合优化了指代消解、省略还原及多轮约束继承等业务痛点问题，内部评测的多轮问题率从 17.4% 降至 7.9%。同时 Hy3 在长对话理解基准中取得显著跨越（如 MRCR 从 42.9% 升至 75.1%），输出更精炼的同时确保复杂意图在长程交互中不衰减、不跑偏。
+
+## Benchmark 附录
+
+<p align="center">
+  <img src="assets/benchmark-appendix.png" width="100%"/>
+</p>
+
+## 新闻
+
+* 🔥 我们在 [Hugging Face](https://huggingface.co/tencent/Hy3)、[ModelScope](https://modelscope.cn/models/Tencent-Hunyuan/Hy3)、[GitCode](https://ai.gitcode.com/tencent_hunyuan/Hy3) 和 [CNB](https://cnb.cool/ai-models/tencent/Hy3) 开源了 **Hy3** 和 **Hy3-FP8** 模型权重。
+
+## 模型链接
+
+
+| 模型名 | 简介 | Hugging Face | ModelScope | GitCode | CNB |
+|:---|:---|:---:|:---:|:---:|:---:|
+| Hy3 | Instruct 模型 | 🤗 [Model](https://huggingface.co/tencent/Hy3) | [Model](https://modelscope.cn/models/Tencent-Hunyuan/Hy3) | [Model](https://ai.gitcode.com/tencent_hunyuan/Hy3) | [Model](https://cnb.cool/ai-models/tencent/Hy3) |
+| Hy3-FP8 | FP8 量化 Instruct 模型 | 🤗 [Model](https://huggingface.co/tencent/Hy3-FP8) | [Model](https://modelscope.cn/models/Tencent-Hunyuan/Hy3-FP8) | [Model](https://ai.gitcode.com/tencent_hunyuan/Hy3-FP8) | [Model](https://cnb.cool/ai-models/tencent/Hy3-FP8) |
+
+## 快速开始
+
+建议先通过 [vLLM](#使用-vllm-推理) 或 [SGLang](#使用-sglang-推理) 部署服务，然后通过 OpenAI 兼容 API 调用：
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")
+
+response = client.chat.completions.create(
+    model="hy3",
+    messages=[
+        {"role": "user", "content": "你好！请简单介绍一下你自己。"},
+    ],
+    temperature=0.9,
+    top_p=1.0,
+    # reasoning_effort: "no_think"（默认，直接回复）、"low"、"high"（深度思维链）
+    extra_body={"chat_template_kwargs": {"reasoning_effort": "no_think"}},
+)
+print(response.choices[0].message.content)
+```
+
+> **推荐参数**：`temperature=0.9`，`top_p=1.0`。
+>
+> **推理模式**：复杂任务（数学、编程、推理）建议设置 `reasoning_effort="high"`，日常对话可使用默认的 `"no_think"` 直接回复。
+
+具体部署方式请参考下方[推理和部署](#推理和部署)章节。
+
+## 推理和部署
+
+Hy3 总参数量为 295B，当使用 8 张 GPU 时，建议使用 H20-3e 或其他有更大显存的卡型。
+
+对于生产环境部署，我们建议使用 vLLM 或 SGLang，这两个框架都为 Hy3 提供了专门的配置方案：
+
+- [vLLM](https://github.com/vllm-project/vllm) - 请查阅 [vLLM recipes](https://recipes.vllm.ai/tencent/Hy3)
+
+- [SGLang](https://docs.sglang.io/) - 请查阅 [SGLang cookbook](https://lmsysorg.mintlify.app/cookbook/autoregressive/Tencent/Hy3)
+
+### vLLM
+
+从源码构建 vLLM：
+
+```bash
+uv venv --python 3.12 --seed --managed-python
+source .venv/bin/activate
+git clone https://github.com/vllm-project/vllm.git
+cd vllm
+uv pip install --editable . --torch-backend=auto
+```
+
+启动 vLLM 服务，开启 MTP：
+
+```bash
+# Switch to trtllm backend to work-around mnnvl workspace size issue.
