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
```

OpenBMB-LLM commited on 3 days ago

Commit

9da0a79

verified ·

1 Parent(s): 94c7e15

Add model card README

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README.md +11 -15

README.md CHANGED Viewed

@@ -35,7 +35,7 @@ datasets:
 ## Highlights
-We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA on public evaluations. The model uses the standard `LlamaForCausalLM` architecture, supports native 128K context, and is released in BF16, GGUF, MLX, AWQ, and GPTQ variants.
 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.
@@ -63,23 +63,19 @@ Use this directory to choose the model format that matches your runtime:
 ## Model Information
-| Item | Description |
-| --- | --- |
-| Model name | MiniCPM5-1B |
-| Model type | Dense decoder-only language model |
-| Architecture | Standard `LlamaForCausalLM` |
-| Parameters | ~1.0B total parameters |
-| Layers | 24 Transformer layers |
-| Attention | GQA, 16 query heads / 2 KV heads |
-| Context length | Native 128K context (`max_position_embeddings = 131,072`) |
-| RoPE | `rope_theta = 5e6`, no extra RoPE scaling required |
-| Chat modes | Think / No Think via `enable_thinking` |
-| Main scenarios | Local assistants, coding agents, tool assistants, reasoning assistants, and resource-constrained deployment |
-| License | Apache-2.0 |
 ## Introduction
-MiniCPM5-1B is a compact dense decoder-only Transformer trained to improve output quality at the 1B scale. It keeps the standard `LlamaForCausalLM` architecture (24 layers, GQA 8:1, native 128K context, ~1.0B total params) so it runs on mainstream inference engines (Transformers, vLLM, SGLang, llama.cpp, MLX, Ollama, LM Studio) without custom kernels.
 For full architecture details and per-component parameter breakdown, see the [Transformers deployment cookbook](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/transformers.md).

 ## Highlights
+We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA on the benchmark suite.
 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.
 ## Model Information
+MiniCPM5-1B has the following features:
+- **Type**: Causal Language Model
+- **Architecture**: Standard `LlamaForCausalLM`
+- **Number of Parameters**: 1,080,632,832
+- **Number of Non-Embedding Parameters**: 679,552,512
+- **Number of Layers**: 24
+- **Number of Attention Heads (GQA)**: 16 for Q and 2 for KV
+- **Context Length**: 131,072
 ## Introduction
+MiniCPM5-1B is a compact dense decoder-only Transformer trained to improve output quality at the 1B scale. It keeps the standard `LlamaForCausalLM` architecture (24 layers, GQA 8:1, native 128K context, 1,080,632,832 parameters) so it runs on mainstream inference engines (Transformers, vLLM, SGLang, llama.cpp, MLX, Ollama, LM Studio) without custom kernels.
 For full architecture details and per-component parameter breakdown, see the [Transformers deployment cookbook](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/transformers.md).