Instructions to use openbmb/MiniCPM5-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM5-1B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-1B")
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B")
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

vLLM

How to use openbmb/MiniCPM5-1B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/openbmb/MiniCPM5-1B

SGLang

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

Docker Model Runner
How to use openbmb/MiniCPM5-1B with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM5-1B
```

Hindi fine-tune of MiniCPM5-1B now available + GGUF quants

by pankajpandey-dev - opened about 5 hours ago

Discussion

pankajpandey-dev

about 5 hours ago

Hi @openbmb team and community! 👋

Thanks for releasing MiniCPM5-1B — the tokenizer handles Devanagari beautifully (0.81 tokens/char on Hindi text) and the model is the perfect size for low-resource Indic adaptation.

I've released a Hindi instruction-tuned version trained on AI4Bharat's indic-instruct-data-v0.1 (anudesh + dolly Hindi splits, ~4k high-quality examples):

🔗 HF Model: https://huggingface.co/pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct
🔗 GGUF Quants (Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0): https://huggingface.co/pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct-v1-GGUF

Training stack: Unsloth + TRL + LoRA (r=32), 60 min on a single T4. Full details on the model card.

One note for the llama.cpp folks: the BPE pre-tokenizer hash isn't in llama.cpp's registry yet — I registered 36f3066e97b7f3994b379aaacde306c1444c6ae84e81a5ae3cd2b7ed3b8c42d4 → qwen2 as the closest match and conversion worked cleanly. Happy to submit a PR to llama.cpp upstream if this is the right pre-tokenizer family for MiniCPM5.

Looking forward to more Indic fine-tunes of this base — thanks again!

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