Instructions to use openbmb/DensingLaw-ScalingModels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/DensingLaw-ScalingModels with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/DensingLaw-ScalingModels")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/DensingLaw-ScalingModels", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/DensingLaw-ScalingModels with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/openbmb/DensingLaw-ScalingModels

SGLang

How to use openbmb/DensingLaw-ScalingModels 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/DensingLaw-ScalingModels" \
    --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/DensingLaw-ScalingModels",
		"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/DensingLaw-ScalingModels" \
        --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/DensingLaw-ScalingModels",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openbmb/DensingLaw-ScalingModels with Docker Model Runner:
```
docker model run hf.co/openbmb/DensingLaw-ScalingModels
```

DensingLaw-ScalingModels

File size: 1,017 Bytes

{
    "_name_or_path": "openbmb/CPM-2B",
    "architectures": [
        "MiniCPMForCausalLM"
    ],
    "auto_map": {
        "AutoConfig": "configuration_minicpm.MiniCPMConfig",
        "AutoModel": "modeling_minicpm.MiniCPMModel",
        "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
        "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
        "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
    },
    "bos_token_id": 1,
    "eos_token_id": [2, 73440],
    "hidden_act": "silu",
    "initializer_range": 0.1,
    "hidden_size": 512,
    "num_hidden_layers": 12,
    "intermediate_size": 1280,
    "max_position_embeddings": 32768,
    "num_attention_heads": 8,
    "num_key_value_heads": 2,
    "rms_norm_eps": 1e-05,
    "rope_scaling": null,
    "torch_dtype": "bfloat16",
    "transformers_version": "4.41.0",
    "use_cache": true,
    "vocab_size": 73448,
    "scale_emb": 12,
    "dim_model_base": 256,
    "scale_depth": 1.4
}