Instructions to use openbmb/MiniCPM3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM3-4B with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM3-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM3-4B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM3-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM3-4B"
# 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/MiniCPM3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM3-4B

SGLang

How to use openbmb/MiniCPM3-4B 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/MiniCPM3-4B" \
    --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/MiniCPM3-4B",
		"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/MiniCPM3-4B" \
        --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/MiniCPM3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/MiniCPM3-4B with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM3-4B
```

FrankC0st1e commited on Sep 5, 2024

Commit

f5d31d8

verified ·

1 Parent(s): e618f3a

Update tokenization_minicpm.py

Browse files

Files changed (1) hide show

tokenization_minicpm.py +2 -2

tokenization_minicpm.py CHANGED Viewed

@@ -14,7 +14,7 @@ from datamodel_code_generator.model import get_data_model_types
 from datamodel_code_generator.parser.jsonschema import JsonSchemaParser
 from jsonschema import Draft202012Validator, exceptions, validate
-from transformers import LlamaTokenizer
 from transformers.tokenization_utils_base import BatchEncoding
 from transformers.utils import TensorType
@@ -22,7 +22,7 @@ from transformers.utils import TensorType
 logger = getLogger(__name__)
-class MiniCPMTokenizer(LlamaTokenizer):
     def apply_chat_template(
         self,
         conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],

 from datamodel_code_generator.parser.jsonschema import JsonSchemaParser
 from jsonschema import Draft202012Validator, exceptions, validate
+from transformers import LlamaTokenizerFast
 from transformers.tokenization_utils_base import BatchEncoding
 from transformers.utils import TensorType
 logger = getLogger(__name__)
+class MiniCPMTokenizer(LlamaTokenizerFast):
     def apply_chat_template(
         self,
         conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],