Instructions to use IRedDragonICY/LaguQA-MiniCPM5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IRedDragonICY/LaguQA-MiniCPM5-2B with Transformers:

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

pipe = pipeline("text-generation", model="IRedDragonICY/LaguQA-MiniCPM5-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IRedDragonICY/LaguQA-MiniCPM5-2B")
model = AutoModelForCausalLM.from_pretrained("IRedDragonICY/LaguQA-MiniCPM5-2B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IRedDragonICY/LaguQA-MiniCPM5-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IRedDragonICY/LaguQA-MiniCPM5-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IRedDragonICY/LaguQA-MiniCPM5-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IRedDragonICY/LaguQA-MiniCPM5-2B

SGLang

How to use IRedDragonICY/LaguQA-MiniCPM5-2B 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 "IRedDragonICY/LaguQA-MiniCPM5-2B" \
    --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": "IRedDragonICY/LaguQA-MiniCPM5-2B",
		"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 "IRedDragonICY/LaguQA-MiniCPM5-2B" \
        --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": "IRedDragonICY/LaguQA-MiniCPM5-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IRedDragonICY/LaguQA-MiniCPM5-2B with Docker Model Runner:
```
docker model run hf.co/IRedDragonICY/LaguQA-MiniCPM5-2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LaguQA-MiniCPM5-2B

openbmb/MiniCPM5-2B with the LaguQA-MiniCPM5-2B-LoRA adapter merged into its weights, so it loads with plain transformers, vLLM, or any tool that does not read PEFT adapters. Training, results, and limitations are documented on the adapter's card; this card covers what is specific to the merged copy.

Baca dalam bahasa Indonesia


Adapter only	IRedDragonICY/LaguQA-MiniCPM5-2B-LoRA
Demo	IRedDragonICY/LaguQA-Demo
Dataset	IRedDragonICY/LaguQA
Code	github.com/IRedDragonICY/LaguQA
Collection	IRedDragonICY/laguqa

How it was merged

PeftModel.merge_and_unload() on CPU in bfloat16 (torch 2.13.0, transformers 5.16.1, peft 0.20.0). The parameter count is unchanged at 2,516,756,480. The sha256 of the merged adapter and the base revision are recorded in asal-gabungan.json.

Merging in bfloat16 rounds each weight once more, so the merged model is not bit-identical to base plus adapter. On the 1,200 LaguQA multiple-choice questions it picks the same option on 1,187 (98.9%); the rest are near ties. It answers 445 correctly against 443 for base plus adapter, both scored with the template default.

Usage

Build prompts with enable_thinking=False, the format the adapter was trained on (see the adapter card for why this matters).

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "IRedDragonICY/LaguQA-MiniCPM5-2B"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16")

pesan = [{"role": "system", "content":
           "Kamu asisten yang menguasai lagu nasional dan lagu daerah "
           "Indonesia, termasuk notasi angka dan notasi ABC-nya."},
         {"role": "user", "content": "Siapa pencipta lagu Gugur Bunga?"}]
teks = tok.apply_chat_template(pesan, tokenize=False, add_generation_prompt=True,
                               enable_thinking=False)
keluar = model.generate(**tok(teks, return_tensors="pt").to(model.device),
                        max_new_tokens=192, do_sample=False)
print(tok.decode(keluar[0], skip_special_tokens=True))

Results in short

LaguQA multiple choice, one seed: 24.3 before training and 51.0 after with enable_thinking=False (24.2 and 36.9 with the template default). IndoMMLU falls from 33.6 to 27.2 with enable_thinking=False, so general Indonesian ability is weaker than in the base model.

Licence

Apache-2.0, the licence of openbmb/MiniCPM5-2B. Training data: CC BY-NC 4.0; per-song rights are listed in HAK-CIPTA.md in the dataset repository.