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 9 days ago

preview code

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metadata

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

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

GGUF quantizations for local deployment: 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. 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.

Overview

Item	Detail
Base model	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

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

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:

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.

Acknowledgements

Base model: OpenBMB / MiniCPM5-2B
GGUF conversion: llama.cpp