Lingjian-1.5 is a specialized vision-language model for clinical laboratory medicine. It is designed for microscopic image understanding, cell identification, morphological analysis, clinical laboratory question answering, and key target localization.

Compared with Lingjian-1.0-Chat, Lingjian-1.5 is a qualitative upgrade with improved microscopy understanding accuracy, stronger instruction following, and better conversational ability. These improvements are intended to make the model more reliable in clinical laboratory image analysis, more responsive to user-specified answer formats, and more natural in open-ended or multi-turn dialogue.

Model Details

Model Description

Lingjian-1.5 ("灵检" signifies "Intelligent Laboratory Testing" in Chinese) is a domain-adapted multimodal model for clinical laboratory applications. The model continues the Lingjian series' focus on microscope-based laboratory scenarios, including blood cell morphology, abnormal finding recognition, concise report generation, medical terminology normalization, and localization-style responses.

This release is trained on a broader and more refined mixture of laboratory image-text data, grounding data, instruction-following data, self-cognition data, and general conversation data. The added training mix improves the model's ability to follow detailed instructions while preserving domain-specific visual reasoning.

Developed by: LingJian AI Research Team
Model type: Vision-Language Model for Medical Imaging
Architecture: Qwen3-VL series vision-language architecture
Language(s): Chinese (primary), English
Tensor type: BF16
License: Apache-2.0
Finetuned from model: Qwen/Qwen3-VL-8B-Instruct and Lingjian internal intermediate checkpoints

What's New in Lingjian-1.5

Lingjian-1.5 improves over Lingjian-1.0-Chat in three main areas:

Accuracy: More reliable recognition of clinically relevant microscopic findings, better handling of morphology-oriented questions, and fewer omissions in image descriptions.
Instruction following: Better adherence to requested answer style, level of detail, localization prompts, concise report formats, and domain-specific terminology requirements.
Dialogue: Improved ability to answer natural user questions, maintain context in multi-turn interactions, and provide clearer explanations in both clinical laboratory and general conversational settings.

These statements describe qualitative release-level improvements. Users should run task-specific validation before relying on the model in production or clinical workflows.

Model Sources

Repository: https://huggingface.co/LingJianAI/Lingjian-1.5
Previous version: https://huggingface.co/LingJianAI/Lingjian-1.0-Chat
Paper: Technical report in preparation

Uses

Direct Use

Clinical laboratory image understanding and secondary review
Microscopic cell identification and morphology description
Abnormal finding recognition and concise report drafting
Laboratory quality control and morphology training
Medical education and teaching demonstrations
Research data screening and analysis

Downstream Use

Specialized fine-tuning for specific laboratory sub-domains
Integration with laboratory information systems
Development of automated reporting or review-assistance tools
Dataset triage, annotation assistance, and expert review workflows

Out-of-Scope Use

Primary diagnosis without qualified human supervision
Emergency, high-stakes, or fully automated clinical decision-making
Applications outside clinical laboratory medicine without additional validation
Pixel-level detection tasks requiring specialized detection models
Non-medical image analysis as a general-purpose vision model

Bias, Risks, and Limitations

The model is intended as an auxiliary tool and must not replace qualified medical professionals.
Performance may vary for rare cell types, unusual staining conditions, poor image quality, or out-of-distribution instruments.
Bounding box or localization outputs may be less precise than task-specific detection models.
The model is optimized primarily for Chinese clinical laboratory terminology; English support is available but may be less mature in specialized contexts.
The model may produce plausible but incorrect statements, especially under ambiguous images or insufficient context.
Clinical deployment requires local validation, quality assurance, and workflow-specific risk controls.

Recommendations

Users should verify outputs with qualified clinical laboratory professionals. Clinical decisions should not be based solely on model predictions. For deployment, evaluate the model on representative local data, define human review requirements, and monitor errors across sample types, staining protocols, and instrument sources.

How to Get Started with the Model

The model can be loaded with transformers. Use a recent version with Qwen3-VL support.

python

import torch
from PIL import Image
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model_id = "LingJianAI/Lingjian-1.5"

model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

image = Image.open("microscope_image.jpg").convert("RGB")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "请分析这张显微图像，指出关键细胞和异常形态。"},
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = processor(
    text=[text],
    images=[image],
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids = [
    output_ids[len(input_ids):]
    for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
]
response = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False,
)[0]
print(response)

For localization-style tasks, use explicit instructions such as:

识别图中的关键细胞并标记位置。/bbox

Citation

If you use Lingjian-1.5 in research or products, please cite the model repository and the forthcoming technical report.