Instructions to use openbmb/MiniCPM5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM5-2B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-2B")
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-2B", 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 openbmb/MiniCPM5-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM5-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM5-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM5-2B

SGLang

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

Docker Model Runner
How to use openbmb/MiniCPM5-2B with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM5-2B
```

BigDong commited on 15 days ago

Commit

aac3680

1 Parent(s): b04e640

update README

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README.md +2 -2

README.md CHANGED Viewed

@@ -155,7 +155,7 @@ We are releasing **MiniCPM5-2B**, the second model in the **MiniCPM5** series, f
   </svg>
 </div>
-📂 **Open High-Quality Data**: Alongside the model, we are releasing the high-quality training datasets behind it as part of the [UltraData](https://ultradata.openbmb.cn/) family: [UltraX](https://huggingface.co/datasets/openbmb/UltraX-Preview), a high-quality web pre-training dataset; [UltraData-Code](https://huggingface.co/datasets/openbmb/UltraData-Code), featuring L0–L3 tiered code data governance to drive a significant leap in coding capabilities; [UltraData-SFT-Agent-2609](https://huggingface.co/datasets/openbmb/UltraData-SFT-Agent-2609), comprising 500K agent training samples to enhance comprehensive on-device agent capabilities; and [UltraData-RL-2609](https://huggingface.co/datasets/openbmb/UltraData-RL-2609), with 80K+ high-quality RL training samples covering mathematics, code, general knowledge, and long-context reasoning.
 ## Model List
@@ -263,7 +263,7 @@ The training of MiniCPM5-2B is a full-stack practice of **[UltraData Tiered Data
 During **base training**, the model goes through stable training and decay training to build core language capability and training stability. It then enters **mid-training** to further strengthen target capabilities and adapt to the target data distribution. The training corpus is released alongside the model as [Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb), [Ultra-FineWeb-L3](https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3), [UltraX](https://huggingface.co/datasets/openbmb/UltraX-Preview), [UltraData-Code](https://huggingface.co/datasets/openbmb/UltraData-Code) and [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math).
-During **post-training**, we proceed in three steps: **SFT**, **RL**, and **OPD**. We first use **400B tokens of deep-thinking SFT** to establish deep-thinking and general chat abilities; the SFT data is released as [UltraData-SFT-2605](https://huggingface.co/datasets/openbmb/UltraData-SFT-2605). We then train specialized **RL teachers** for math, code, agentic tasks, writing, and related domains(with the corresponding data also open-sourced as [UltraData-RL-2609](https://huggingface.co/datasets/openbmb/UltraData-RL-2609)), and use **On-Policy Distillation (OPD)** to distill these teachers back into one release model.
 ![MiniCPM5-2B Training Recipe](https://raw.githubusercontent.com/OpenBMB/MiniCPM/minicpm5-2b/assets/minicpm5/minicpm5_2b_training_recipe.jpg)

   </svg>
 </div>
+📂 **Open High-Quality Data**: Alongside the model, we are releasing the high-quality training datasets behind it as part of the [UltraData](https://ultradata.openbmb.cn/) family: [UltraX](https://huggingface.co/datasets/openbmb/UltraX-Preview), a high-quality web pre-training dataset; [UltraData-Code](https://huggingface.co/datasets/openbmb/UltraData-Code), featuring L0–L3 tiered code data management to drive a significant leap in coding capabilities; [UltraData-SFT-Agent-2609](https://huggingface.co/datasets/openbmb/UltraData-SFT-Agent-2609), comprising 500K agent training samples to enhance comprehensive on-device agent capabilities; and [UltraData-RL-2609](https://huggingface.co/datasets/openbmb/UltraData-RL-2609), with 80K+ high-quality RL training samples covering mathematics, code, general knowledge, and long-context reasoning.
 ## Model List
 During **base training**, the model goes through stable training and decay training to build core language capability and training stability. It then enters **mid-training** to further strengthen target capabilities and adapt to the target data distribution. The training corpus is released alongside the model as [Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb), [Ultra-FineWeb-L3](https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3), [UltraX](https://huggingface.co/datasets/openbmb/UltraX-Preview), [UltraData-Code](https://huggingface.co/datasets/openbmb/UltraData-Code) and [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math).
+During **post-training**, we proceed in three steps: **SFT**, **RL**, and **OPD**. We first use **400B tokens of deep-thinking SFT** to establish deep-thinking and general chat abilities; the SFT data is released as [UltraData-SFT-2605](https://huggingface.co/datasets/openbmb/UltraData-SFT-2605) and the Agent SFT data is released as [UltraData-SFT-Agent-2609](https://huggingface.co/datasets/openbmb/UltraData-SFT-Agent-2609). We then train specialized **RL teachers** for math, code, agentic tasks, writing, and related domains (with the corresponding data also open-sourced as [UltraData-RL-2609](https://huggingface.co/datasets/openbmb/UltraData-RL-2609)), and use **On-Policy Distillation (OPD)** to distill these teachers back into one release model.
 ![MiniCPM5-2B Training Recipe](https://raw.githubusercontent.com/OpenBMB/MiniCPM/minicpm5-2b/assets/minicpm5/minicpm5_2b_training_recipe.jpg)