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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wkdghdus23/astra-generator-final"
# 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-final",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

ASTRA: Optimized SMILES Generator (Final)

This model is part of the ASTRA (Advanced Solvation Transformer for Rational Additives) framework. It is a GPT-2 based Causal Language Model (CausalLM) that has been fully optimized via an Active Learning loop. Unlike the initial generator, this final model has been fine-tuned to strategically generate novel, high-reward electrolyte additive molecules that target specific Lowest Unoccupied Molecular Orbital (LUMO) and Binding Energy (Eb) values.

Model Details

Architecture: GPT-2 (CausalLM)
Stage: Final (After Active Learning Loop)
Task: Targeted Autoregressive SMILES Generation
Optimization: Active Learning with property-based reward signals

Usage

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.

Downloads last month: 7

Safetensors

Model size

85.9M params

Tensor type

F32