Upload folder using huggingface_hub

LICENSE

@@ -0,0 +1,202 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+

README-cn.md

@@ -0,0 +1,149 @@
+<p align="center">
+<a href="https://github.com/Caldalis/MiniCPM-Math-harness" target="_blank">MiniCPM-Math harness</a> |
+<a href="https://huggingface.co/Caldalis/MiniCPM5-2B-Math-GGUF" target="_blank">GGUF</a> |
+<a href="https://huggingface.co/openbmb/MiniCPM5-2B" target="_blank">MiniCPM5-2B</a> |
+<a href="https://github.com/OpenBMB/MiniCPM" target="_blank">MiniCPM GitHub</a>
+</p>
+
+<p align="center">
+<a href="https://huggingface.co/Caldalis/MiniCPM5-2B-Math" target="_blank">English</a> |
+中文
+</p>
+
+
+## 亮点
+
+**MiniCPM5-2B-Math** 是一个基于 MiniCPM5 的数学与证明专项模型：基于
+[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) 针对长链数学推理和证明写作做了后训练的
+2.5B 稠密模型。单张消费级显卡或一台笔记本就能运行，保留原生 128k 上下文。
+
+- **同尺寸下很强。** 作为最多思考 128K token 的普通单次调用，AIME 2025 92.3、AIME 2026 89.8、HMMT 2026 年 2 月赛 81.8
+  （avg@16），16 样本投票为 100 / 93.3 / 90.9。官方通用模型 MiniCPM5-2B 在同一协议下为 87.1 / 89.8 / 68.4。
+- **能写出可核查的证明。** 在 IMO-ProofBench Basic 上，一次最多 128K token 的普通调用平均能为 30 题中的 21 题写出完整证明，
+  证明蜂群为 23 题（AI 评审盲评）。
+- **自带"证明蜂群"。** 开源的 [MiniCPM-Math harness](https://github.com/Caldalis/MiniCPM-Math-harness) 可以在任意兼容
+  OpenAI 协议的服务上，围绕本模型运行并行证明、对抗式审稿、暴力验算和迭代修补。它针对实测到的失败模式设计。
+- **小到可以自己拥有。** BF16 权重 5.0 GB，GGUF Q4_K_M 仅 1.6 GB。标准 `LlamaForCausalLM` 架构，无需自定义代码。
+
+## 测评
+
+### AIME 与 HMMT
+
+| 测试 | 指标 | MiniCPM5-2B | MiniCPM5-2B-Math |
+|---|---|---:|---:|
+| AIME 2025 | avg@16 | 87.1 | **92.3** |
+| | 投票（maj@16） | 93.3 | **100.0** |
+| AIME 2026 | avg@16 | 89.8 | 89.8 |
+| | 投票（maj@16） | 93.3 | 93.3 |
+| HMMT Feb 2026 | avg@16 | 68.4 | **81.8** |
+| | 投票（maj@16） | 75.8 | **90.9** |
+
+*avg@16* 是单个样本的平均正确率，也就是一次普通 API 调用的正确率；*投票*是 16 个样本的多数答案（用
+[harness](https://github.com/Caldalis/MiniCPM-Math-harness) 复现：`minicpm-math solve -n 16 --max-tokens 131072 --no-early-stop --no-force-finish`）。
+
+### 证明题：IMO-ProofBench Basic
+
+全部 30 题。*单次调用*：每题 16 次独立调用，每次最多 128K token，不做 forced finish（该行为平均一次调用的水平）。
+*证明蜂群*：harness 的 `standard` 档位，prover 和 refiner 每次可用 128K token（8 个 prover，3 个 verifier，最多 2 轮修补）。
+全部 505 份写出的证明由单个 AI 评审（Claude Opus 5.5）对照官方参考解和评分标准盲评，未经人工复核；另有 5 次单次调用
+达到 128K 上限、没有写出证明，记为 0 分。所有证明和评分均已
+[公开](https://github.com/Caldalis/MiniCPM-Math-harness/tree/main/docs/results/proofbench-basic)。
+
+| | 完整证明 | 平均分（0–7） |
+|---|---:|---:|
+| 单次调用（avg@16） | 21.0 / 30 | 5.48 |
+| 证明蜂群 | **23 / 30** | **5.83** |
+
+## 搭配证明蜂群使用
+
+```bash
+pip install "minicpm-math[math] @ git+https://github.com/Caldalis/MiniCPM-Math-harness"
+vllm serve Caldalis/MiniCPM5-2B-Math --max-model-len 131072 --port 8000
+minicpm-math prove "证明：对任意整数 n，n^5 - n 能被 30 整除。" --base-url http://127.0.0.1:8000/v1
+```
+
+上面的蜂群成绩使用了 `--max-tokens 131072`；默认档位每次调用 32K token，速度更快，但没有做过测评。
+
+蜂群会返回最佳证明以及它的评审情况（只有全部 verifier 都打 ≥ 6/7 才标记为 `accepted`），并保留每一次调用的完整记录。
+配置档位、答案投票模式和 benchmark 用法见 [harness README](https://github.com/Caldalis/MiniCPM-Math-harness)。
+
+## 快速开始
+
+> [!Tip]
+> 推荐采样参数：`temperature=0.9, top_p=0.95, min_p=0.0`，开启思考模式。难题的证明可能需要 64K–120K 的输出
