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Ihor/
OpenBioLLM-Text2Graph-8B

Name: OpenBioLLM-Text2Graph-8B
Rating: 2.5 (7 reviews)

safetensors

llama

2504.00676

aaditya/Llama3-OpenBioLLM-8B

finetune

apache-2.0

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OpenBioLLM-Text2Graph-8B

This model is a biomedical annotation model designed to generate named entity annotations from unlabeled biomedical text.
It was introduced in the paper GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition.

This model enables high-throughput, cost-efficient synthetic biomedical NER data generation, serving as the synthetic annotation backbone for GLiNER-BioMed models.

Usage

To use the model with transformer package, see the example below:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Ihor/OpenBioLLM-Text2Graph-8B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|end_of_text|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.bfloat16
)


MESSAGES = [
    {
        "role": "system",
        "content": (
            "You are an advanced assistant trained to process biomedical text for Named Entity Recognition (NER) and Relation Extraction (RE). "
            "Your task is to analyze user-provided text, identify all unique and contextually relevant entities, and infer directed relationships "
            "between these entities based on the context. Ensure that all relations exist only between annotated entities. "
            "Entities and relationships should be human-readable and natural, reflecting real-world concepts and connections. "
            "Output the annotated data in JSON format, structured as follows:\n\n"
            """{"entities": [{"id": 0, "text": "ner_string_0", "type": "ner_type_string_0"}, {"id": 1, "text": "ner_string_1", "type": "ner_type_string_1"}], "relations": [{"head": 0, "tail": 1, "type": "re_type_string_0"}]}"""
            "\n\nEnsure that the output captures all significant entities and their directed relationships in a clear and concise manner."
        ),
    },
    {
        "role": "user",
        "content": (
            'Here is a text input: "Subjects will receive a 100mL dose of IV saline every 6 hours for 24 hours. The first dose will be administered prior to anesthesia induction, approximately 30 minutes before skin incision. A total of 4 doses will be given." '
            "Analyze this text, select and classify the entities, and extract their relationships as per your instructions."
        ),
    },
]

# Build prompt text
chat_prompt = tokenizer.apply_chat_template(
    MESSAGES, tokenize=False, add_generation_prompt=True
)

# Tokenize
inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(
    **inputs,
    max_new_tokens=3000,
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
    return_dict_in_generate=True
)

# Decode ONLY the new tokens (skip the prompt tokens)
prompt_len = inputs["input_ids"].shape[-1]
generated_ids = outputs.sequences[0][prompt_len:]
response = tokenizer.decode(generated_ids, skip_special_tokens=True)
print(response)

To use the model with vllm package, please refer to the example below:

python

# !pip install vllm

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

MODEL_ID = "Ihor/OpenBioLLM-Text2Graph-8B"  

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
tokenizer.chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|end_of_text|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"

llm = LLM(model=MODEL_ID)

sampling_params = SamplingParams(
    max_tokens=3000,          
    n=1,
    best_of=1,
    presence_penalty=0.0,
    frequency_penalty=0.0,
    repetition_penalty=1.0,
    temperature=0.0,
    top_p=1.0,
    top_k=-1,
    min_p=0.0,
    seed=42,
)


MESSAGES = [
    {
        "role": "system",
        "content": (
            "You are an advanced assistant trained to process biomedical text for Named Entity Recognition (NER) and Relation Extraction (RE). "
            "Your task is to analyze user-provided text, identify all unique and contextually relevant entities, and infer directed relationships "
            "between these entities based on the context. Ensure that all relations exist only between annotated entities. "
            "Entities and relationships should be human-readable and natural, reflecting real-world concepts and connections. "
            "Output the annotated data in JSON format, structured as follows:\n\n"
            """{"entities": [{"id": 0, "text": "ner_string_0", "type": "ner_type_string_0"}, {"id": 1, "text": "ner_string_1", "type": "ner_type_string_1"}], "relations": [{"head": 0, "tail": 1, "type": "re_type_string_0"}]}"""
            "\n\nEnsure that the output captures all significant entities and their directed relationships in a clear and concise manner."
        ),
    },
    {
        "role": "user",
        "content": (
            'Here is a text input: "Subjects will receive a 100mL dose of IV saline every 6 hours for 24 hours. The first dose will be administered prior to anesthesia induction, approximately 30 minutes before skin incision. A total of 4 doses will be given." '
            "Analyze this text, select and classify the entities, and extract their relationships as per your instructions."
        ),
    },
]

chat_prompt = tokenizer.apply_chat_template(
    MESSAGES,
    tokenize=False,
    add_generation_prompt=True,
    add_special_tokens=False, 
)

outputs = llm.generate([chat_prompt], sampling_params)
response_text = outputs[0].outputs[0].text
print(response_text)

Citation

If you use this model, please cite:

bibtex

@misc{yazdani2025glinerbiomedsuiteefficientmodels,
      title={GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition}, 
      author={Anthony Yazdani and Ihor Stepanov and Douglas Teodoro},
      year={2025},
      eprint={2504.00676},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.00676}, 
}