Instructions to use GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic with Transformers:

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

pipe = pipeline("text-generation", model="GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic")
model = AutoModelForCausalLM.from_pretrained("GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic", device_map="auto")
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/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic"
# 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/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic

SGLang

How to use GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic 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/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic" \
    --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/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic",
		"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/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic" \
        --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/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic with Docker Model Runner:
```
docker model run hf.co/GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic
```

MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic / README.md

GnLOLot

Initial release: MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic

934bcdf verified 10 days ago

preview code

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	---
	library_name: transformers
	license: apache-2.0
	language:
	- en
	- zh
	base_model: openbmb/MiniCPM5-2B
	base_model_relation: finetune
	pipeline_tag: text-generation
	tags:
	- minicpm
	- minicpm5
	- llama
	- text-generation
	- thinking
	- fable5
	- tool-calling
	- function-calling
	- agentic
	- coding
	- instruction-following
	- conversational
	---

	<p align="center">
	<img src="assets/banner.png" alt="MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic" width="100%"/>
	</p>

	# MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic

	GGUF quantizations for local deployment: [MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-GGUF](https://huggingface.co/GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-GGUF)

	MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic is a compact 2B Thinking language model built on [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B). Fine-tuned on Claude data with a strong focus on agentic tool calling / function calling, coding, and instruction following. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.

	For llama.cpp / Ollama / LM Studio deployment, see the [GGUF repository](https://huggingface.co/GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic-GGUF).

	---

	## Overview

	\| Item \| Detail \|
	\|---\|---\|
	\| Base model \| [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) (2B dense Llama architecture) \|
	\| Post-training \| Claude data \|
	\| Key capabilities \| Agentic tool calling, coding, instruction following, chain-of-thought reasoning \|
	\| Chat format \| MiniCPM5 native Thinking template with optional chain-of-thought blocks \|
	\| Context length \| 128K (`max_position_embeddings = 131072`) \|
	\| Precision \| bfloat16 \|
	\| Deployment \| Single-GPU friendly; suitable for edge / local use \|

	---

	## Capabilities

	- Agentic tool calling — reliable XML / function-calling style tool use on top of MiniCPM5's native format, designed for multi-step agentic workflows
	- Coding — code generation, debugging, and software-engineering-style tasks
	- Instruction following — reliable adherence to user prompts and structured constraints
	- Thinking mode — chain-of-thought reasoning via the MiniCPM5 chat template
	- Long context — up to 128K tokens (131,072 tokens per `config.json`)

	---

	## Benchmark

	### ClawBench (Agentic Coding)

	\| Model \| QwenClawBench \| WildClawBench \|
	\|---\|---\|---\|
	\| MiniCPM5-2B (Base, RL-only) \| 42.11 \| 23.19 \|
	\| MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic \| 44.56 (+2.45) \| 24.32 (+1.13) \|

	> ClawBench evaluates agentic coding ability — the model's capacity to autonomously use tools, navigate codebases, and complete multi-step software engineering tasks. QwenClawBench uses structured coding scenarios; WildClawBench tests on diverse real-world tasks.

	> More benchmarks (BFCL, SWE-bench, Tau-Bench, etc.) coming soon.

	---

	## Quick start

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "GnLOLot/MiniCPM5-2B-Claude-Fable5-1-Thinking-Agentic"

	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": "Write a Python function to merge two sorted lists."}]
	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))
	```

	### Tool calling example

	```python
	tools = [
	{
	"type": "function",
	"function": {
	"name": "get_weather",
	"description": "Get the current weather for a given city.",
	"parameters": {
	"type": "object",
	"properties": {
	"city": {"type": "string", "description": "City name"}
	},
	"required": ["city"]
	}
	}
	}
	]

	messages = [
	{"role": "user", "content": "What's the weather like in Beijing?"}
	]

	text = tokenizer.apply_chat_template(
	messages, tools=tools, tokenize=False, add_generation_prompt=True
	)
	inputs = tokenizer(text, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
	print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	---

	## Sampling recommendations

	Inherited from [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B):

	\| Scenario \| Params \|
	\|---\|---\|
	\| Default \| `temperature=1.0, top_p=0.95, min_p=0.0` \|
	\| If repetitive outputs \| `temperature=1.0, top_p=0.95, min_p=0.0, repetition_penalty=1.05` \|

	This model is Thinking-only — chain-of-thought reasoning is always active.

	> Support for sampling parameters varies across inference frameworks — check your runtime's documentation.

	---

	## Limitations

	- Thinking outputs — the model may emit reasoning blocks before the final answer; downstream apps can strip them before display
	- 2B scale — optimized for lightweight local deployment, not frontier-scale general reasoning

	---

	## Provenance & licensing

	Released under Apache-2.0, inherited from [MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B).

	## Acknowledgements

	- Base model: [OpenBMB / MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B)
	- GGUF conversion: [llama.cpp](https://github.com/ggml-org/llama.cpp)