README.md

---
license: apache-2.0
language:
- en
base_model: OpenGVLab/InternVL3_5-14B-Pretrained
tags:
- internvl
- internvl_chat
- vision-language
- multimodal
- medical
- scientific
- reasoning
pipeline_tag: image-text-to-text
library_name: transformers
---

# KVL

## Introduction

**KVL** is a Vision-Language Model fine-tuned from [InternVL3_5-14B-Pretrained](https://huggingface.co/OpenGVLab/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](https://huggingface.co/OpenGVLab/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](https://github.com/modelscope/ms-swift) |

### Training Datasets (~4M samples)

| Dataset | Samples | Domain |
|---------|---------|--------|
| [ArXivQA](https://huggingface.co/datasets/MMInstruction/ArxivQA) | 100K | Scientific Papers |
| [VisCon-100k](https://huggingface.co/datasets/tiiuae/viscon-100k) | 100K | Visual Consistency |
| [Visual-CoT](https://huggingface.co/datasets/deepcs233/Visual-CoT) | 404K | Chain-of-Thought Reasoning |
| [SPIQA](https://huggingface.co/datasets/google/spiqa) | 263K | Scientific Paper QA |
| [PMC-VQA](https://huggingface.co/datasets/xmcmic/PMC-VQA) | 330K | Medical (PubMed) |
| [VQA-RAD](https://huggingface.co/datasets/flaviagiammarino/vqa-rad) | 1.7K | Medical Radiology |
| [Path-VQA](https://huggingface.co/datasets/flaviagiammarino/path-vqa) | 20K | Medical Pathology |
| [Kvasir-VQA-x1](https://huggingface.co/datasets/SimulaMet/Kvasir-VQA-x1) | 160K | Medical Endoscopy |
| [InternVL-Chat-SFT](https://huggingface.co/datasets/OpenGVLab/InternVL-Chat-V1-2-SFT-Data) | 1.27M | General VL Conversation |
| [OpenThoughts](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) | 114K | Reasoning |
| [VLAA-Thinking](https://huggingface.co/datasets/UCSC-VLAA/VLAA-Thinking) | 126K | Visual Reasoning |
| [MedMax](https://huggingface.co/datasets/mint-medmax/medmax_data) | 1.14M | Medical Comprehensive |

**Total: ~4 Million samples**

## Quick Start

### Requirements

```bash
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:

```python
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

```python
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](https://github.com/open-compass/VLMEvalKit).

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

```python
from functools import partial
from vlmeval.vlm import InternVLChat

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

Then run evaluation:

```bash
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](https://github.com/OpenGVLab/InternVL) for the excellent base model
- [ms-swift](https://github.com/modelscope/ms-swift) for the training framework
- All dataset creators for their valuable contributions

## License

This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).