+export VLLM_FLASHINFER_ALLREDUCE_BACKEND=trtllm
+
+vllm serve tencent/Hy3 \
+  --tensor-parallel-size 8 \
+  --speculative-config.method mtp \
+  --speculative-config.num_speculative_tokens 2 \
+  --tool-call-parser hy_v3 \
+  --reasoning-parser hy_v3 \
+  --enable-auto-tool-choice \
+  --port 8000 \
+  --served-model-name hy3
+```
+
+### SGLang
+
+从源码构建 SGLang：
+
+```bash
+git clone https://github.com/sgl-project/sglang
+cd sglang
+pip3 install pip --upgrade
+pip3 install "transformers>=5.6.0"
+pip3 install -e "python"
+```
+
+启动 SGLang 服务，开启 MTP：
+
+```bash
+python3 -m sglang.launch_server \
+  --model tencent/Hy3 \
+  --tp-size 8 \
+  --tool-call-parser hunyuan \
+  --reasoning-parser hunyuan \
+  --speculative-num-steps 2 \
+  --speculative-eagle-topk 1 \
+  --speculative-num-draft-tokens 3 \
+  --speculative-algorithm EAGLE \
+  --port 8000 \
+  --served-model-name hy3
+```
+
+## 模型微调
+
+Hy3 提供了完整的模型微调流程，详细的微调文档请参考：[模型微调指南](https://huggingface.co/tencent/Hy3/blob/main/finetune/README_CN.md)
+
+## 量化工具
+
+我们提供了 [AngelSlim](https://github.com/tencent/AngelSlim)——一套易用、全面、高效的大模型压缩工具包，涵盖常用量化算法、低比特量化和投机采样等能力。
+
+## 许可证
+
+
+Hy3 基于 **Apache 2.0 许可证** 发布。详情请参阅 [LICENSE](https://huggingface.co/tencent/Hy3/blob/main/LICENSE)。
+
+## 联系我们
+
+如有问题或建议，欢迎通过邮件联系我们：
+
+📧 **hunyuan_opensource@tencent.com**
+
+---
+
+<p align="center">
+  <i>Hy3 由腾讯混元团队研发。</i>
+</p>

chat_template.jinja

@@ -0,0 +1,222 @@
+{#- ----------‑‑‑ special token variables ‑‑‑---------- -#}
+{%- set HYTK = ':opensource' %}
+{%- set eos_token = '<｜hy_eos{}｜>'.format(HYTK) %}
+{%- set bos_token = '<｜hy_begin_of_sentence{}｜>'.format(HYTK) %}
+{%- set pad_token = '<｜hy_pad{}｜>'.format(HYTK) %}
+{%- set user_token = '<｜hy_User{}｜>'.format(HYTK) %}
+{%- set assistant_token = '<｜hy_Assistant{}｜>'.format(HYTK) %}
+{%- set think_begin_token = '<think{}>'.format(HYTK) %}
+{%- set think_end_token = '</think{}>'.format(HYTK) %}
+{%- set toolcalls_begin_token = '<tool_calls{}>'.format(HYTK) %}
+{%- set toolcalls_end_token = '</tool_calls{}>'.format(HYTK) %}
+{%- set toolcall_begin_token = '<tool_call{}>'.format(HYTK) %}
+{%- set toolcall_end_token = '</tool_call{}>'.format(HYTK) %}
+{%- set toolsep_token = '<tool_sep{}>'.format(HYTK) %}
+{%- set argkey_begin_token = '<arg_key{}>'.format(HYTK) %}
+{%- set argkey_end_token = '</arg_key{}>'.format(HYTK) %}
+{%- set argvalue_begin_token = '<arg_value{}>'.format(HYTK) %}
+{%- set argvalue_end_token = '</arg_value{}>'.format(HYTK) %}
+{%- set toolresponses_begin_token = '<tool_responses{}>'.format(HYTK) %}
+{%- set toolresponses_end_token = '</tool_responses{}>'.format(HYTK) %}
+{%- set toolresponse_begin_token = '<tool_response{}>'.format(HYTK) %}
+{%- set toolresponse_end_token = '</tool_response{}>'.format(HYTK) %}
+{%- set reasoning_mode_token = '<｜reasoning_mode{}｜>'.format(HYTK) %}
+
+{#- ----------‑‑‑ hyperparameters variables ‑‑‑---------- -#}
+{%- if not add_generation_prompt is defined %}
+    {%- set add_generation_prompt = false %}
+{%- endif %}
+{%- if not preserved_thinking is defined %}
+    {%- if not tools %}
+        {%- set preserved_thinking = false %}
+    {%- else %}
+        {%- set preserved_thinking = true %}
+    {%- endif %}
+{%- endif %}
+{%- if not is_training is defined %}
+    {%- set is_training = false %}
+{%- endif %}
+
+{%- if not reasoning_effort is defined %}
+    {%- set reasoning_effort = 'no_think' %}
+{%- elif reasoning_effort not in ['high', 'low', 'no_think'] %}
+    {%- if reasoning_effort is none %}
