运行模型的完整提示词
{% code overflow="wrap" %}
```bash
./llama.cpp/llama-cli \\
--model unsloth/DeepSeek-R1-0528-GGUF/UD-IQ1_S/DeepSeek-R1-0528-UD-IQ1_S-00001-of-00004.gguf \
--cache-type-k q4_0 \
--threads -1 \\
--n-gpu-layers 99 \
--prio 3 \
--temp 0.6 \\
--top_p 0.95 \
--min_p 0.01 \
--ctx-size 16384 \
--seed 3407 \
-ot ".ffn_.*_exps.=CPU" \\
-no-cnv \
--prompt "<｜User｜>编写一个 Python 程序，在旋转的七边形中显示 20 个弹跳的球：\n- 所有球的半径相同。\n- 所有球上都有 1 到 20 的编号。\n- 所有球在开始时都从七边形中心下落。\n- 颜色如下：#f8b862, #f6ad49, #f39800, #f08300, #ec6d51, #ee7948, #ed6d3d, #ec6800, #ec6800, #ee7800, #eb6238, #ea5506, #ea5506, #eb6101, #e49e61, #e45e32, #e17b34, #dd7a56, #db8449, #d66a35\n- 球应受到重力和摩擦影响，并且必须与旋转的墙壁真实地反弹。球与球之间也应发生碰撞。\n- 所有球的材质决定了它们的碰撞反弹高度不会超过七边形的半径，但要高于球半径。\n- 所有球都带有摩擦旋转，球上的数字可用于表示球的自旋。\n- 七边形绕其中心旋转，旋转速度为每 5 秒 360 度。\n- 七边形大小应足以容纳所有球。\n- 不要使用 pygame 库；请自行实现碰撞检测算法、碰撞响应等。允许使用以下 Python 库：tkinter、math、numpy、dataclasses、typing、sys。\n- 所有代码应放在一个 Python 文件中。<｜Assistant｜>"
```
{% endcode %}
## 🦥 使用 Unsloth 微调 DeepSeek-R1-0528
要微调 **DeepSeek-R1-0528-Qwen3-8B** 使用 Unsloth，我们制作了一个新的 GRPO notebook，其中包含一个自定义奖励函数，旨在显著增强多语言输出——特别是将期望语言回复的比例（在我们的示例中使用印尼语，但你可以使用任何语言）提高 40% 以上。
* [**DeepSeek-R1-0528-Qwen3-8B notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) **- 新**
虽然许多推理类 LLM 都具备多语言能力，但它们在推理轨迹中常常会产生混合语言输出，将英语与目标语言混在一起。我们的奖励函数通过强烈鼓励以目标语言输出，有效缓解了这一问题，从而显著提升了语言一致性。
这个奖励函数也完全可定制，允许你将其适配到其他语言，或针对特定领域或用例进行微调。
{% hint style="success" %}
这个奖励函数和 notebook 最棒的一点是，你根本不需要语言数据集来强迫模型学习特定语言。这个 notebook 中没有印尼语数据集。
{% endhint %}
Unsloth 让 R1-Qwen3 蒸馏版微调速度提升 2 倍，显存使用减少 70%，并支持 8 倍更长的上下文长度。
---
# Agent Instructions
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```
GET https://unsloth.ai/docs/zh/mo-xing/tutorials/deepseek-r1-0528-how-to-run-locally.md?ask=&goal=
```
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