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
```

TxGravity-30B-A5B-Open / README.md

sungjunhan-trl

model card

802d85c verified about 1 month ago

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - moe
  - mixture-of-experts
  - gravity
  - txgravity
  - therapeutics
  - tdc
  - trillion-labs
base_model:
  - trillionlabs/Gravity-30B-A5B

TxGravity-30B-A6B-Open

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.

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
Total Parameters	29.56B
Active Parameters	~6B
Architecture	GravityMoE (DeepSeek-V3-compatible)
Layers	52
Routed / Shared Experts	64 (top-8) / 1
Context Length	8,192 tokens
Precision	bf16
Base model	Gravity-30B-A5B

⚠️ This is a task-specialized property predictor, not a general instruction-tuned or safety-aligned assistant. Its outputs are intended for the TDC-style therapeutic prediction prompts it was trained on. Predictions may be inaccurate, biased, or incomplete and must be independently verified before any experimental, clinical, or decision-making use.

Performance

Evaluated on the TDC therapeutic benchmark against a reproduced TxGemma-27B baseline across 60 comparable tasks:

16 wins · 26 ties · 18 losses

Selected results (TxGravity vs TxGemma-27B):

Task	Metric	TxGravity	TxGemma-27B
bindingdb_ki	PCC ↑	0.750	−0.112
bindingdb_ic50	Spearman ↑	0.766	0.643
protein_sabdab	MAE ↓	0.969	2.332
ld50_zhu	MAE ↓	0.651	0.776
drugcomb_css	MAE ↓	7.838	9.418
bioavailability_ma	AUROC ↑	0.749	0.697
bbb_martins	AUROC ↑	0.919	0.897
ames	AUROC ↑	0.841	0.830
davis	MSE ↓	0.760	0.643
kiba	MSE ↓	0.637	0.460

Full per-task results (66 tasks, metric, N, margin, verdict) are in summary.csv.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("trillionlabs/TxGravity-30B-A6B-Open")
model = AutoModelForCausalLM.from_pretrained(
    "trillionlabs/TxGravity-30B-A6B-Open", dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "<TDC-formatted instruction here>"}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=16)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

The model expects TDC-style prompts and answers in TDC format: (A)/(B) for classification, or a 000–1000 normalized bin for regression.

License

Apache 2.0