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
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library_name: transformers
license: apache-2.0
base_model: ibm-research/GP-MoLFormer-Uniq
tags:
- generated_from_trainer
model-index:
- name: NPComposer-v2
results: []
---
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/npcomposer/npcomposer/runs/lpvwn86m)
# NPComposer
NPComposer is a conditional molecular generation model trained by fine-tuning GP-MoLFormer [ibm-research/GP-MoLFormer-Uniq](https://huggingface.co/ibm-research/GP-MoLFormer-Uniq) — a 46.8M parameter transformer decoder foundation model — on the COCONUT database containing over 700,000 experimentally validated natural products:
https://github.com/shawnralyn/NPComposer
By providing class labels and molecular property information as special tokens during model fine-tuning, NPComposer allows for conditional natural product generation based on: NP biosynthesis pathway (7 pathways including Alkaloids, Terpenoids, Shikimates and Phenylpropanoids, etc.), NP superclass (70+ superclasses), presence or absence of glycoside, number of aromatic rings (0–22), QED drug-likeness (0–1), and synthetic accessibility score (1–10).
## Model description
[More information needed](https://github.com/shawnralyn/NPComposer)
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 8.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.4069 | 1.0 | 27763 | 0.3820 |
| 0.3507 | 2.0 | 55526 | 0.3371 |
| 0.3247 | 3.0 | 83289 | 0.3142 |
| 0.3074 | 4.0 | 111052 | 0.2995 |
| 0.291 | 5.0 | 138815 | 0.2888 |
| 0.2765 | 6.0 | 166578 | 0.2803 |
| 0.2677 | 7.0 | 194341 | 0.2750 |
| 0.2569 | 8.0 | 222104 | 0.2718 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.10.0+cu128
- Datasets 2.21.0
- Tokenizers 0.19.1
### Hardware
1 x NVIDIA A40 48GB GPU
|