+> token，请给服务足够的上下文（`--max-model-len 131072`），并为请求设置较大的 `max_tokens`。
+
+### vLLM
+
+```bash
+pip install "vllm>=0.21"
+vllm serve Caldalis/MiniCPM5-2B-Math --max-model-len 131072 --port 8000
+```
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Caldalis/MiniCPM5-2B-Math",
+    "messages": [{"role": "user", "content": "证明根号 2 是无理数。"}],
+    "max_tokens": 16384, "temperature": 0.9, "top_p": 0.95,
+    "chat_template_kwargs": {"enable_thinking": true}
+  }'
+```
+
+vLLM 0.24.0 + RTX 4090 实测：单卡、短输入时单流解码约 155 tok/s（保持 CUDA Graph 开启，不要加 `--enforce-eager`，
+eager 模式只有约 48 tok/s）。在五张 4090、最多 10 路并发的长证明任务中，单请求解码速度中位数约 118 tok/s，总吞吐约 900 tok/s。
+
+### SGLang
+
+```bash
+pip install "sglang[srt]>=0.5.16"
+python -m sglang.launch_server --model-path Caldalis/MiniCPM5-2B-Math --context-length 131072 --port 30000
+```
+
+### llama.cpp
+
+```bash
+llama-server -hf Caldalis/MiniCPM5-2B-Math-GGUF:Q8_0 --jinja -ngl 99 -c 98304 -np 4 -kvu --port 8000
+```
+
+8000 是 harness 默认连接的端口（不加 `--port` 时 llama-server 监听 8080）；配合这个服务运行 harness 时请用 `--profile quick`。
+请使用 llama.cpp b9833 或更新版本：b9354 加入了 MiniCPM5 分词器，更早的版本加载不了这个 GGUF；b9833 加入了把推理和答案分开的
+chat parser。Apple M5 笔记本（16 GB）+ Q8_0 实测：单流约 46 tok/s，4 个 slot 并发总计约 125 tok/s。llama.cpp 默认的
+`min_p=0.05` 容易引发复读，请在请求中设置 `min_p=0.0`（harness 会自动设置）。
+
+### Transformers
+
+```bash
+pip install -U "transformers>=5.6" accelerate torch
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Caldalis/MiniCPM5-2B-Math"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+# 在 Apple Silicon（MPS）上请额外传入 attn_implementation="eager"：PyTorch 的 MPS SDPA 内核（torch 2.11 下验证）
+# 处理本模型的分组查询注意力时会出错，导致输出质量下降。CUDA 和 CPU 不受影响。
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")
+
+messages = [{"role": "user", "content": "求所有使 n^2 - 19n + 99 为完全平方数的正整数 n。"}]
+inputs = tokenizer.apply_chat_template(
+    messages, tokenize=True, add_generation_prompt=True, enable_thinking=True,
+    return_dict=True, return_tensors="pt",
+).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=8192, do_sample=True, temperature=0.9, top_p=0.95)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+### 提示词建议
+
+- 有最终答案的题目，在末尾加上 *"Please reason step by step, and put your final answer within \boxed{}."*
+- 证明题请明确要求"完整、严谨的证明"；遇到"求所有""确定"类问题，要求双向论证。
+- 开启思考时，推理以 `</think>` 结束，后面是最终答案。带 reasoning parser 的服务会分开返回两部分，否则按 `</think>` 切分即可。
+
+## 许可证
+
+模型权重采用 [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) 许可证发布（见 `LICENSE`），与基座模型
+[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) 的许可证相同。
+

README.md

@@ -0,0 +1,181 @@
+---
+license: apache-2.0
+language:
+  - en
+  - zh
+library_name: transformers
+pipeline_tag: text-generation
+base_model: openbmb/MiniCPM5-2B
+base_model_relation: finetune
+tags:
+  - minicpm
+  - minicpm5
+  - llama
+  - math
+  - reasoning
+  - theorem-proving
+  - long-context
+  - on-device
+---
+
+<p align="center">
+<a href="https://github.com/Caldalis/MiniCPM-Math-harness" target="_blank">MiniCPM-Math Harness</a> |
