Instructions to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking")
model = AutoModelForCausalLM.from_pretrained("GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking

SGLang

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking with Docker Model Runner:
```
docker model run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
```

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking / README-cn.md

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metadata

library_name: transformers
license: apache-2.0
language:
  - en
  - zh
base_model: openbmb/MiniCPM5-1B
base_model_relation: finetune
pipeline_tag: text-generation
tags:
  - minicpm
  - minicpm5
  - thinking
  - fable5
  - tool-calling
  - function-calling
  - coding
  - instruction-following

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking

GGUF 量化版：**MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF**

English README

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking 是基于 openbmb/MiniCPM5-1B 的 1B Thinking 语言模型。相对 V1，本 V2 版本在 Fable 5 数据上进一步微调，重点增强了 工具调用（Tool Calling / Function Calling），同时继续提升 Coding 与 指令遵循，并保留 MiniCPM5 原生 Thinking 对话模板与 XML 工具调用格式。

上一版本：**MiniCPM5-1B-Claude-Opus-Fable5-Thinking**（V1）

llama.cpp / Ollama / LM Studio 部署请参阅 **GGUF 仓库**。

模型概述

项目	说明
基座模型	openbmb/MiniCPM5-1B（1B 稠密 Llama 架构）
后训练数据	Fable 5 traces（V2）
主要提升（相对 V1 / 基座）	更强的工具调用，以及更好的 Coding 与指令遵循
对话格式	MiniCPM5 原生 Thinking 模板，支持可选的思维链推理块
上下文长度	128K（`max_position_embeddings = 131072`）
部署特点	单卡友好，适合边缘 / 本地场景

能力

工具调用（V2 增强） — 在 MiniCPM5 原生 XML / Function Calling 格式上更稳定可靠
Coding — 代码生成、调试及软件工程类任务
Instruction Following — 更稳定地遵循用户提示与结构化任务约束
Thinking 模式 — 通过 MiniCPM5 对话模板进行思维链推理
长上下文 — 最高 128K tokens（config.json 中为 131,072）

Benchmark

BFCL + API-Bank

模型	BFCL non_live	BFCL live	API-Bank
MiniCPM5-1B（基座）	41.51%	60.24%	7.30%
MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking	43.06%	63.33%	22.10%

Tau-Bench

域	MiniCPM5-1B（基座）	MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
Airline	0.34 (17/50)	0.36 (18/50)
Retail	0.052 (6/115)	0.070 (8/115)

快速开始

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True,
    torch_dtype=torch.bfloat16, device_map="auto",
)

messages = [{"role": "user", "content": "写一个 Python 函数，合并两个有序链表。"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

采样建议

生成参数继承自 **MiniCPM5-1B**：

模式	参数
Think（默认）	`temperature=0.9, top_p=0.95`
No Think	`temperature=0.7, top_p=0.95`，`enable_thinking=False`

局限性

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
1B 体量 — 面向轻量本地部署，非前沿规模通用推理模型

许可与致谢

许可证：Apache-2.0（继承自 MiniCPM5-1B）
基座：OpenBMB / MiniCPM5-1B
GGUF：llama.cpp