+        {{- raise_exception('reasoning_effort error : None, should be no_think/low/high') }}
+    {%- else %}
+        {{- raise_exception('reasoning_effort error : ' + reasoning_effort + ', should be no_think/low/high') }}
+    {%- endif %}
+{%- endif %}
+
+{%- if fallback_strategy is defined and fallback_strategy == 'reasoning_toolcall_retry' %}
+    {%- set reasoning_effort = 'high' %}
+    {%- set add_generation_prompt = false %}
+{%- endif %}
+{%- if not raw_last_assistant is defined %}
+    {%- set raw_last_assistant = false %}
+{%- endif %}
+
+{%- macro visible_text(content) -%}
+    {%- if content is string -%}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping -%}
+        {%- for item in content -%}
+            {%- if item is mapping and item.type == 'text' -%}
+                {{- item.text }}
+            {%- elif item is string -%}
+                {{- item }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- elif content is none -%}
+        {{- '' }}
+    {%- else -%}
+        {{- content }}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- set ns = namespace(last_user_index=-1) %}
+{%- set sp_ns = namespace(system_prompt='', is_first_sp=true) %}
+{%- for message in messages %}
+    {%- if message['role'] == 'system' %}
+        {%- set sp_ns.system_prompt = sp_ns.system_prompt + visible_text(message['content']) %}
+    {%- endif %}
+    {%- if message['role'] == 'user' %}
+        {%- set ns.last_user_index = loop.index0 %}
+    {%- endif %}
+{%- endfor %}
+{%- if reasoning_effort is defined and reasoning_effort is string and reasoning_effort != '' and not tools %}
+    {%- set sp_ns.system_prompt = sp_ns.system_prompt + reasoning_mode_token + 'reasoning_effort:' + reasoning_effort %}
+{%- endif %}
+{{- bos_token }}
+{{- sp_ns.system_prompt }}
+{%- if tools %}
+    {%- if sp_ns.system_prompt != '' %}
+        {{- '\n\n# Tools\n\nYou may call one or more functions to assist with the user query.' }}
+    {%- else %}
+        {{- '# Tools\n\nYou may call one or more functions to assist with the user query.' }}
+    {%- endif %}
+    {{- '\n\nYou are provided with function signatures within <tools></tools> XML tags:' }}
+    {{- '\n<tools>\n' }}
+    {%- for tool in tools %}
+        {%- if loop.index0 > 0 %}
+            {{- '\n' }}
+        {%- endif %}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- '\n</tools>\n\n' }}
+    {{- 'For function call returns, you should first print ' + toolcalls_begin_token + '\n' }}
+    {{- 'For each function call, you should return object like:\n' }}
+    {{- toolcall_begin_token + '{function-name}' + toolsep_token + '\n' }}
+    {{- argkey_begin_token + '{arg-key-1}' + argkey_end_token + '\n' }}
+    {{- argvalue_begin_token + '{arg-value-1}' + argvalue_end_token + '\n' }}
+    {{- argkey_begin_token + '{arg-key-2}' + argkey_end_token + '\n' }}
+    {{- argvalue_begin_token + '{arg-value-2}' + argvalue_end_token + '\n' }}
+    {{- '...\n' }}
+    {{- toolcall_end_token + '\n' }}
+    {%- if reasoning_effort is defined and reasoning_effort is string and reasoning_effort != '' %}
+        {{- 'At the end of function call returns, you should print ' + toolcalls_end_token + reasoning_mode_token + 'reasoning_effort:' + reasoning_effort }}
+    {%- else %}
+        {{- 'At the end of function call returns, you should print ' + toolcalls_end_token }}
+    {%- endif %}
+{%- endif %}
+
+{%- set prev_ns = namespace(is_tool=false, is_tool_first=true) %}
+{%- set last_ns = namespace(last_is_assistant=false) %}
+{%- for message in messages %}
