Instructions to use ThaiLLM/ThaiLLM-27B-Prescreen-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThaiLLM/ThaiLLM-27B-Prescreen-Preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="ThaiLLM/ThaiLLM-27B-Prescreen-Preview")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ThaiLLM/ThaiLLM-27B-Prescreen-Preview")
model = AutoModelForMultimodalLM.from_pretrained("ThaiLLM/ThaiLLM-27B-Prescreen-Preview", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ThaiLLM/ThaiLLM-27B-Prescreen-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThaiLLM/ThaiLLM-27B-Prescreen-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThaiLLM/ThaiLLM-27B-Prescreen-Preview",
		"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/ThaiLLM/ThaiLLM-27B-Prescreen-Preview

SGLang

How to use ThaiLLM/ThaiLLM-27B-Prescreen-Preview 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 "ThaiLLM/ThaiLLM-27B-Prescreen-Preview" \
    --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": "ThaiLLM/ThaiLLM-27B-Prescreen-Preview",
		"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 "ThaiLLM/ThaiLLM-27B-Prescreen-Preview" \
        --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": "ThaiLLM/ThaiLLM-27B-Prescreen-Preview",
		"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 ThaiLLM/ThaiLLM-27B-Prescreen-Preview with Docker Model Runner:
```
docker model run hf.co/ThaiLLM/ThaiLLM-27B-Prescreen-Preview
```

PumeTu commited on Jan 16

Commit

fe7a7df

verified ·

1 Parent(s): 6a6009c

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -0

README.md CHANGED Viewed

@@ -7,6 +7,9 @@ license: apache-2.0
 ThaiLLM-27B-Prescreen is a specialized model for initial screening of the disease the patient could possibly be suffering from, the department the patient should go to, and the severity of the condition. The model is a supervised fine-tuned model from [ThaiLLM-27B](https://huggingface.co/ThaiLLM/ThaiLLM-27B).
 ## Performance
 We evaluated our supervised-finetuned model against the base ThaiLLM-27B and Medgemma-27B-it, where we see that after SFT the ThaiLLM-27B-Prescreen rivals Medgemma-27B-it on our prescreen task.

 ThaiLLM-27B-Prescreen is a specialized model for initial screening of the disease the patient could possibly be suffering from, the department the patient should go to, and the severity of the condition. The model is a supervised fine-tuned model from [ThaiLLM-27B](https://huggingface.co/ThaiLLM/ThaiLLM-27B).
+## Data
+The model was trained on a chat style dataset, similar to that of the example in the usage below. The dataset comprises of 989 conversations that emulate a conversation betwen a patient and a doctor with the label being the disease, department, and severity that the doctor determines.
 ## Performance
 We evaluated our supervised-finetuned model against the base ThaiLLM-27B and Medgemma-27B-it, where we see that after SFT the ThaiLLM-27B-Prescreen rivals Medgemma-27B-it on our prescreen task.