Instructions to use eshiryae/MiniCPM4-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eshiryae/MiniCPM4-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="eshiryae/MiniCPM4-0.5B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("eshiryae/MiniCPM4-0.5B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eshiryae/MiniCPM4-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eshiryae/MiniCPM4-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eshiryae/MiniCPM4-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eshiryae/MiniCPM4-0.5B

SGLang

How to use eshiryae/MiniCPM4-0.5B 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 "eshiryae/MiniCPM4-0.5B" \
    --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": "eshiryae/MiniCPM4-0.5B",
		"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 "eshiryae/MiniCPM4-0.5B" \
        --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": "eshiryae/MiniCPM4-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eshiryae/MiniCPM4-0.5B with Docker Model Runner:
```
docker model run hf.co/eshiryae/MiniCPM4-0.5B
```

BigDong commited on Jun 5, 2025

Commit

17a5a30

1 Parent(s): a2f4ede

update configuration_minicpm.py

Browse files

Files changed (1) hide show

configuration_minicpm.py +3 -0

configuration_minicpm.py CHANGED Viewed

@@ -137,6 +137,7 @@ class MiniCPMConfig(PretrainedConfig):
         scale_emb=1,
         dim_model_base=1,
         scale_depth=1,
         sparse_config=None,
         **kwargs):
@@ -165,6 +166,8 @@ class MiniCPMConfig(PretrainedConfig):
         self.scale_emb = scale_emb
         self.dim_model_base = dim_model_base
         self.scale_depth = scale_depth
         # sparse config
         self.sparse_config = sparse_config

         scale_emb=1,
         dim_model_base=1,
         scale_depth=1,
+        mup_denominator=None,
         sparse_config=None,
         **kwargs):
         self.scale_emb = scale_emb
         self.dim_model_base = dim_model_base
         self.scale_depth = scale_depth
+        # only used for Eagle Head
+        self.mup_denominator = mup_denominator
         # sparse config
         self.sparse_config = sparse_config