KVL (Knowledge Vision Language)

Introduction

KVL is a Vision-Language Model fine-tuned from InternVL3_5-14B-Pretrained using Supervised Fine-Tuning (SFT) on a curated collection of approximately 4 million high-quality multimodal samples. The model is designed to excel in scientific, medical, and reasoning-intensive vision-language tasks.

Model Architecture

Component	Details
Base Model	InternVL3_5-14B-Pretrained
Vision Encoder	InternViT-300M (0.3B parameters)
Language Model	Qwen3-14B (14.8B parameters)
Total Parameters	15.1B
Precision	BF16
Architecture	ViT-MLP-LLM (InternVL Chat)

Training Details

Training Configuration

Hyperparameter	Value
Training Type	Full Parameter Fine-tuning
Learning Rate	1e-5
LR Scheduler	Cosine
Epochs	1
Batch Size	2 (per device)
Gradient Accumulation	32
Number of GPUs	8
Effective Batch Size	512
Max Sequence Length	16,384
Optimizer	AdamW (fused)
Weight Decay	0.1
DeepSpeed	ZeRO Stage 3
Framework	ms-swift

Training Datasets (~4M samples)

Dataset	Samples	Domain
ArXivQA	100K	Scientific Papers
VisCon-100k	100K	Visual Consistency
Visual-CoT	404K	Chain-of-Thought Reasoning
SPIQA	263K	Scientific Paper QA
PMC-VQA	330K	Medical (PubMed)
VQA-RAD	1.7K	Medical Radiology
Path-VQA	20K	Medical Pathology
Kvasir-VQA-x1	160K	Medical Endoscopy
InternVL-Chat-SFT	1.27M	General VL Conversation
OpenThoughts	114K	Reasoning
VLAA-Thinking	126K	Visual Reasoning
MedMax	1.14M	Medical Comprehensive

Total: ~4 Million samples

Quick Start

Requirements

pip install transformers>=4.52.1 torch torchvision timm
pip install flash-attn --no-build-isolation  # Optional but recommended

Basic Usage

Since KVL is fine-tuned from InternVL3.5, the usage is identical to InternVL. You can use the standard InternVL inference code:

import torch
from transformers import AutoTokenizer, AutoModel
from PIL import Image
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    return T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
    ])

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1)
        for i in range(1, n + 1) for j in range(1, n + 1)
        if i * j <= max_num and i * j >= min_num
    )
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size
    )

    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_image(image_file, input_size=448, max_num=12):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(img) for img in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

# Load model
model_path = "amoeba04/KVL"
model = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    use_flash_attn=True,
    trust_remote_code=True
).eval().cuda()

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)

# Inference
image = load_image('your_image.jpg').to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=False)

question = '<image>\nDescribe this image in detail.'
response = model.chat(tokenizer, image, question, generation_config)
print(response)

Multi-GPU Inference

model = AutoModel.from_pretrained(
    "amoeba04/KVL",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    use_flash_attn=True,
    trust_remote_code=True,
    device_map="auto"  # Automatic multi-GPU distribution
).eval()

Evaluation with VLMEvalKit

This model is fully compatible with VLMEvalKit.

To add KVL to VLMEvalKit, register it in vlmeval/config.py:

from functools import partial
from vlmeval.vlm import InternVLChat

# Add to internvl3_5 dict or ungrouped dict
"KVL": partial(InternVLChat, model_path="amoeba04/KVL", max_new_tokens=16384, version="V2.0"),

Then run evaluation:

python run.py --data MMBench_DEV_EN --model KVL --verbose

Intended Use

• Scientific Document Understanding: Analyzing figures, tables, and diagrams from scientific papers
• Medical Image Analysis: Radiology, pathology, and endoscopy image interpretation
• Visual Question Answering: General and domain-specific VQA tasks
• Chain-of-Thought Reasoning: Complex visual reasoning with step-by-step explanations

Acknowledgements

• InternVL Team for the excellent base model
• ms-swift for the training framework
• All dataset creators for their valuable contributions

License

This model is released under the Apache 2.0 License.