+    {%- if message['role'] == 'user' %}
+        {%- if prev_ns.is_tool %}
+            {{- toolresponses_end_token }}
+        {%- endif %}
+        {{- user_token + visible_text(message['content']) }}
+        {%- set prev_ns.is_tool = false %}
+    {%- endif %}
+    {%- if message['role'] == 'assistant' %}
+        {%- if is_training %}
+            {%- if 'reasoning_content' in message and message['reasoning_content'] is string %}
+                {%- set rc = message['reasoning_content'] %}
+            {%- elif 'reasoning' in message and message['reasoning'] is string %}
+                {%- set rc = message['reasoning'] %}
+            {%- else %}
+                {%- set rc = none %}
+            {%- endif %}
+            {%- if rc is not none %}
+                {%- set content = think_begin_token + rc + think_end_token + visible_text(message['content']) %}
+            {%- else %}
+                {%- set content = think_begin_token + think_end_token + visible_text(message['content']) %}
+            {%- endif %}
+        {%- else %}
+            {%- if ((preserved_thinking is defined and preserved_thinking) or loop.index0 > ns.last_user_index) %}
+                {%- if 'reasoning_content' in message and message['reasoning_content'] is string %}
+                    {%- set rc = message['reasoning_content'] %}
+                {%- elif 'reasoning' in message and message['reasoning'] is string %}
+                    {%- set rc = message['reasoning'] %}
+                {%- else %}
+                    {%- set rc = none %}
+                {%- endif %}
+                {%- if rc is not none %}
+                    {%- set content = think_begin_token + rc + think_end_token + visible_text(message['content']) %}
+                {%- else %}
+                    {%- set content = think_begin_token + think_end_token + visible_text(message['content']) %}
+                {%- endif %}
+            {%- else %}
+                {%- set content = think_begin_token + think_end_token + visible_text(message['content']) %}
+            {%- endif %}
+        {%- endif %}
+        {%- if prev_ns.is_tool %}
+            {{- toolresponses_end_token }}
+        {%- endif %}
+        {{- assistant_token }}
+        {%- if message['tool_calls'] is defined and message['tool_calls'] %}
+            {%- set prev_ns.is_tool_first = true %}
+            {{- content }}
+            {{- toolcalls_begin_token + '\n' }}
+            {%- for tool in message['tool_calls'] %}
+                {%- set arguments = tool['function']['arguments'] %}
+                {{- toolcall_begin_token + tool['function']['name'] + toolsep_token + '\n' }}
+                {%- for key, value in arguments.items() %}
+                    {{- argkey_begin_token + key + argkey_end_token + '\n' }}
+                    {%- if value is not string %}
+                        {%- set value = value | tojson(ensure_ascii=False) %}
+                    {%- endif %}
+                    {{- argvalue_begin_token + value + argvalue_end_token + '\n' }}
+                {%- endfor %}
+                {{- toolcall_end_token + '\n' }}
+            {%- endfor %}
+            {{- toolcalls_end_token + eos_token }}
+        {%- else %}
+            {%- if loop.last and raw_last_assistant %}
+                {{- visible_text(message['content']) }}
+            {%- elif not loop.last or is_training %}
+                {{- content + eos_token }}
+            {%- else %}
+                {{- content }}
+            {%- endif %}
+        {%- endif %}