+<a href="https://huggingface.co/Caldalis/MiniCPM5-2B-Math-GGUF" target="_blank">GGUF</a> |
+<a href="https://huggingface.co/openbmb/MiniCPM5-2B" target="_blank">MiniCPM5-2B</a> |
+<a href="https://github.com/OpenBMB/MiniCPM" target="_blank">MiniCPM GitHub</a>
+</p>
+
+<p align="center">
+English |
+<a href="https://huggingface.co/Caldalis/MiniCPM5-2B-Math/blob/main/README-cn.md" target="_blank">中文</a>
+</p>
+
+## Highlights
+
+**MiniCPM5-2B-Math** is a math and proof model built on MiniCPM5: a dense 2.5B model based on
+[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) and post-trained for long-form mathematical reasoning and proof
+writing. It runs on a single consumer GPU or a laptop and keeps the native 128K context.
+
+- **Strong for its size.** As a plain single call with up to 128K tokens of thinking, 92.3 on AIME 2025, 89.8 on
+  AIME 2026 and 81.8 on HMMT February 2026 (avg@16), and 100 / 93.3 / 90.9 with a 16-sample vote. The official
+  general-purpose MiniCPM5-2B scores 87.1 / 89.8 / 68.4 under the same protocol.
+- **Writes checkable proofs.** On IMO-ProofBench Basic, an average single call with up to 128K tokens writes a
+  complete proof for 21 of the 30 problems, and the proof swarm for 23, as graded blind by an AI grader.
+- **Comes with a proof swarm.** The open-source [MiniCPM-Math Harness](https://github.com/Caldalis/MiniCPM-Math-harness) runs
+  parallel provers, adversarial verifiers, brute-force checks and repair loops around this model on any
+  OpenAI-compatible server. It is designed around the failure modes we measured.
+- **Small enough to own.** BF16 weights are 5.0 GB; GGUF Q4_K_M is 1.6 GB. Standard `LlamaForCausalLM`
+  architecture, so no custom code is needed.
+
+## Evaluation
+
+### AIME and HMMT
+
+| Benchmark | Metric | MiniCPM5-2B | MiniCPM5-2B-Math |
+|---|---|---:|---:|
+| AIME 2025 | avg@16 | 87.1 | **92.3** |
+| | vote (maj@16) | 93.3 | **100.0** |
+| AIME 2026 | avg@16 | 89.8 | 89.8 |
+| | vote (maj@16) | 93.3 | 93.3 |
+| HMMT Feb 2026 | avg@16 | 68.4 | **81.8** |
+| | vote (maj@16) | 75.8 | **90.9** |
+
+*avg@16* is the mean accuracy of single samples, i.e. of one plain API call; *vote* is the majority answer of the 16
+samples (reproduce with the [harness](https://github.com/Caldalis/MiniCPM-Math-harness):
+`minicpm-math solve -n 16 --max-tokens 131072 --no-early-stop --no-force-finish`).
+
+### Proofs: IMO-ProofBench Basic
+
+All 30 problems. *Single call*: 16 independent calls per problem with up to 128K tokens each and no forced finish
+(the row is the average call). *Proof swarm*: the harness' `standard` profile with 128K-token provers and refiners
+(8 provers, 3 verifiers, up to 2 repair rounds). All 505 written proofs were graded blind by one AI grader (Claude
+Opus 5.5) against the official reference solutions and grading guidelines, not by people; the 5 single calls that
+reached the 128K limit without a proof count as 0. Every proof and grade is
+[published](https://github.com/Caldalis/MiniCPM-Math-harness/tree/main/docs/results/proofbench-basic).
