MiniCPM
Collection
The MiniCPM family of LLMs and VLLMs. • 33 items • Updated • 77
How to use openbmb/MiniCPM-2B-sft-int4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="openbmb/MiniCPM-2B-sft-int4", 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/MiniCPM-2B-sft-int4", trust_remote_code=True, dtype="auto")How to use openbmb/MiniCPM-2B-sft-int4 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM-2B-sft-int4"
# 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/MiniCPM-2B-sft-int4",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/openbmb/MiniCPM-2B-sft-int4
How to use openbmb/MiniCPM-2B-sft-int4 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "openbmb/MiniCPM-2B-sft-int4" \
--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/MiniCPM-2B-sft-int4",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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/MiniCPM-2B-sft-int4" \
--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/MiniCPM-2B-sft-int4",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use openbmb/MiniCPM-2B-sft-int4 with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM-2B-sft-int4
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/MiniCPM-2B-sft-int4" \
--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/MiniCPM-2B-sft-int4",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'
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/MiniCPM-2B-sft-int4" \ --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/MiniCPM-2B-sft-int4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'