BioGravity-bilingual-Inst
BioGravity-bilingual-Inst is a Korean–English biomedical instruction model from AIGEN Sciences, Inc., developed from the Gravity 30B-A5B family. It is intended for biomedical explanations, instruction following, and tool-assisted research in the Biomni A1 environment.
The repository provides a complete merged checkpoint, tokenizer, chat template, Gravity model-loading code, and A1 protocol metadata.
Model Summary
| Property | Value |
|---|---|
| Developer | AIGEN Sciences, Inc. |
| Base model | Gravity-30B-A5B-Preview |
| Architecture | GravityMoEForCausalLM (gravity_moe), sparse MoE with MLA |
| Total / active parameters | 29.56B / approximately 5.34B |
| Layers / routed experts | 52 / 64, with 8 experts selected per token |
| Primary languages | Korean and English |
| Configured context limit | 131,072 tokens |
| Stored precision | FP32, 24 safetensors shards |
| Supported inference precision | BF16 |
| Agent environment | Biomni A1 |
| A1 protocol | Dedicated execute, solution, and observation special tokens |
| License | Apache 2.0 |
Evaluation Results
Reported Biomni Eval1 results from a separate evaluation environment:
| Model | Evaluation harness | Accuracy |
|---|---|---|
| BioGravity-bilingual-Inst | Not applied | 39.72% |
| BioGravity-bilingual-Inst | Applied | 55.1% |
The 55.1% result uses the evaluation harness. The harness and its configuration are available upon request through AIGEN Sciences.
Quickstart
Use a CUDA-compatible PyTorch installation and the tested Transformers version:
pip install "transformers==4.57.6" accelerate safetensors
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "aigensciences/BioGravity-bilingual-Inst"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
dtype=torch.bfloat16,
device_map="auto",
attn_implementation="eager",
).eval()
messages = [
{"role": "system", "content": "You are a biomedical research assistant."},
{"role": "user", "content": "관찰연구에서 상관관계와 인과관계의 차이를 한국어로 설명해 주세요."},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
tokenizer_kwargs={"return_token_type_ids": False},
).to(model.device)
with torch.inference_mode():
outputs = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
continuation = outputs[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(continuation, skip_special_tokens=False))
BF16 loading requires approximately 59 GB for weights, plus memory for the KV cache and runtime. The configured 131,072-token context limit does not establish benchmark performance at that length.
Biomni A1 integration
Prepare the tools and data lake using the Biomni installation instructions, together with the evaluation harness supplied on request.
| Purpose | Format |
|---|---|
| Reasoning segment | <think>...</think> |
| Code action | <execute>Python code</execute> |
| Tool observation | <observation>tool result</observation> |
| Final answer | <solution>answer</solution> |
The A1 runtime executes actions and returns observations. Preserve the six dedicated action/observation special tokens by using skip_special_tokens=False. The runtime should retain the </execute> and </solution> stop markers when delimiting actions and final answers.
The default generation prompt opens a <think> segment. Passing enable_thinking=False to apply_chat_template prefills an empty <think></think> segment. Keep the tokenizer, chat template, and protocol_manifest.json together when deploying the model.
Limitations
- Research use and environment. The model is released for research purposes and is intended to operate in the Biomni A1 environment.
- Factual accuracy. Generated content, explanations, and references may contain factual errors. Expert verification is required before relying on the results.
- Knowledge freshness. Literature and guidelines published after the training-data cutoff are not reflected in the model weights. Current sources need to be retrieved and verified separately.
- Language coverage. Performance in languages other than Korean and English is not guaranteed.
- Harness dependence. The reported 55.1% Eval1 result uses the evaluation harness and environment associated with that measurement. Performance depends on the deployment and evaluation setup.
Acknowledgements
Support for this research comes from the 인공지능 특화 파운데이션 모델 프로젝트 (Domain-Specific Foundation Model Project). 과학기술정보통신부 (MSIT) provides funding, and 정보통신산업진흥원 (NIPA) administers the program.
We thank Lunit, Trillion Labs, SK Biopharmaceuticals, Kakao Healthcare, and the consortium's industry, academic, and hospital partners for the Gravity research collaboration. The Lunit model card provides the consortium acknowledgements on which this statement is based. We also acknowledge the Biomni project for its agent framework and evaluation resources.
License
The model is released under the Apache License 2.0. Research use describes its intended application and does not add restrictions to the license.
Contact
AIGEN Sciences, Inc. — contact the organization to request the evaluation harness and configuration.