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

Libraries

How to use fwerkor/MiniCPM5-2B-Diffusion-Base with Transformers:

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

pipe = pipeline("text-generation", model="fwerkor/MiniCPM5-2B-Diffusion-Base", trust_remote_code=True)

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

tokenizer = AutoTokenizer.from_pretrained("fwerkor/MiniCPM5-2B-Diffusion-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("fwerkor/MiniCPM5-2B-Diffusion-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fwerkor/MiniCPM5-2B-Diffusion-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/fwerkor/MiniCPM5-2B-Diffusion-Base

SGLang

How to use fwerkor/MiniCPM5-2B-Diffusion-Base 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 "fwerkor/MiniCPM5-2B-Diffusion-Base" \
    --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": "fwerkor/MiniCPM5-2B-Diffusion-Base",
		"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 "fwerkor/MiniCPM5-2B-Diffusion-Base" \
        --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": "fwerkor/MiniCPM5-2B-Diffusion-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

MiniCPM5-2B-Diffusion-Base / config.json

fwerkor

Release MiniCPM5-2B-Diffusion-Base

5f58db9 verified about 1 month ago

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1 kB

{
  "architectures": [
    "CIDDiffusionForMaskedLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "base_model_name_or_path": "openbmb/MiniCPM5-2B-Base",
  "bos_token_id": 0,
  "cid_diffusion_base": true,
  "diffusion_attention": "bidirectional",
  "diffusion_objective": "masked-diffusion",
  "dtype": "bfloat16",
  "eos_token_id": 1,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 6144,
  "mask_token_id": 130560,
  "max_position_embeddings": 524288,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 16,
  "num_hidden_layers": 42,
  "num_key_value_heads": 2,
  "pad_token_id": 1,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 5000000,
  "tie_word_embeddings": false,
  "transformers_version": "4.57.6",
  "use_cache": false,
  "vocab_size": 130561,
  "auto_map": {
    "AutoModelForCausalLM": "modeling_cid_diffusion.CIDDiffusionForMaskedLM"
  }
}

	{
	"architectures": [
	"CIDDiffusionForMaskedLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"base_model_name_or_path": "openbmb/MiniCPM5-2B-Base",
	"bos_token_id": 0,
	"cid_diffusion_base": true,
	"diffusion_attention": "bidirectional",
	"diffusion_objective": "masked-diffusion",
	"dtype": "bfloat16",
	"eos_token_id": 1,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 6144,
	"mask_token_id": 130560,
	"max_position_embeddings": 524288,
	"mlp_bias": false,
	"model_type": "llama",
	"num_attention_heads": 16,
	"num_hidden_layers": 42,
	"num_key_value_heads": 2,
	"pad_token_id": 1,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 5000000,
	"tie_word_embeddings": false,
	"transformers_version": "4.57.6",
	"use_cache": false,
	"vocab_size": 130561,
	"auto_map": {
	"AutoModelForCausalLM": "modeling_cid_diffusion.CIDDiffusionForMaskedLM"
	}
	}