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

Libraries

How to use opsirygi/MiniCPM5-1B-IndoChat with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("opsirygi/MiniCPM5-1B-IndoChat")
model = AutoModelForCausalLM.from_pretrained("opsirygi/MiniCPM5-1B-IndoChat", 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 opsirygi/MiniCPM5-1B-IndoChat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/opsirygi/MiniCPM5-1B-IndoChat

SGLang

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

Unsloth Desktop
Docker Model Runner
How to use opsirygi/MiniCPM5-1B-IndoChat with Docker Model Runner:
```
docker model run hf.co/opsirygi/MiniCPM5-1B-IndoChat
```

MiniCPM5-1B-IndoChat / tokenizer.json

Commit History

(Trained with Unsloth)

0c754d8
verified

opsi ryougi commited on 22 days ago

(Trained with Unsloth)

0c63760
verified

opsi ryougi commited on 22 days ago

(Trained with Unsloth)

5c470d5
verified

opsi ryougi commited on 22 days ago

Commit History

(Trained with Unsloth) 0c754d8 verified

(Trained with Unsloth) 0c63760 verified

(Trained with Unsloth) 5c470d5 verified

(Trained with Unsloth)

0c754d8
verified

(Trained with Unsloth)

0c63760
verified

(Trained with Unsloth)

5c470d5
verified