Instructions to use wkdghdus23/astra-generator-initial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wkdghdus23/astra-generator-initial with Transformers:

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

pipe = pipeline("text-generation", model="wkdghdus23/astra-generator-initial")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wkdghdus23/astra-generator-initial")
model = AutoModelForCausalLM.from_pretrained("wkdghdus23/astra-generator-initial", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wkdghdus23/astra-generator-initial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wkdghdus23/astra-generator-initial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wkdghdus23/astra-generator-initial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wkdghdus23/astra-generator-initial

SGLang

How to use wkdghdus23/astra-generator-initial 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 "wkdghdus23/astra-generator-initial" \
    --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": "wkdghdus23/astra-generator-initial",
		"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 "wkdghdus23/astra-generator-initial" \
        --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": "wkdghdus23/astra-generator-initial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wkdghdus23/astra-generator-initial with Docker Model Runner:
```
docker model run hf.co/wkdghdus23/astra-generator-initial
```

astra-generator-initial / README.md

wkdghdus23

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	---
	tags:
	- chemistry
	- cheminformatics
	- materials-science
	- astra
	- transformers
	- gpt2
	- text-generation
	license: mit
	---

	# ASTRA: Initial SMILES Generator

	This model is part of the ASTRA (Advanced Solvation Transformer for Rational Additives) framework.
	It is a GPT-2 based Causal Language Model (CausalLM) trained on a large-scale dataset of canonical SMILES strings. While this model has learned the fundamental grammar and structural rules necessary to generate chemically valid molecules autonomously, it has not yet been optimized for specific quantum chemical properties.

	This model serves as the starting point (pre-generator) for the ASTRA Active Learning loop.

	## Model Details
	- Architecture: GPT-2 (CausalLM)
	- Stage: Initial (Before Active Learning)
	- Task: Autoregressive SMILES Generation
	- Data: Large-scale unlabelled canonical SMILES (~4M -> ~260K)

	## Usage
	```python
	from astra.tokenizer import initial_gpt_tokenizer_with_vocabulary
	from astra.model import GPTForCausalLM

	# Load the tokenizer and model
	vocab_file = "./vocab.txt"
	tokenizer = initial_gpt_tokenizer_with_vocabulary(path=vocab_file)

	pretrained_path = "<path/to/pretrained/model>" # Replace with your actual paths
	model = GPTForCausalLM.from_pretrained(pretrained_gpt_path)
	```

	## More Information
	For more details on data preparation, downstream fine-tuning, and the full active learning loop, please visit our [GitHub Repository](https://github.com/wkdghdus23/astra).