104 lines
3.5 KiB
Markdown
104 lines
3.5 KiB
Markdown
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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- zh
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base_model: openbmb/MiniCPM5-1B
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base_model_relation: finetune
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pipeline_tag: text-generation
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tags:
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- minicpm
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- minicpm5
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- thinking
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- fable5
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- coding
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- instruction-following
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---
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<p align="center">
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<img src="assets/banner.png" alt="MiniCPM5-1B-Claude-Opus-Fable5-Thinking" width="100%"/>
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</p>
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# MiniCPM5-1B-Claude-Opus-Fable5-Thinking
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GGUF 量化版：**[MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF)**
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[English README](./README.md)
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**MiniCPM5-1B-Claude-Opus-Fable5-Thinking** 是基于 [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) 的 1B **Thinking** 语言模型。该模型使用 **Fable 5** 数据进一步微调，增强了 **Coding（编程）** 与 **指令遵循（Instruction Following）** 能力，同时保留 MiniCPM5 原生的 Thinking 对话模板与工具调用格式。
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llama.cpp / Ollama / LM Studio 部署请参阅 **[GGUF 仓库](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF)**。
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---
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## 模型概述
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| 项目 | 说明 |
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|---|---|
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| **基座模型** | [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)（1B 稠密 Llama 架构） |
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| **后训练数据** | Fable 5 traces |
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| **主要提升** | 相较基座，Coding 与指令遵循能力更强 |
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| **对话格式** | MiniCPM5 原生 Thinking 模板，支持可选的思维链推理块 |
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| **上下文长度** | **128K**（`max_position_embeddings = 131072`） |
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| **部署特点** | 单卡友好，适合边缘 / 本地场景 |
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---
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## 能力
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- **Coding** — 代码生成、调试及软件工程类任务
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- **Instruction Following** — 更稳定地遵循用户提示与结构化任务约束
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- **Thinking 模式** — 通过 MiniCPM5 对话模板进行思维链推理
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- **工具调用** — 继承 MiniCPM5 的 XML 工具调用格式
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- **长上下文** — 最高 **128K tokens**（`config.json` 中为 131,072）
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---
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## 快速开始
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id, trust_remote_code=True,
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torch_dtype=torch.bfloat16, device_map="auto",
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)
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messages = [{"role": "user", "content": "写一个 Python 函数，合并两个有序链表。"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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---
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## 采样建议
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生成参数继承自 **[MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)**：
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| 模式 | 参数 |
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|---|---|
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| **Think**（默认） | `temperature=0.9, top_p=0.95` |
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| **No Think** | `temperature=0.7, top_p=0.95`，`enable_thinking=False` |
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---
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## 局限性
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- **Thinking 输出** — 模型可能在最终回答前输出推理块；下游应用可在展示前将其剥离
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- **1B 体量** — 面向轻量本地部署，非前沿规模通用推理模型
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---
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## 许可与致谢
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- 许可证：**Apache-2.0**（继承自 MiniCPM5-1B）
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- 基座：[OpenBMB / MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)
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- GGUF：[llama.cpp](https://github.com/ggml-org/llama.cpp)
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