Instructions to use SparseLLM/ProSparse-MiniCPM-1B-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/ProSparse-MiniCPM-1B-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SparseLLM/ProSparse-MiniCPM-1B-sft", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SparseLLM/ProSparse-MiniCPM-1B-sft", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SparseLLM/ProSparse-MiniCPM-1B-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SparseLLM/ProSparse-MiniCPM-1B-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SparseLLM/ProSparse-MiniCPM-1B-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SparseLLM/ProSparse-MiniCPM-1B-sft
- SGLang
How to use SparseLLM/ProSparse-MiniCPM-1B-sft 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 "SparseLLM/ProSparse-MiniCPM-1B-sft" \ --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": "SparseLLM/ProSparse-MiniCPM-1B-sft", "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 "SparseLLM/ProSparse-MiniCPM-1B-sft" \ --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": "SparseLLM/ProSparse-MiniCPM-1B-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SparseLLM/ProSparse-MiniCPM-1B-sft with Docker Model Runner:
docker model run hf.co/SparseLLM/ProSparse-MiniCPM-1B-sft
| { | |
| "_name_or_path": "SparseLLM/ProSparse-MiniCPM-1B-sft", | |
| "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, | |
| "hidden_act": "fatrelu", | |
| "hidden_act_param": 0.03, | |
| "hidden_size": 1536, | |
| "initializer_range": 0.1, | |
| "intermediate_size": 3840, | |
| "max_position_embeddings": 4096, | |
| "num_attention_heads": 24, | |
| "num_hidden_layers": 52, | |
| "num_key_value_heads": 8, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.36.0", | |
| "use_cache": true, | |
| "vocab_size": 73440, | |
| "scale_emb": 12, | |
| "dim_model_base": 256, | |
| "scale_depth": 1.4 | |
| } | |