Instructions to use ralyn/NPComposer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ralyn/NPComposer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ralyn/NPComposer", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ralyn/NPComposer", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ralyn/NPComposer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ralyn/NPComposer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ralyn/NPComposer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ralyn/NPComposer
- SGLang
How to use ralyn/NPComposer 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 "ralyn/NPComposer" \ --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": "ralyn/NPComposer", "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 "ralyn/NPComposer" \ --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": "ralyn/NPComposer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ralyn/NPComposer with Docker Model Runner:
docker model run hf.co/ralyn/NPComposer
Download config.json from ralyn/NPComposer: direct link, hf CLI and curl.
- Browser
- Download file 986 Bytes
-
https://huggingface.co/ralyn/NPComposer/resolve/main/config.json
- Command line
-
hf download hf://ralyn/NPComposer/config.json
-
curl -L -o config.json https://huggingface.co/ralyn/NPComposer/resolve/main/config.json
986 Bytes
| { | |
| "_name_or_path": "ibm-research/GP-MoLFormer-Uniq", | |
| "architectures": [ | |
| "MolformerForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "ibm-research/GP-MoLFormer-Uniq--configuration_molformer.MolformerConfig", | |
| "AutoModelForCausalLM": "ibm-research/GP-MoLFormer-Uniq--modeling_molformer.MolformerForCausalLM" | |
| }, | |
| "bos_token_id": 0, | |
| "deterministic_eval": false, | |
| "embedding_dropout_prob": 0.2, | |
| "eos_token_id": 1, | |
| "feature_map_kernel": "relu", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 768, | |
| "is_decoder": true, | |
| "layer_norm_eps": 1e-12, | |
| "linear_attention_eps": 1e-06, | |
| "max_position_embeddings": 202, | |
| "model_type": "molformer", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "num_random_features": 32, | |
| "pad_token_id": 2, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.2", | |
| "use_cache": true, | |
| "vocab_size": 2488 | |
| } | |