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Model: GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking Source: Original Platform
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README-cn.md
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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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110
README.md
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110
README.md
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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
|
||||
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
|
||||
- llama
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- text-generation
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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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||||
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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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|
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# MiniCPM5-1B-Claude-Opus-Fable5-Thinking
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GGUF quantizations for local deployment: **[MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF)**
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[中文说明](./README-cn.md)
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**MiniCPM5-1B-Claude-Opus-Fable5-Thinking** is a compact 1B **Thinking** language model built on [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B). It is further fine-tuned on **Fable 5** data to improve **coding** and **instruction-following** while keeping MiniCPM5's native Thinking chat template and tool-call format.
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For llama.cpp / Ollama / LM Studio deployment, see the **[GGUF repository](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF)**.
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---
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## Overview
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| Item | Detail |
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|---|---|
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| **Base model** | [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) (1B dense Llama architecture) |
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| **Post-training** | Fable 5 traces |
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| **Key gains** | Stronger coding and instruction following vs. the base checkpoint |
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| **Chat format** | MiniCPM5 native Thinking template with optional chain-of-thought blocks |
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| **Context length** | **128K** (`max_position_embeddings = 131072`) |
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| **Deployment** | Single-GPU friendly; suitable for edge / local use |
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---
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## Capabilities
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- **Coding** — code generation, debugging, and software-engineering-style tasks
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- **Instruction following** — more reliable adherence to user prompts and structured constraints
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- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
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- **Tool calling** — inherits MiniCPM5's XML tool-call format
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- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
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---
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## Quick start
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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,
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
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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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## Sampling recommendations
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Generation defaults are inherited from **[MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)**:
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| Mode | Params |
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|---|---|
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| **Think** (default) | `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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## Limitations
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- **Thinking outputs** — the model may emit reasoning blocks before the final answer; downstream apps can strip them before display
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- **1B scale** — optimized for lightweight local deployment, not frontier-scale general reasoning
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---
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## Provenance & licensing
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Released under **Apache-2.0**, inherited from [MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B).
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## Acknowledgements
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- Base model: [OpenBMB / MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)
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- GGUF conversion: [llama.cpp](https://github.com/ggml-org/llama.cpp)
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{{- bos_token }}{%- if tools %}
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{%- set tool_definitions %}
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{{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson(ensure_ascii=False) }}
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{%- endfor %}
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{{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\n- When calling a function, return an XML object within <function ... </function> using:\n<function name="function-name"><param name="param-name">param-value</param></function>\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name="param-name"><![CDATA[...multi-line value...]]></param>' }}
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{%- endset %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{%- if '<tool_def_sep>' in messages[0].content %}
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{{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
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{%- else %}
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{{- messages[0].content + '\n\n' + tool_definitions }}
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{%- endif %}
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{%- else %}
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{{- tool_definitions.lstrip() }}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- set content_parts = content.split('<tool_sep>') %}
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{%- set processed_content = content_parts[0] %}
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{%- set tool_calls_count = message.tool_calls|length %}
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{%- set tool_sep_count = content_parts|length - 1 %}
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{%- set min_count = [tool_calls_count, tool_sep_count]|min %}
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{%- for i in range(1, content_parts|length) %}
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{%- set tool_index = i - 1 %}
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{%- if tool_index < tool_calls_count %}
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{%- set tool_call = message.tool_calls[tool_index] %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{%- set single_tool_xml %}
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{{- '<function name="' ~ tool_call.name ~ '">' }}
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{%- if tool_call.arguments %}
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{%- set args_dict = tool_call.arguments %}
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{%- for param_name, param_value in args_dict.items() %}
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{{- '<param name="' ~ param_name ~ '">' }}
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{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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{{- '<![CDATA[' + param_value + ']]>' }}
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{%- else %}
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{{- param_value }}
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{%- endif %}
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{{- '</param>' }}
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{%- endfor %}
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{%- endif %}
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{{- '</function>' }}
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{%- endset %}
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{%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
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{%- else %}
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{%- set processed_content = processed_content + content_parts[i] %}
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{%- endif %}
|
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{%- endfor %}
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{%- if tool_calls_count > tool_sep_count %}
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{%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
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{%- set tool_call = message.tool_calls[remaining_index] %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{%- set remaining_tool_xml %}
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{{- '<function name="' ~ tool_call.name ~ '">' }}
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{%- if tool_call.arguments %}
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{%- set args_dict = tool_call.arguments %}
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{%- for param_name, param_value in args_dict.items() %}
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{{- '<param name="' ~ param_name ~ '">' }}
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{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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{{- '<![CDATA[' + param_value + ']]>' }}
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{%- else %}
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{{- param_value }}
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{%- endif %}
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{{- '</param>' }}
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{%- endfor %}
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{%- endif %}
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{{- '</function>' }}
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{%- endset %}
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{%- set processed_content = processed_content + remaining_tool_xml %}
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{%- endfor %}
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{%- endif %}
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|
||||
{%- set content = processed_content %}
|
||||
{%- endif %}
|
||||
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if reasoning_content %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
|
||||
{%- if message.tool_calls and not has_tool_sep %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<function name="' ~ tool_call.name ~ '">' }}
|
||||
{%- if tool_call.arguments %}
|
||||
{%- set args_dict = tool_call.arguments %}
|
||||
{%- for param_name, param_value in args_dict.items() %}
|
||||
{{- '<param name="' ~ param_name ~ '">' }}
|
||||
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
|
||||
{{- '<![CDATA[' + param_value + ']]>' }}
|
||||
{%- else %}
|
||||
{{- param_value }}
|
||||
{%- endif %}
|
||||
{{- '</param>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '</function>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{%- if message.content is string %}
|
||||
{{- content }}
|
||||
{%- else %}
|
||||
{{- message.content | tojson(ensure_ascii=False) }}
|
||||
{%- endif %}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined %}
|
||||
{%- if enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- elif enable_thinking is true %}
|
||||
{{- '<think>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
35
config.json
Normal file
35
config.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 0,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": [
|
||||
1,
|
||||
130073
|
||||
],
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1536,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 4608,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 24,
|
||||
"num_key_value_heads": 2,
|
||||
"pad_token_id": 1,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 5000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "5.6.0",
|
||||
"use_cache": true,
|
||||
"vocab_size": 130560
|
||||
}
|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 0,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
1,
|
||||
130073
|
||||
],
|
||||
"pad_token_id": 1,
|
||||
"temperature": 0.9,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "5.6.0"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:689e9f7eb54a22c91b02e57bb2af884693184db963845cffcae373df47f9b178
|
||||
size 2161290944
|
||||
653947
tokenizer.json
Normal file
653947
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
22
tokenizer_config.json
Normal file
22
tokenizer_config.json
Normal file
@@ -0,0 +1,22 @@
|
||||
{
|
||||
"add_prefix_space": null,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"extra_special_tokens": [
|
||||
"<|im_end|>"
|
||||
],
|
||||
"is_local": true,
|
||||
"legacy": true,
|
||||
"local_files_only": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "</s>",
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "TokenizersBackend",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user