Instructions to use Crystalcareai/CrystalMiniCPM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crystalcareai/CrystalMiniCPM with Transformers:

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

pipe = pipeline("text-generation", model="Crystalcareai/CrystalMiniCPM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Crystalcareai/CrystalMiniCPM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Crystalcareai/CrystalMiniCPM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Crystalcareai/CrystalMiniCPM

SGLang

How to use Crystalcareai/CrystalMiniCPM 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 "Crystalcareai/CrystalMiniCPM" \
    --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": "Crystalcareai/CrystalMiniCPM",
		"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 "Crystalcareai/CrystalMiniCPM" \
        --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": "Crystalcareai/CrystalMiniCPM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Crystalcareai/CrystalMiniCPM with Docker Model Runner:
```
docker model run hf.co/Crystalcareai/CrystalMiniCPM
```

CrystalMiniCPM / config.json

Crystalcareai

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	{
	"_name_or_path": "openbmb/MiniCPM-2B-sft-bf16",
	"architectures": [
	"MiniCPMForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "openbmb/MiniCPM-2B-sft-bf16--configuration_minicpm.MiniCPMConfig",
	"AutoModel": "openbmb/MiniCPM-2B-sft-bf16--modeling_minicpm.MiniCPMModel",
	"AutoModelForCausalLM": "openbmb/MiniCPM-2B-sft-bf16--modeling_minicpm.MiniCPMForCausalLM",
	"AutoModelForSeq2SeqLM": "openbmb/MiniCPM-2B-sft-bf16--modeling_minicpm.MiniCPMForCausalLM",
	"AutoModelForSequenceClassification": "openbmb/MiniCPM-2B-sft-bf16--modeling_minicpm.MiniCPMForSequenceClassification"
	},
	"bos_token_id": 1,
	"dim_model_base": 256,
	"eos_token_id": 2,
	"hidden_act": "silu",
	"hidden_size": 2304,
	"initializer_range": 0.1,
	"intermediate_size": 5760,
	"max_position_embeddings": 4096,
	"model_type": "minicpm",
	"num_attention_heads": 36,
	"num_hidden_layers": 40,
	"num_key_value_heads": 36,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"scale_depth": 1.4,
	"scale_emb": 12,
	"torch_dtype": "float32",
	"transformers_version": "4.38.0.dev0",
	"use_cache": false,
	"vocab_size": 122753
	}