Instructions to use trillionlabs/TxGravity-30B-A5B-Open with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trillionlabs/TxGravity-30B-A5B-Open with Transformers:

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

pipe = pipeline("text-generation", model="trillionlabs/TxGravity-30B-A5B-Open")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trillionlabs/TxGravity-30B-A5B-Open")
model = AutoModelForCausalLM.from_pretrained("trillionlabs/TxGravity-30B-A5B-Open", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trillionlabs/TxGravity-30B-A5B-Open with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trillionlabs/TxGravity-30B-A5B-Open"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionlabs/TxGravity-30B-A5B-Open",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trillionlabs/TxGravity-30B-A5B-Open

SGLang

How to use trillionlabs/TxGravity-30B-A5B-Open 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 "trillionlabs/TxGravity-30B-A5B-Open" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionlabs/TxGravity-30B-A5B-Open",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "trillionlabs/TxGravity-30B-A5B-Open" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionlabs/TxGravity-30B-A5B-Open",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use trillionlabs/TxGravity-30B-A5B-Open with Docker Model Runner:
```
docker model run hf.co/trillionlabs/TxGravity-30B-A5B-Open
```

sungjunhan-trl commited on Sep 9

Commit

974cfce

verified ·

1 Parent(s): 4c725af

README: clarify TDC-to-instruction reformatting

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -20,11 +20,11 @@ base_model:
 ## Model Summary
-**TxGravity-30B-A6B-Open** is a therapeutics-focused, task-specialized model built on the **Gravity-30B-A5B** base. It predicts a broad range of therapeutic properties — small-molecule ADMET, toxicity, drug–target interaction, protein–protein and peptide–MHC interaction, and more — following the instruction format of the **Therapeutic Data Commons (TDC)** benchmark, in the same spirit as Google's TxGemma.
 TxGravity is **co-developed by SK Biopharmaceuticals (SKBP) and Trillion Labs**.
-It is trained on TDC therapeutic instruction data (single-turn instruction → answer) covering 57 tasks. Answers are formatted as `(A)`/`(B)` for binary classification or a normalized `000`–`1000` bin for regression.
 | Property | Value |
 |---|---|

 ## Model Summary
+**TxGravity-30B-A6B-Open** is a therapeutics-focused, task-specialized model built on the **Gravity-30B-A5B** base. It predicts a broad range of therapeutic properties — small-molecule ADMET, toxicity, drug–target interaction, protein–protein and peptide–MHC interaction, and more — by converting the **Therapeutic Data Commons (TDC)** benchmark tasks into an instruction format for LLMs, in the same spirit as Google's TxGemma.
 TxGravity is **co-developed by SK Biopharmaceuticals (SKBP) and Trillion Labs**.
+It is trained on TDC tasks reformatted as single-turn instructions (instruction → answer), covering 57 tasks. Answers are formatted as `(A)`/`(B)` for binary classification or a normalized `000`–`1000` bin for regression.
 | Property | Value |
 |---|---|