Instructions to use lighteternal/tissuedecision-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lighteternal/tissuedecision-9b with Transformers:

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

pipe = pipeline("image-text-to-text", model="lighteternal/tissuedecision-9b")
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("lighteternal/tissuedecision-9b")
model = AutoModelForMultimodalLM.from_pretrained("lighteternal/tissuedecision-9b", 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 lighteternal/tissuedecision-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lighteternal/tissuedecision-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lighteternal/tissuedecision-9b",
		"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/lighteternal/tissuedecision-9b

SGLang

How to use lighteternal/tissuedecision-9b 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 "lighteternal/tissuedecision-9b" \
    --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": "lighteternal/tissuedecision-9b",
		"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 "lighteternal/tissuedecision-9b" \
        --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": "lighteternal/tissuedecision-9b",
		"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 lighteternal/tissuedecision-9b with Docker Model Runner:
```
docker model run hf.co/lighteternal/tissuedecision-9b
```

tissuedecision-9b / usage_example.py

lighteternal

Rename to TissueDecision-9B: card, tissuedecision_config.json, tissuedecision_server.py, usage_example.py, charts; remove toxdecision_* files

93f1465 verified 17 days ago

Raw History Blame Contribute Delete

2.23 kB

"""TissueDecision-9B with llm2jev: one forward pass per question, label-token probabilities (no text generation).
Start the model first (any GPU with >= 24 GB):
  vllm serve lighteternal/tissuedecision-9b --max-logprobs 256 --return-tokens-as-token-ids --enable-scale-out \
      --enable-prefix-caching --max-model-len 16384 --default-chat-template-kwargs '{"enable_thinking": false}'
Then:  pip install "llm2jev[vision]==0.6.1"  and  python usage_example.py lesion_tile.jpg "Rat liver, H&E"
(`prepare_image` comes from tissuedecision_server.py in the model repo: it resizes to the training pixel budget.)
"""
import json
import os
import sys

from llm2jev import LLM2Jev
from llm2jev.backends import VLLM
from transformers import AutoProcessor

from tissuedecision_server import prepare_image

MODEL = os.environ.get("TISSUEDECISION_MODEL", "lighteternal/tissuedecision-9b")
QUESTIONS = {
    "lesion": {"type": "noul", "instructions": "Is there any histopathological lesion (abnormal finding) in this image?"},
    "finding": {"type": "choice", "instructions": "What is the primary histopathological finding in this image?",
                "criteria": {"necrosis": None, "single cell necrosis": None, "inflammatory cell infiltrate": None,
                             "hemorrhage": None, "vacuolation": None, "no significant findings": None}},
    "organ": {"type": "choice", "instructions": "Which organ is shown?",
              "criteria": {"liver": None, "kidney": None, "thyroid gland": None, "testis": None, "urinary bladder": None,
                           "lung": None, "heart": None, "stomach": None, "salivary gland": None}},
}

if __name__ == "__main__":
    image = sys.argv[1] if len(sys.argv) > 1 else "lesion_tile.jpg"
    context = sys.argv[2] if len(sys.argv) > 2 else None  # e.g. "Rat liver, H&E"; optional
    jev = LLM2Jev(AutoProcessor.from_pretrained(MODEL), VLLM("http://localhost:8000", MODEL), temperature=1.0)
    content = [{"type": "image", "image": prepare_image(image)}] + ([{"type": "text", "text": context}] if context else [])
    answers = jev([{"role": "user", "content": content}], QUESTIONS)  # raw probabilities (calibration: see the server)
    print(json.dumps(answers, indent=1))

	"""TissueDecision-9B with llm2jev: one forward pass per question, label-token probabilities (no text generation).

	Start the model first (any GPU with >= 24 GB):
	vllm serve lighteternal/tissuedecision-9b --max-logprobs 256 --return-tokens-as-token-ids --enable-scale-out \
	--enable-prefix-caching --max-model-len 16384 --default-chat-template-kwargs '{"enable_thinking": false}'
	Then: pip install "llm2jev[vision]==0.6.1" and python usage_example.py lesion_tile.jpg "Rat liver, H&E"
	(`prepare_image` comes from tissuedecision_server.py in the model repo: it resizes to the training pixel budget.)
	"""
	import json
	import os
	import sys

	from llm2jev import LLM2Jev
	from llm2jev.backends import VLLM
	from transformers import AutoProcessor

	from tissuedecision_server import prepare_image

	MODEL = os.environ.get("TISSUEDECISION_MODEL", "lighteternal/tissuedecision-9b")
	QUESTIONS = {
	"lesion": {"type": "noul", "instructions": "Is there any histopathological lesion (abnormal finding) in this image?"},
	"finding": {"type": "choice", "instructions": "What is the primary histopathological finding in this image?",
	"criteria": {"necrosis": None, "single cell necrosis": None, "inflammatory cell infiltrate": None,
	"hemorrhage": None, "vacuolation": None, "no significant findings": None}},
	"organ": {"type": "choice", "instructions": "Which organ is shown?",
	"criteria": {"liver": None, "kidney": None, "thyroid gland": None, "testis": None, "urinary bladder": None,
	"lung": None, "heart": None, "stomach": None, "salivary gland": None}},
	}

	if __name__ == "__main__":
	image = sys.argv[1] if len(sys.argv) > 1 else "lesion_tile.jpg"
	context = sys.argv[2] if len(sys.argv) > 2 else None # e.g. "Rat liver, H&E"; optional
	jev = LLM2Jev(AutoProcessor.from_pretrained(MODEL), VLLM("http://localhost:8000", MODEL), temperature=1.0)
	content = [{"type": "image", "image": prepare_image(image)}] + ([{"type": "text", "text": context}] if context else [])
	answers = jev([{"role": "user", "content": content}], QUESTIONS) # raw probabilities (calibration: see the server)
	print(json.dumps(answers, indent=1))