+        {%- set prev_ns.is_tool = false %}
+    {%- endif %}
+    {%- if message['role'] == 'tool' %}
+        {%- set prev_ns.is_tool = true %}
+        {%- if prev_ns.is_tool_first %}
+            {{- toolresponses_begin_token + '\n' }}
+            {%- set prev_ns.is_tool_first = false %}
+        {%- endif %}
+        {{- toolresponse_begin_token + '\n' + visible_text(message['content']) + '\n' + toolresponse_end_token + '\n' }}
+    {%- endif %}
+    {%- if loop.last and message['role'] == 'assistant' %}
+        {%- set last_ns.last_is_assistant = true %}
+    {%- endif %}
+
+{%- endfor %}
+{%- if prev_ns.is_tool %}
+    {{- toolresponses_end_token }}
+{%- endif %}
+{%- if add_generation_prompt %}
+    {%- if not last_ns.last_is_assistant %}
+        {%- if reasoning_effort is defined and reasoning_effort in ['low', 'high'] %}
+            {{- assistant_token + think_begin_token }}
+        {%- elif reasoning_effort is defined and reasoning_effort == 'no_think' %}
+            {{- assistant_token + think_begin_token + think_end_token }}
+        {%- else %}
+            {{- assistant_token }}
+        {%- endif %}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,84 @@
+{
+  "architectures": [
+    "HYV3ForCausalLM"
+  ],
+  "bos_token_id": 120000,
+  "enable_attention_fp32_softmax": false,
+  "enable_lm_head_fp32": true,
+  "enable_moe_fp32_combine": false,
+  "eod_token_id": 120026,
+  "eos_token_id": 120025,
+  "expert_hidden_dim": 1536,
+  "moe_intermediate_size": 1536,
+  "first_k_dense_replace": 1,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.006,
+  "intermediate_size": 13312,
+  "max_position_embeddings": 262144,
+  "model_type": "hy_v3",
+  "moe_router_enable_expert_bias": true,
+  "moe_router_use_sigmoid": true,
+  "num_attention_heads": 64,
+  "num_experts": 192,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "num_shared_experts": 1,
+  "output_router_logits": true,
+  "pad_token_id": 120002,
+  "qk_norm": true,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 11158840.0,
+    "rope_type": "yarn",
+    "factor": 4.0,
+    "original_max_position_embeddings": 262144
+  },
+  "route_norm": true,
+  "router_scaling_factor": 2.826,
+  "sep_token_id": 120007,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.6.0",
+  "use_cache": true,
+  "use_grouped_mm": false,
+  "vocab_size": 120832,
+  "num_nextn_predict_layers": 1,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "num_bits": 4,
+          "type": "int",
+          "symmetric": true,
+          "group_size": 128,
+          "strategy": "group",
+          "block_structure": null,
+          "dynamic": false,
+          "actorder": null,
+          "scale_dtype": null,
+          "zp_dtype": null,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {}
+        },
+        "input_activations": null,
+        "output_activations": null,
+        "format": null
+      }
+    },
+    "quant_method": "compressed-tensors",
+    "kv_cache_scheme": null,
+    "format": "pack-quantized",
+    "quantization_status": "compressed",
+    "global_compression_ratio": null,
+    "ignore": [
+      "lm_head",
+      "re:.*mlp.router.gate$",
+      "re:.*eh_proj$"
+    ]
+  }
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+    "bos_token_id": 120000,
+    "do_sample": true,
+    "eos_token_id": 120025,
+    "pad_token_id": 120002,
+    "temperature": 0.9,
+    "top_k": -1,
+    "top_p": 1,
+    "transformers_version": "5.6.0"
+}

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