BioJev-9B

BioJev-9B is the 9B member of the BioJev biomedical decision-model family.

Built from Qwen/Qwen3.5-9B-Base, it follows the full BioJev pipeline:

Qwen3.5-9B-Base
        ↓
Biomedical DAPT — PubMed + PMC
        ↓
General NLI — SNLI + MNLI + ANLI
        ↓
Biomedical NLI — BioNLI + NLI4CT
        ↓
BioJev-9B

The released checkpoint is a PEFT/QLoRA sequence-classification adapter with:

0 → contradiction
1 → entailment
2 → neutral

Links

• BioJev-9B: https://huggingface.co/Gabriel382/BioJev-9B
• BioJev-4B: https://huggingface.co/Gabriel382/BioJev
• BioJev-Nano: https://huggingface.co/Gabriel382/BioJev-Nano
• Source code: https://github.com/Gabriel382/BioJev

Authors

Creator: Gabriel Henrique Alencar Medeiros
Supervisor: Lina F. Soualmia

Developed at LITIS / Université de Rouen Normandie.

Training recipe

Biomedical DAPT

100M biomedical tokens
80% PubMed / 20% PMC
sequence length 2048
QLoRA
bfloat16
4-bit quantization

General NLI

SNLI      35,000
MNLI      45,000
ANLI R1   20,000
----------------
Total    100,000

Dev results:

Accuracy: 89.20%
Macro-F1: 89.15%
Loss:     0.2958

Biomedical NLI

BioNLI    30,000
NLI4CT    10,000
----------------
Total     40,000

Dev results:

Accuracy: 92.77%
Macro-F1: 92.30%
Loss:     0.1996

Frozen evaluation

Dataset	Role	Macro-F1
BioNLI	held-out biomedical NLI	94.73
NLI4CT	validation seen during full training	72.50
ChemProt	zero-shot relation typing	31.13
DDI2013	zero-shot relation typing	36.35
BioRED	zero-shot relation typing	34.32

Zero-shot relation-transfer mean: 33.93 macro-F1

All-5 mean: 53.80 macro-F1

Reliability

Dataset	Accuracy (%)	Macro-F1 (%)	ECE (%)	NLL	Mean confidence (%)
BioNLI	95.14	94.73	2.20	0.146	97.27
NLI4CT	72.50	72.50	14.70	0.693	86.30
ChemProt	32.24	31.13	10.34	1.995	41.00
DDI2013	39.33	36.35	9.17	1.372	32.27
BioRED	50.11	34.32	6.52	1.156	54.64

Scaling context

Relation-transfer mean macro-F1

BioJev-Nano   21.01
BioJev-4B     30.83
BioJev-9B     33.93

The 9B model improves aggregate transfer relative to 4B, with strong gains on ChemProt and BioRED, while DDI2013 does not improve monotonically with model size.

This is an empirical model-size comparison, not a formal scaling-law study.

Loading BioJev-9B

Install:

pip install -U torch transformers peft accelerate

Optional 4-bit inference:

pip install -U bitsandbytes

Because BioJev is a three-class PEFT sequence classifier, reconstruct the base model with num_labels=3 before loading the adapter.

import torch
from peft import PeftConfig, PeftModelForSequenceClassification
from transformers import AutoModelForSequenceClassification, AutoTokenizer

MODEL = "Gabriel382/BioJev-9B"
LABEL2ID = {"contradiction": 0, "entailment": 1, "neutral": 2}
ID2LABEL = {v: k for k, v in LABEL2ID.items()}

cfg = PeftConfig.from_pretrained(MODEL)

base = AutoModelForSequenceClassification.from_pretrained(
    cfg.base_model_name_or_path,
    num_labels=3,
    label2id=LABEL2ID,
    id2label=ID2LABEL,
    dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto" if torch.cuda.is_available() else None,
)

model = PeftModelForSequenceClassification.from_pretrained(
    base, MODEL, is_trainable=False
)

tokenizer = AutoTokenizer.from_pretrained(MODEL)
if tokenizer.pad_token_id is None:
    tokenizer.pad_token = tokenizer.eos_token

model.config.pad_token_id = tokenizer.pad_token_id
model.eval()

System One compatibility API

The main BioJev repository exposes BioJev checkpoints through:

POST /v1/systemone

Supported types:

choice
noul
score

This is an engineering compatibility bridge over BioJev's NLI classifier, not a native Ollama/System-One checkpoint.

Serve a local copy with:

python scripts/serve_systemone.py \
  --checkpoint /path/to/BioJev-9B \
  --model-name biojev-9b \
  --load-in-4bit \
  --host 0.0.0.0 \
  --port 8000

Scientific status

BioJev is a research project. The model and System One compatibility layer are not validated clinical decision systems and must not be used as the sole basis for diagnosis, treatment, or other high-stakes medical decisions.

Citation

A formal paper citation will be added when the BioJev publication is available. Until then, please cite the BioJev repository and the specific Hugging Face checkpoint used.

Acknowledgements

BioJev-9B was created by Gabriel Henrique Alencar Medeiros under the supervision of Lina F. Soualmia at LITIS / Université de Rouen Normandie.