---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- biomedical
- infectious-disease
- vaccine-adjuvant
- information-extraction
- evidence-grounded
base_model: Qwen/Qwen2.5-7B-Instruct
---

# qwen2.5-7b-instruct-adjuvant-extractor

This model is a task-specific fine-tuned version of **Qwen/Qwen2.5-7B-Instruct** for extracting **vaccine adjuvant concepts** and associated **evidence snippets** from infectious disease biomedical abstracts.

## Model Summary

- **Model name:** `qwen2.5-7b-instruct-adjuvant-extractor`
- **Base model:** `Qwen/Qwen2.5-7B-Instruct`
- **Fine-tuning method:** LoRA adapter training, merged into full model weights
- **Primary task:** Evidence-linked adjuvant extraction from title+abstract text

## Prompt Used for Inference

### System prompt

```text
You are a biomedical information extraction assistant.
```

### User instruction template

```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.
```

### Input format

```text
Title: <paper title>
Abstract: <paper abstract>
```

The model receives the user instruction template followed by the title/abstract text.

## Actual Output Format Target

The model is prompted to return a JSON array of objects with exactly two keys:

```json
[
  {
    "adjuvant": "<string>",
    "evidence": "<string>"
  }
]
```

Expected behavior:
- Return a JSON array (can be empty: `[]`).
- Each object must contain:
  - `adjuvant`: normalized or near-normalized adjuvant name
  - `evidence`: supporting text snippet from the same input abstract
- No extra keys and no explanatory text outside JSON.

## Input/Output Example

### Example Input

```text
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.
```

### Expected Output

```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."
  }
]
```

### Notes on Output Validity

- Output must be valid JSON.
- Output must be a JSON array (use `[]` if no supported adjuvant is found).
- Each item should include only `adjuvant` and `evidence`.
- Evidence text should come from the provided input abstract.

## Working Inference Code (Validated)

```python
import torch
import json
from 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))
```

## Intended Use

This model is intended for research workflows in biomedical literature mining, especially:

- infectious disease vaccine literature curation
- vaccine adjuvant concept extraction
- evidence-linked information extraction for downstream manual review

This model is not intended for clinical decision-making.

## Training Data and Split Context

The model was trained on a curated infectious disease adjuvant corpus derived from VIOLIN ecosystem resources.

- Corpus size used in workflow: 298 abstracts
- Fixed split framework used across models:
  - 256 train
  - 13 validation
  - 29 test

## Training Configuration (Fixed Manuscript Setting)

- LoRA rank (`r`): 8
- Learning rate: `2e-4`
- Epochs: `5`
- Quantization during fine-tuning: 4-bit NF4 with double quantization
- Compute dtype: float16
- Per-device batch size and gradient accumulation were configured for stable updates across model families.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
repo_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",
)
```

## Prompting Recommendation

Use prompts that explicitly request structured JSON output containing only:

- `adjuvant`
- `evidence`

and restrict extra commentary to reduce parsing errors.

## Limitations

- Evaluated on a focused infectious-disease adjuvant corpus; broader-domain generalization is not guaranteed.
- Performance depends on abstract quality and terminology variation.
- Structured output may still require post-processing and manual validation.

## Ethical and Safety Notes

- Outputs can contain extraction errors or unsupported predictions.
- Human review is required before downstream knowledge integration.
- Not for diagnosis, treatment, or direct patient-care decisions.

## Reproducibility Resources

Code, notebooks, and workflow details are available at:  
https://github.com/hurlab/Infectious-Disease-Adjuvant-LLM-Fine-tuning


## Citation

If you use this model, please cite the associated manuscript and project repository.

## Contact

For questions, please contact hasin.rehana@und.edu.