Instructions to use Sai1290/X-Rays-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sai1290/X-Rays-LLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Sai1290/X-Rays-LLM")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Sai1290/X-Rays-LLM")
model = AutoModelForMultimodalLM.from_pretrained("Sai1290/X-Rays-LLM", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sai1290/X-Rays-LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sai1290/X-Rays-LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sai1290/X-Rays-LLM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Sai1290/X-Rays-LLM

SGLang

How to use Sai1290/X-Rays-LLM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Sai1290/X-Rays-LLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sai1290/X-Rays-LLM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Sai1290/X-Rays-LLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sai1290/X-Rays-LLM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Sai1290/X-Rays-LLM with Docker Model Runner:
```
docker model run hf.co/Sai1290/X-Rays-LLM
```

X-Rays-LLM / README.md

Sai1290

Update README.md

2c1f736 verified about 1 year ago

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---
tags:
- vision-language
- multimodal
- image-question-answering
- biomedical
- transformers
- huggingface
- fastvision
license: openrail
language:
- en
datasets:
- axiong/pmc_oa_demo
library_name: transformers
model-index:
  - name: Medical Image QA Model (PMC-OA)
    results: []
---
# 🩺 Medical Image QA Model — Vision-Language Expert

This is a multimodal model fine-tuned for **image-based biomedical question answering and captioning**, based on scientific figures from [PMC Open Access subset](https://huggingface.co/datasets/axiong/pmc_oa_demo). The model takes a biomedical image and an optional question, then generates an expert-level description or answer.

---

## 🧠 Model Architecture

- **Base Model:** `FastVisionModel` (e.g., a BLIP, MiniGPT4, or Flamingo-style model)
- **Backbone:** Vision encoder + LLM (supports `apply_chat_template` for prompt formatting)
- **Trained for Tasks:**
  - Biomedical image captioning
  - Image-based question answering

---

## 🧬 Dataset

- **Name:** [axiong/pmc_oa_demo](https://huggingface.co/datasets/axiong/pmc_oa_demo)
- **Samples:** 100 samples (demo)
- **Fields:**
  - `image`: Biomedical figure (from scientific paper)
  - `caption`: Expert-written caption
  - `question`: (optional) User query about the image
  - `answer`: (optional) Expert response

---

## 🧪 Example Usage

### 🔍 Visual Inference with Instruction & Optional Question

```python
from transformers import TextStreamer
import matplotlib.pyplot as plt
# Prepare model and tokenizer
FastVisionModel.for_inference(model)
sample = dataset[10]
image = sample["image"]
caption = sample.get("caption", "")
# Display the image
plt.imshow(image)
plt.axis('off')
plt.title("Input Image")
plt.show()
instruction = "You are an expert Doctor. Describe accurately what you see in this image."
question = input("Please enter your question about the image (or press Enter to skip): ").strip()
# Build messages for the chat template
user_content = [
    {"type": "image", "image": image},
    {"type": "text", "text": instruction}
]
if question:
    user_content.append({"type": "text", "text": question})
messages = [{"role": "user", "content": user_content}]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
inputs = tokenizer(image, input_text, add_special_tokens=False, return_tensors="pt").to("cuda")
streamer = TextStreamer(tokenizer, skip_prompt=True)
_ = model.generate(
    **inputs,
    streamer=streamer,
    max_new_tokens=128,
    use_cache=True,
    temperature=1.5,
    min_p=0.1,
)
# Optional: display true caption for comparison
print("\nGround Truth Caption:\n", caption)

	---
	tags:
	- vision-language
	- multimodal
	- image-question-answering
	- biomedical
	- transformers
	- huggingface
	- fastvision
	license: openrail
	language:
	- en
	datasets:
	- axiong/pmc_oa_demo
	library_name: transformers
	model-index:
	- name: Medical Image QA Model (PMC-OA)
	results: []
	---

	# 🩺 Medical Image QA Model — Vision-Language Expert

	This is a multimodal model fine-tuned for image-based biomedical question answering and captioning, based on scientific figures from [PMC Open Access subset](https://huggingface.co/datasets/axiong/pmc_oa_demo). The model takes a biomedical image and an optional question, then generates an expert-level description or answer.

	---

	## 🧠 Model Architecture

	- Base Model: `FastVisionModel` (e.g., a BLIP, MiniGPT4, or Flamingo-style model)
	- Backbone: Vision encoder + LLM (supports `apply_chat_template` for prompt formatting)
	- Trained for Tasks:
	- Biomedical image captioning
	- Image-based question answering

	---

	## 🧬 Dataset

	- Name: [axiong/pmc_oa_demo](https://huggingface.co/datasets/axiong/pmc_oa_demo)
	- Samples: 100 samples (demo)
	- Fields:
	- `image`: Biomedical figure (from scientific paper)
	- `caption`: Expert-written caption
	- `question`: (optional) User query about the image
	- `answer`: (optional) Expert response

	---

	## 🧪 Example Usage

	### 🔍 Visual Inference with Instruction & Optional Question

	```python
	from transformers import TextStreamer
	import matplotlib.pyplot as plt

	# Prepare model and tokenizer
	FastVisionModel.for_inference(model)

	sample = dataset[10]
	image = sample["image"]
	caption = sample.get("caption", "")

	# Display the image
	plt.imshow(image)
	plt.axis('off')
	plt.title("Input Image")
	plt.show()

	instruction = "You are an expert Doctor. Describe accurately what you see in this image."
	question = input("Please enter your question about the image (or press Enter to skip): ").strip()

	# Build messages for the chat template
	user_content = [
	{"type": "image", "image": image},
	{"type": "text", "text": instruction}
	]
	if question:
	user_content.append({"type": "text", "text": question})

	messages = [{"role": "user", "content": user_content}]
	input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

	inputs = tokenizer(image, input_text, add_special_tokens=False, return_tensors="pt").to("cuda")
	streamer = TextStreamer(tokenizer, skip_prompt=True)

	_ = model.generate(
	**inputs,
	streamer=streamer,
	max_new_tokens=128,
	use_cache=True,
	temperature=1.5,
	min_p=0.1,
	)

	# Optional: display true caption for comparison
	print("\nGround Truth Caption:\n", caption)