Text Generation
Transformers
Safetensors
English
deepseek_v3
Mixture of Experts
mixture-of-experts
gravity
txgravity
therapeutics
tdc
trillion-labs
conversational
text-generation-inference
Instructions to use trillionlabs/TxGravity-30B-A5B-Open with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download summary.csv from trillionlabs/TxGravity-30B-A5B-Open: direct link, hf CLI and curl.
- Browser
- Download file 3.35 kB
-
https://huggingface.co/trillionlabs/TxGravity-30B-A5B-Open/resolve/802d85cc6b12b3fd3cffd0d99bf4357d2f347066/summary.csv
- Command line
-
hf download hf://trillionlabs/TxGravity-30B-A5B-Open@802d85cc6b12b3fd3cffd0d99bf4357d2f347066/summary.csv
-
curl -L -o summary.csv https://huggingface.co/trillionlabs/TxGravity-30B-A5B-Open/resolve/802d85cc6b12b3fd3cffd0d99bf4357d2f347066/summary.csv
3.35 kB
| task,metric,N,TxGemma-27B (repro),TxGravity-release,margin,basis,verdict | |
| ames,AUROC,1457,0.8300,0.8410,+1.1,%p,TIE | |
| bbb_martins,AUROC,406,0.8970,0.9190,+2.2,%p,TIE | |
| bindingdb_ic50,Spearman,35725,0.6430,0.7660,+12.3,%p,WIN | |
| bindingdb_kd,PCC,1630,0.5960,0.5220,-7.4,%p,LOSS | |
| bindingdb_ki,PCC,11857,-0.1120,0.7500,+86.2,%p,WIN | |
| bindingdb_patent,PCC,49028,0.5140,0.5410,+2.7,%p,TIE | |
| bioavailability_ma,AUROC,128,0.6970,0.7490,+5.2,%p,WIN | |
| buchwald_hartwig,PCC,791,0.8550,0.8920,+3.7,%p,WIN | |
| butkiewicz,AUROC,401997,0.8620,0.7540,-10.8,%p,LOSS | |
| caco2_wang,MAE,182,0.4660,0.4160,+10.7,%rel,WIN | |
| carcinogens_lagunin,Accuracy,56,0.8930,0.8750,-1.8,%p,TIE | |
| clearance_hepatocyte_az,Spearman,243,0.2910,0.3240,+3.3,%p,WIN | |
| clearance_microsome_az,Spearman,221,0.4870,0.5000,+1.3,%p,TIE | |
| clintox,AUROC,297,0.7560,0.7610,+0.5,%p,TIE | |
| cyp1a2_veith,AUPRC,2517,0.9350,0.9310,-0.4,%p,TIE | |
| cyp2c19_veith,AUROC,2534,0.8910,0.8940,+0.3,%p,TIE | |
| cyp2c9_substrate_carbonmangels,AUPRC,135,0.4730,0.3460,-12.7,%p,LOSS | |
| cyp2c9_veith,AUPRC,2419,0.7820,0.7920,+1.0,%p,TIE | |
| cyp2d6_substrate_carbonmangels,AUPRC,135,0.7380,0.7080,-3.0,%p,LOSS | |
| cyp2d6_veith,AUPRC,2626,0.6620,0.6840,+2.2,%p,TIE | |
| cyp3a4_substrate_carbonmangels,AUROC,135,0.6950,0.6410,-5.4,%p,LOSS | |
| cyp3a4_veith,AUPRC,2467,0.8410,0.8610,+2.0,%p,TIE | |
| davis,MSE,1064,0.6430,0.7600,-18.2,%rel,LOSS | |
