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

Libraries

How to use openbmb/MiniCPM5-2B-DSpark with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM5-2B-DSpark", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-2B-DSpark", trust_remote_code=True)
model = AutoModel.from_pretrained("openbmb/MiniCPM5-2B-DSpark", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM5-2B-DSpark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM5-2B-DSpark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM5-2B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM5-2B-DSpark

SGLang

How to use openbmb/MiniCPM5-2B-DSpark 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/MiniCPM5-2B-DSpark" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM5-2B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/MiniCPM5-2B-DSpark" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM5-2B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openbmb/MiniCPM5-2B-DSpark with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM5-2B-DSpark
```

MiniCPM5-2B-DSpark / config.json

BigDong

upload MiniCPM5-2B-DSpark

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	{
	"architectures": [
	"Qwen3DSparkModel"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoModel": "dspark.DSparkDraftModel"
	},
	"block_size": 7,
	"bos_token_id": 0,
	"draft_vocab_size": 130560,
	"dtype": "bfloat16",
	"eos_token_id": [
	1,
	130073
	],
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 6144,
	"layer_types": [
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention"
	],
	"max_position_embeddings": 131072,
	"max_window_layers": 5,
	"mlp_bias": false,
	"model_type": "qwen3",
	"num_attention_heads": 16,
	"num_hidden_layers": 5,
	"num_key_value_heads": 2,
	"num_target_layers": 42,
	"pad_token_id": 1,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 5000000,
	"rope_type": "default"
	},
	"sliding_window": null,
	"tie_word_embeddings": false,
	"transformers_version": "5.12.1",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 130560,
	"attention_mode": "gqa",
	"confidence_head_alpha": 1.0,
	"confidence_head_with_markov": true,
	"enable_confidence_head": true,
	"markov_head_type": "vanilla",
	"markov_rank": 256,
	"mask_token_id": 75982,
	"projector_type": "dspark",
	"target_layer_ids": [
	1,
	10,
	20,
	30,
	39
	]
	}