Instructions to use Jeethu/MiniCPM5-1B-PARO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jeethu/MiniCPM5-1B-PARO with Transformers:

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

pipe = pipeline("text-generation", model="Jeethu/MiniCPM5-1B-PARO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jeethu/MiniCPM5-1B-PARO")
model = AutoModelForCausalLM.from_pretrained("Jeethu/MiniCPM5-1B-PARO", 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
Local Apps Settings

vLLM

How to use Jeethu/MiniCPM5-1B-PARO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jeethu/MiniCPM5-1B-PARO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jeethu/MiniCPM5-1B-PARO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jeethu/MiniCPM5-1B-PARO

SGLang

How to use Jeethu/MiniCPM5-1B-PARO 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 "Jeethu/MiniCPM5-1B-PARO" \
    --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": "Jeethu/MiniCPM5-1B-PARO",
		"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 "Jeethu/MiniCPM5-1B-PARO" \
        --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": "Jeethu/MiniCPM5-1B-PARO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jeethu/MiniCPM5-1B-PARO with Docker Model Runner:
```
docker model run hf.co/Jeethu/MiniCPM5-1B-PARO
```

Jeethu/MiniCPM5-1B-PARO

Pairwise Rotation Quantization for Efficient Reasoning LLM Inference

ParoQuant is the state-of-the-art INT4 quantization for LLMs. It closes the accuracy gap with FP16 while running at near-AWQ speed. Supports NVIDIA GPUs (vLLM, Transformers) and Apple Silicon (MLX). For more information, see https://github.com/z-lab/paroquant.

Jeethu/MiniCPM5-1B-PARO is a 4-bit openbmb/MiniCPM5-1B quantized with ParoQuant.

Downloads last month: 12

Safetensors

Model size

0.5B params

Tensor type

I32

F16

I16

Model tree for Jeethu/MiniCPM5-1B-PARO

Base model

openbmb/MiniCPM5-1B

Quantized

(94)

this model

Datasets used to train Jeethu/MiniCPM5-1B-PARO

Collection including Jeethu/MiniCPM5-1B-PARO

ParoQuant

Collection

Pairwise Rotation Quantization for Efficient Reasoning LLM Inference • 18 items • Updated 20 days ago

Paper for Jeethu/MiniCPM5-1B-PARO

ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference

Paper • 2511.10645 • Published Nov 13, 2025 • 14