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RehanaHasin
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Model Details
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RehanaHasin
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Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
qwen2.5-7b-instruct-adjuvant-extractorQwen/Qwen2.5-7B-Instructtext
You are a biomedical information extraction assistant.text
Extract infectious-disease adjuvants from the text and provide evidence snippets.Return ONLY valid JSON in this format:[{"adjuvant": "<string>", "evidence": "<string>"}, ...]Do not include any extra keys or explanation.text
Title: <paper title>Abstract: <paper abstract>The model receives the user instruction template followed by the title/abstract text.
The model is prompted to return a JSON array of objects with exactly two keys:
json
[ { "adjuvant": "<string>", "evidence": "<string>" }]Expected behavior:
[]).adjuvant: normalized or near-normalized adjuvant nameevidence: supporting text snippet from the same input abstracttext
Title: Intranasal vaccination study using alum and MPLA adjuvants in a murine influenza model.Abstract: Mice immunized with antigen formulated with alum showed increased IgG titers. A separate group receiving MPLA-adjuvanted vaccine demonstrated stronger IFN-gamma responses and reduced viral load after challenge.json
[ { "adjuvant": "alum", "evidence": "Mice immunized with antigen formulated with alum showed increased IgG titers." }, { "adjuvant": "MPLA", "evidence": "A separate group receiving MPLA-adjuvanted vaccine demonstrated stronger IFN-gamma responses and reduced viral load after challenge." }][] if no supported adjuvant is found).adjuvant and evidence.python
import torchimport jsonfrom transformers import AutoTokenizer, AutoModelForCausalLM repo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"SYS_PROMPT = "You are a biomedical information extraction assistant."PROMPT_INSTRUCTION = ( "Extract infectious-disease adjuvants from the text and provide evidence snippets.\n" "Return ONLY valid JSON in this format:\n" "[{\"adjuvant\": \"<string>\", \"evidence\": \"<string>\"}, ...]\n" "Do not include any extra keys or explanation.")title = "Protective immune response against Streptococcus pyogenes in mice after intranasal vaccination with the fibronectin-binding protein SfbI."abstract = ( "Despite the significant impact on human health caused by Streptococcus pyogenes, " "there is currently no vaccine available. Intranasal immunization of mice with either " "SfbI alone or coupled to cholera toxin B subunit (CTB) triggered efficient SfbI-specific responses.")user_input = f"{PROMPT_INSTRUCTION}\n\nTitle: {title}\nAbstract: {abstract}"tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True)model = AutoModelForCausalLM.from_pretrained( repo_id, dtype=torch.float16, device_map="auto",)model.eval()chat = tokenizer.apply_chat_template( [ {"role": "system", "content": SYS_PROMPT}, {"role": "user", "content": user_input}, ], tokenize=False, add_generation_prompt=True,)inputs = tokenizer(chat, return_tensors="pt", truncation=True, max_length=1024)inputs = {k: v.to(model.get_input_embeddings().weight.device) for k, v in inputs.items()}with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=200, do_sample=False, pad_token_id=tokenizer.eos_token_id, )prediction = tokenizer.decode( outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()print(json.dumps(json.loads(prediction), indent=2, ensure_ascii=False))This model is intended for research workflows in biomedical literature mining, especially:
This model is not intended for clinical decision-making.
The model was trained on a curated infectious disease adjuvant corpus derived from VIOLIN ecosystem resources.
r): 82e-45python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torchrepo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained( repo_id, torch_dtype=torch.float16, device_map="auto",)Use prompts that explicitly request structured JSON output containing only:
adjuvantevidenceand restrict extra commentary to reduce parsing errors.
Code, notebooks, and workflow details are available at:
https://github.com/hurlab/Infectious-Disease-Adjuvant-LLM-Fine-tuning
If you use this model, please cite the associated manuscript and project repository.
For questions, please contact hasin.rehana@und.edu.
Extract infectious-disease adjuvants from the text and provide evidence snippets.Return ONLY valid JSON in this format:[{"adjuvant": "<string>", "evidence": "<string>"}, ...]Do not include any extra keys or explanation.[ { "adjuvant": "<string>", "evidence": "<string>" }]Title: Intranasal vaccination study using alum and MPLA adjuvants in a murine influenza model.Abstract: Mice immunized with antigen formulated with alum showed increased IgG titers. A separate group receiving MPLA-adjuvanted vaccine demonstrated stronger IFN-gamma responses and reduced viral load after challenge.[ { "adjuvant": "alum", "evidence": "Mice immunized with antigen formulated with alum showed increased IgG titers." }, { "adjuvant": "MPLA", "evidence": "A separate group receiving MPLA-adjuvanted vaccine demonstrated stronger IFN-gamma responses and reduced viral load after challenge." }]import torchimport jsonfrom transformers import AutoTokenizer, AutoModelForCausalLM repo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"SYS_PROMPT = "You are a biomedical information extraction assistant."PROMPT_INSTRUCTION = ( "Extract infectious-disease adjuvants from the text and provide evidence snippets.\n" "Return ONLY valid JSON in this format:\n" "[{\"adjuvant\": \"<string>\", \"evidence\": \"<string>\"}, ...]\n" "Do not include any extra keys or explanation.")title = "Protective immune response against Streptococcus pyogenes in mice after intranasal vaccination with the fibronectin-binding protein SfbI."abstract = ( "Despite the significant impact on human health caused by Streptococcus pyogenes, " "there is currently no vaccine available. Intranasal immunization of mice with either " "SfbI alone or coupled to cholera toxin B subunit (CTB) triggered efficient SfbI-specific responses.")user_input = f"{PROMPT_INSTRUCTION}\n\nTitle: {title}\nAbstract: {abstract}"tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True)model = AutoModelForCausalLM.from_pretrained( repo_id, dtype=torch.float16, device_map="auto",)model.eval()chat = tokenizer.apply_chat_template( [ {"role": "system", "content": SYS_PROMPT}, {"role": "user", "content": user_input}, ], tokenize=False, add_generation_prompt=True,)inputs = tokenizer(chat, return_tensors="pt", truncation=True, max_length=1024)inputs = {k: v.to(model.get_input_embeddings().weight.device) for k, v in inputs.items()}with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=200, do_sample=False, pad_token_id=tokenizer.eos_token_id, )prediction = tokenizer.decode( outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()print(json.dumps(json.loads(prediction), indent=2, ensure_ascii=False))from transformers import AutoTokenizer, AutoModelForCausalLMimport torchrepo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained( repo_id, torch_dtype=torch.float16, device_map="auto",)