+
+| | Complete proofs | Mean score (0–7) |
+|---|---:|---:|
+| single call (avg@16) | 21.0 / 30 | 5.48 |
+| proof swarm | **23 / 30** | **5.83** |
+
+## Use it with the proof swarm
+
+```bash
+pip install "minicpm-math[math] @ git+https://github.com/Caldalis/MiniCPM-Math-harness"
+vllm serve Caldalis/MiniCPM5-2B-Math --max-model-len 131072 --port 8000
+minicpm-math prove "Prove that for every integer n, n^5 - n is divisible by 30." --base-url http://127.0.0.1:8000/v1
+```
+
+The proof-swarm numbers above used `--max-tokens 131072`; the default profile gives each call 32K tokens, which is
+faster but was not benchmarked.
+
+The swarm returns the best proof together with how it was graded (`accepted` only if every verifier gives at
+least 6/7), and keeps a full trace of every call. See the [harness README](https://github.com/Caldalis/MiniCPM-Math-harness)
+for profiles, answer-mode voting, and benchmarking.
+
+## Quickstart
+
+> [!Tip]
+> Recommended sampling: `temperature=0.9, top_p=0.95, min_p=0.0`, with thinking enabled. Proofs for hard
+> problems can need 64K–120K output tokens. Give the server enough context (`--max-model-len 131072`) and the
+> request a large `max_tokens`.
+
+### vLLM
+
+```bash
+pip install "vllm>=0.21"
+vllm serve Caldalis/MiniCPM5-2B-Math --max-model-len 131072 --port 8000
+```
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Caldalis/MiniCPM5-2B-Math",
+    "messages": [{"role": "user", "content": "Prove that the square root of 2 is irrational."}],
+    "max_tokens": 16384, "temperature": 0.9, "top_p": 0.95,
+    "chat_template_kwargs": {"enable_thinking": true}
+  }'
+```
+
+Measured with vLLM 0.24.0 on RTX 4090s: about 155 tok/s single-stream decode on one GPU with short inputs (keep
+CUDA graphs on; do not use `--enforce-eager`, which measured about 48 tok/s). On long proof runs across five 4090s
+with up to 10 concurrent requests, the median request decoded at about 118 tok/s, about 900 tok/s in total.
+
+### SGLang
+
+```bash
+pip install "sglang[srt]>=0.5.16"
+python -m sglang.launch_server --model-path Caldalis/MiniCPM5-2B-Math --context-length 131072 --port 30000
+```
+
+### llama.cpp
+
+```bash
+llama-server -hf Caldalis/MiniCPM5-2B-Math-GGUF:Q8_0 --jinja -ngl 99 -c 98304 -np 4 -kvu --port 8000
+```
+
+Port 8000 is where the harness looks by default (`llama-server` alone listens on 8080); with this server, run the
+harness with `--profile quick`. Use llama.cpp b9833 or newer: b9354 added the MiniCPM5 tokenizer, so older builds
+cannot load the GGUF, and b9833 added the chat parser that separates the reasoning from the answer. On an Apple M5
+laptop (16 GB) with Q8_0: about 46 tok/s for one stream, about 125 tok/s total with 4 slots. llama.cpp's default
+`min_p=0.05` can encourage repetition, so send `min_p=0.0` (the harness does this for you).
+
+### Transformers
+
+```bash
+pip install -U "transformers>=5.6" accelerate torch
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Caldalis/MiniCPM5-2B-Math"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+# On Apple Silicon (MPS), also pass attn_implementation="eager": PyTorch's MPS SDPA kernel (checked with
+# torch 2.11) mishandles this model's grouped-query attention and degrades the output. CUDA and CPU are fine.
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")
+
+messages = [{"role": "user", "content": "Find all positive integers n such that n^2 - 19n + 99 is a perfect square."}]
+inputs = tokenizer.apply_chat_template(
+    messages, tokenize=True, add_generation_prompt=True, enable_thinking=True,
+    return_dict=True, return_tensors="pt",
+).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=8192, do_sample=True, temperature=0.9, top_p=0.95)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+### Prompting tips
+
+- For problems with a final answer, end the prompt with *"Please reason step by step, and put your final answer
+  within \boxed{}."*
+- For proofs, ask explicitly for a complete and rigorous proof, and to prove both directions when the problem asks
+  you to "find all" or "determine".
+- With thinking enabled, the reasoning ends with `</think>`, followed by the final answer. Servers with a reasoning
+  parser return the two parts separately; otherwise split on `</think>`.
+
+## License
+
+The model weights are released under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) license (see
+`LICENSE`), the same license as the base model,
+[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B).