ViX-Ray — Fine-tuned Medical Vision-Language Models

Fine-tuned weights for Vietnamese chest X-ray report generation across 3 clinical tasks and 6 model architectures.

Best overall performance: Qwen2-VL-7B across all 3 tasks.

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Tasks

#	Task	Description
1	finding	Generate radiology findings from a chest X-ray image
2	impression	Generate the clinical impression (final diagnosis) from a chest X-ray image
3	multi	Multi-turn dialogue — findings → impression via conversation history

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Models

Key	Base model	Size
Intern	InternVL2.5-1B	1B
Vintern	Vintern-1B-v3.5	1B
Qwen2B	Qwen2-VL-2B-Instruct	2B
Qwen7B	Qwen2-VL-7B-Instruct ⭐	7B
MiniCPM	MiniCPM-V-2_6	8B
LaVy	LaVy-Instruct	7B

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Quick Start

1. Install

pip install huggingface_hub transformers torch torchvision pillow

For Qwen models, also install:

pip install qwen-vl-utils

For Intern / Vintern models, also install:

pip install decord

For MiniCPM, pin versions:

pip install Pillow==10.1.0 torch==2.1.2 torchvision==0.16.2 transformers==4.40.0 sentencepiece==0.1.99 decord

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2. Download a model zip

# task  : finding | impression | multi
# model : Intern | Vintern | Qwen2B | Qwen7B | MiniCPM | LaVy

huggingface-cli download presencesw/ViX-Ray <task>/<Model>.zip \
    --repo-type model --local-dir ./

Example — download the best model for finding:

huggingface-cli download presencesw/ViX-Ray finding/Qwen7B.zip \
    --repo-type model --local-dir ./

Download all models at once:

huggingface-cli download presencesw/ViX-Ray \
    --repo-type model --local-dir ./vix_ray_models

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3. Unzip

unzip <task>/<Model>.zip -d ./models/<task>/
# result: ./models/<task>/<Model>/

Or in Python:

import zipfile
with zipfile.ZipFile("<task>/<Model>.zip") as zf:
    zf.extractall("./models/<task>/")

---

4. Load & infer

Set model_path = "./models/<task>/<Model>" then use the snippet for your model family.

Qwen2-VL (Qwen2B / Qwen7B)

from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model_path = "./models/<task>/<Model>"

model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_path, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "your_image.jpg"},
            {"type": "text",  "text": "Mô tả hình ảnh X-quang ngực này."},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(text=[text], images=image_inputs, videos=video_inputs,
                   padding=True, return_tensors="pt").to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [o[len(i):] for i, o in zip(inputs.input_ids, generated_ids)]
print(processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True)[0])

InternVL / Vintern (Intern / Vintern)

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

model_path = "./models/<task>/<Model>"

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)

MEAN, STD = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
transform = T.Compose([
    T.Lambda(lambda img: img.convert("RGB")),
    T.Resize((448, 448), interpolation=InterpolationMode.BICUBIC),
    T.ToTensor(),
    T.Normalize(mean=MEAN, std=STD),
])

pixel_values = transform(Image.open("your_image.jpg")).unsqueeze(0).to(torch.bfloat16).cuda()
response = model.chat(tokenizer, pixel_values, "<image>\nMô tả hình ảnh X-quang ngực này.",
                      dict(max_new_tokens=512, do_sample=True))
print(response)

MiniCPM-V

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

model_path = "./models/<task>/<Model>"

model = AutoModel.from_pretrained(
    model_path, trust_remote_code=True,
    attn_implementation="sdpa", torch_dtype=torch.bfloat16
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

image = Image.open("your_image.jpg").convert("RGB")
msgs = [{"role": "user", "content": [image, "Mô tả hình ảnh X-quang ngực này."]}]
print(model.chat(image=None, msgs=msgs, tokenizer=tokenizer))

LaVy

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

model_path = "./models/<task>/<Model>"

model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

inputs = processor(
    images=Image.open("your_image.jpg").convert("RGB"),
    text="Mô tả hình ảnh X-quang ngực này.",
    return_tensors="pt"
).to("cuda")

outputs = model.generate(**inputs, max_new_tokens=512)
print(processor.batch_decode(outputs, skip_special_tokens=True)[0])

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Multi-turn (Task 3)

For the multi task, pass conversation history between turns:

# Turn 1 — findings
response1 = ...  # run inference as above

# Turn 2 — impression (append assistant turn then ask)
messages.append({"role": "assistant", "content": [{"type": "text", "text": response1}]})
messages.append({"role": "user",      "content": [{"type": "text", "text": "Kết luận bệnh gì?"}]})
response2 = ...  # run inference again with updated messages

See readme/<task>_<Model>.md for the full per-model multi-turn example.

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Full Model Table

Task	Model	Base	Zip path
finding	Intern	InternVL2.5-1B	finding/Intern.zip
finding	Vintern	Vintern-1B-v3.5	finding/Vintern.zip
finding	Qwen2B	Qwen2-VL-2B	finding/Qwen2B.zip
finding	Qwen7B ⭐	Qwen2-VL-7B	finding/Qwen7B.zip
finding	MiniCPM	MiniCPM-V-2_6	finding/MiniCPM.zip
finding	LaVy	LaVy-Instruct	finding/LaVy.zip
impression	Intern	InternVL2.5-1B	impression/Intern.zip
impression	Vintern	Vintern-1B-v3.5	impression/Vintern.zip
impression	Qwen2B	Qwen2-VL-2B	impression/Qwen2B.zip
impression	Qwen7B ⭐	Qwen2-VL-7B	impression/Qwen7B.zip
impression	MiniCPM	MiniCPM-V-2_6	impression/MiniCPM.zip
impression	LaVy	LaVy-Instruct	impression/LaVy.zip
multi	Intern	InternVL2.5-1B	multi/Intern.zip
multi	Vintern	Vintern-1B-v3.5	multi/Vintern.zip
multi	Qwen2B	Qwen2-VL-2B	multi/Qwen2B.zip
multi	Qwen7B ⭐	Qwen2-VL-7B	multi/Qwen7B.zip
multi	MiniCPM	MiniCPM-V-2_6	multi/MiniCPM.zip
multi	LaVy	LaVy-Instruct	multi/LaVy.zip

Per-model details (installation, full inference code) are in readme/<task>_<Model>.md.

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Citation

If you use these models or the ViX-Ray dataset in your research, please cite:

@article{nguyen2026vix,
  title={ViX-Ray: A Vietnamese Chest X-Ray Dataset for Vision-Language Models},
  author={Nguyen, Duy Vu Minh and Truong, Chinh Thanh and Tran, Phuc Hoang and Le, Hung Tuan and Dat, Nguyen Van-Thanh and Pham, Trung Hieu and Van Nguyen, Kiet},
  journal={arXiv preprint arXiv:2603.15513},
  year={2026}
}