| dili,AUROC,96,0.8890,0.8930,+0.4,%p,TIE | |
| disgenet,MAE,10495,-,-,-,-,EXCL | |
| drugcomb_bliss,MAE,59708,3.9280,3.7370,+4.9,%rel,WIN | |
| drugcomb_css,MAE,59708,9.4180,7.8380,+16.8,%rel,WIN | |
| drugcomb_hsa,MAE,59708,3.7880,3.5840,+5.4,%rel,WIN | |
| drugcomb_loewe,MAE,59708,7.6720,6.4130,+16.4,%rel,WIN | |
| drugcomb_zip,MAE,59708,3.4100,3.0610,+10.2,%rel,WIN | |
| gdsc1,PCC,35462,-,-,-,-,EXCL | |
| gdsc2,PCC,18541,-,-,-,-,EXCL | |
| half_life_obach,Spearman,135,0.2890,0.3600,+7.1,%p,WIN | |
| herg,AUROC,132,0.9000,0.8930,-0.7,%p,TIE | |
| herg_central,AUROC,61379,0.8830,0.8760,-0.7,%p,TIE | |
| herg_karim,Accuracy,2690,0.8000,0.7920,-0.8,%p,TIE | |
| hia_hou,AUROC,117,0.9860,0.9860,+0.0,%p,TIE | |
| hiv,AUROC,8227,0.7930,0.7640,-2.9,%p,TIE | |
| huri,AUPRC,4204,0.7920,0.8080,+1.6,%p,TIE | |
| kiba,MSE,4537,0.4600,0.6370,-38.5,%rel,LOSS | |
| ld50_zhu,MAE,1478,0.7760,0.6510,+16.1,%rel,WIN | |
| leenay,Spearman,1520,0.2260,0.1510,-7.5,%p,LOSS | |
| lipophilicity_astrazeneca,MAE,840,0.5950,0.5700,+4.2,%rel,WIN | |
| mhc1_iedb_imgt_nielsen,AUROC,37197,0.9680,0.9200,-4.8,%p,LOSS | |
| mhc2_iedb_jensen,AUROC,26856,0.8500,0.8680,+1.8,%p,TIE | |
| mirtarbase,Accuracy,160033,0.8040,0.5550,-24.9,%p,LOSS | |
| oncopolypharmacology,PCC,4647,0.5690,0.5050,-6.4,%p,LOSS | |
| pampa_ncats,AUROC,408,0.7080,0.6630,-4.5,%p,LOSS | |
| pgp_broccatelli,AUROC,245,0.9320,0.9380,+0.6,%p,TIE | |
| phase1,AUROC,561,-,-,-,-,EXCL | |
| phase2,AUROC,1279,-,-,-,-,EXCL | |
| phase3,AUROC,1200,-,-,-,-,EXCL | |
| ppbr_az,MAE,559,9.4180,8.2900,+12.0,%rel,WIN | |
| protein_sabdab,MAE,99,2.3320,0.9690,+58.4,%rel,WIN | |
| sabdab_chen,AUPRC,482,0.7080,0.6580,-5.0,%p,LOSS | |
| sarscov2_3clpro_diamond,AUROC,176,0.7250,0.7470,+2.2,%p,TIE | |
| sarscov2_vitro_touret,AUROC,298,0.5300,0.4580,-7.2,%p,LOSS | |
| skin_reaction,AUROC,82,0.6370,0.6510,+1.4,%p,TIE | |
| solubility_aqsoldb,MAE,1997,0.8050,0.8550,-6.2,%rel,LOSS | |
| tap,MAE,240,4.7850,5.4400,-13.7,%rel,LOSS | |
| tox21,AUROC,15600,0.8430,0.8230,-2.0,%p,TIE | |
| toxcast,AUROC,307282,0.9050,0.8970,-0.8,%p,TIE | |
| uspto,Accuracy,221648,0.0880,0.0000,-8.8,%p,LOSS | |
| uspto_yields,PCC,170728,0.1860,-0.0571,-24.3,%p,LOSS | |
| vdss_lombardo,Spearman,226,0.5660,0.5820,+1.6,%p,TIE | |
| weber,AUROC,9417,0.7390,0.7180,-2.1,%p,TIE | |