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", 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
AMD Developer Cloud
Local Apps Settings

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 May 24

Commit

b0834b9

verified ·

1 Parent(s): 2a83ad0

Update Chinese model card (README-cn.md)

Browse files

Files changed (1) hide show

README-cn.md +4 -4

README-cn.md CHANGED Viewed

@@ -15,7 +15,9 @@ tags:
   - on-device
   - edge-ai
 datasets:
   - openbmb/Ultra-FineWeb-L3
   - openbmb/UltraData-SFT-2605
 ---
@@ -59,8 +61,6 @@ datasets:
 - **[MiniCPM5-1B-Base](https://huggingface.co/openbmb/MiniCPM5-1B-Base)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-Base) · BF16 base checkpoint（仅预训练）
 - **[MiniCPM5-1B-GGUF](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-GGUF) · GGUF，适用于 llama.cpp / Ollama / LM Studio
 - **[MiniCPM5-1B-MLX](https://huggingface.co/openbmb/MiniCPM5-1B-MLX)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-MLX) · MLX / 4bit，适用于 Apple Silicon
-- **[MiniCPM5-1B-AWQ](https://huggingface.co/openbmb/MiniCPM5-1B-AWQ)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-AWQ) · AWQ-Marlin Int4，适用于 vLLM
-- **[MiniCPM5-1B-GPTQ](https://huggingface.co/openbmb/MiniCPM5-1B-GPTQ)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-GPTQ) · GPTQ-Marlin Int4，适用于 vLLM
 ## 模型信息
@@ -88,7 +88,7 @@ MiniCPM5-1B 是 MiniCPM5 系列的首个模型，面向本地助手、coding age
 MiniCPM5-1B 的训练过程是 **[UltraData 分级数据管理体系](https://ultradata.openbmb.cn/)** 的一次完整实践，覆盖 base training、mid-training 与后训练三个阶段。
-**Base training** 采用逐级推进的训练配方，包含 stable training 与 decay training，用于建立基础语言能力与训练稳定性。随后进入 **mid-training**，进一步强化目标能力并适配数据分布。训练语料来自我们同步开源的 [Ultra-FineWeb-L3](https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3)。
 **后训练阶段**分为 **SFT**、**RL** 与 **OPD** 三步。我们先使用 **200B tokens deep-thinking SFT** 与 **200B tokens hybrid-thinking SFT** 建立深度思考、混合思考和通用对话能力，相关 SFT 数据已同步开源为 [UltraData-SFT-2605](https://huggingface.co/datasets/openbmb/UltraData-SFT-2605)。随后针对数学、代码、闭卷问答和写作等方向训练专用 **RL teacher**，并通过 **On-Policy Distillation (OPD)** 将这些 teacher 的能力蒸馏回同一个发布模型。
@@ -199,7 +199,7 @@ MiniCPM5-1B 使用**标准 `LlamaForCausalLM` 架构**，主流推理引擎可
 | 后端 / 框架 | 模型格式 / 适用场景 | Cookbook | Agent Skill |
 | --- | --- | --- | --- |
 | Transformers | BF16 / FP16，本地 Python 推理，GPU + CPU | [transformers.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/transformers.md) | [minicpm5-deploy-transformers](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-transformers/SKILL.md) |
-| vLLM | BF16 / FP16 OpenAI server；支持 AWQ / GPTQ-Marlin Int4 量化权重 | [vllm.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/vllm.md)；量化：[awq.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/awq.md) / [gptq.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/gptq.md) | [minicpm5-deploy-vllm](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-vllm/SKILL.md)；量化：[awq](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-awq/SKILL.md) / [gptq](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-gptq/SKILL.md) |
 | SGLang | BF16 / FP16 OpenAI server，推荐用于 tool calling | [sglang.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/sglang.md) | [minicpm5-deploy-sglang](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-sglang/SKILL.md) |
 | llama.cpp | GGUF，CPU/GPU 本地推理 | [llama_cpp.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/llama_cpp.md) | [minicpm5-deploy-llama-cpp](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-llama-cpp/SKILL.md) |
 | Ollama | GGUF，本地端侧运行 | [ollama.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/ollama.md) | [minicpm5-deploy-ollama](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-ollama/SKILL.md) |

   - on-device
   - edge-ai
 datasets:
+  - openbmb/Ultra-FineWeb
   - openbmb/Ultra-FineWeb-L3
+  - openbmb/UltraData-Math
   - openbmb/UltraData-SFT-2605
 ---
 - **[MiniCPM5-1B-Base](https://huggingface.co/openbmb/MiniCPM5-1B-Base)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-Base) · BF16 base checkpoint（仅预训练）
 - **[MiniCPM5-1B-GGUF](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-GGUF) · GGUF，适用于 llama.cpp / Ollama / LM Studio
 - **[MiniCPM5-1B-MLX](https://huggingface.co/openbmb/MiniCPM5-1B-MLX)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-MLX) · MLX / 4bit，适用于 Apple Silicon
 ## 模型信息
 MiniCPM5-1B 的训练过程是 **[UltraData 分级数据管理体系](https://ultradata.openbmb.cn/)** 的一次完整实践，覆盖 base training、mid-training 与后训练三个阶段。
+**Base training** 采用逐级推进的训练配方，包含 stable training 与 decay training，用于建立基础语言能力与训练稳定性。随后进入 **mid-training**，进一步强化目标能力并适配数据分布。训练语料来自我们同步开源的 [Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)、[Ultra-FineWeb-L3](https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3) 与 [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math)。
 **后训练阶段**分为 **SFT**、**RL** 与 **OPD** 三步。我们先使用 **200B tokens deep-thinking SFT** 与 **200B tokens hybrid-thinking SFT** 建立深度思考、混合思考和通用对话能力，相关 SFT 数据已同步开源为 [UltraData-SFT-2605](https://huggingface.co/datasets/openbmb/UltraData-SFT-2605)。随后针对数学、代码、闭卷问答和写作等方向训练专用 **RL teacher**，并通过 **On-Policy Distillation (OPD)** 将这些 teacher 的能力蒸馏回同一个发布模型。
 | 后端 / 框架 | 模型格式 / 适用场景 | Cookbook | Agent Skill |
 | --- | --- | --- | --- |
 | Transformers | BF16 / FP16，本地 Python 推理，GPU + CPU | [transformers.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/transformers.md) | [minicpm5-deploy-transformers](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-transformers/SKILL.md) |
+| vLLM | BF16 / FP16 OpenAI server | [vllm.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/vllm.md) | [minicpm5-deploy-vllm](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-vllm/SKILL.md) |
 | SGLang | BF16 / FP16 OpenAI server，推荐用于 tool calling | [sglang.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/sglang.md) | [minicpm5-deploy-sglang](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-sglang/SKILL.md) |
 | llama.cpp | GGUF，CPU/GPU 本地推理 | [llama_cpp.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/llama_cpp.md) | [minicpm5-deploy-llama-cpp](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-llama-cpp/SKILL.md) |
 | Ollama | GGUF，本地端侧运行 | [ollama.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/ollama.md) | [minicpm5-deploy-ollama](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-ollama/SKILL.md) |