Instructions to use autotrust/JEV-27B-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use autotrust/JEV-27B-VL with Transformers:

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

pipe = pipeline("image-text-to-text", model="autotrust/JEV-27B-VL")
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("autotrust/JEV-27B-VL")
model = AutoModelForMultimodalLM.from_pretrained("autotrust/JEV-27B-VL", 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 autotrust/JEV-27B-VL with vLLM:

Install from pip and serve model

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

SGLang

How to use autotrust/JEV-27B-VL 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 "autotrust/JEV-27B-VL" \
    --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": "autotrust/JEV-27B-VL",
		"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 "autotrust/JEV-27B-VL" \
        --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": "autotrust/JEV-27B-VL",
		"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 autotrust/JEV-27B-VL with Docker Model Runner:
```
docker model run hf.co/autotrust/JEV-27B-VL
```

cloudyu commited on 29 days ago

Commit

50efb65

verified ·

1 Parent(s): f31ded2

Model card: add PubMedQA

Browse files

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -107,6 +107,7 @@ Human gold labels (CLINC-150, MTOP, GoEmotions, DBpedia), 800 items.
 | task | result | comparison |
 |---|---|---|
 | **Image recommendation, zero-shot** (MicroLens, covers only) · JEV-27B-VL | **AUC 0.727** | collaborative filtering from 59,045 users' logs 0.728 · titles only 0.649 |
 | **Response judge** (RewardBench, 2,985 pairs) | **89.9** | Gemini 1.5 Pro 88.2 · GPT-4o 86.7 · Claude 3.5 Sonnet 84.2 as judges |
 | **Hallucination guard** (TriviaQA, answer the trusted half) | **96.4% accuracy** | answering everything 71.2% · model's own stated confidence 87.2% |
 | **News recommendation, zero-shot** (MIND) | **AUC 0.642** | best zero-shot baseline 0.606 · LightGBM trained on MIND 0.616 |

 | task | result | comparison |
 |---|---|---|
 | **Image recommendation, zero-shot** (MicroLens, covers only) · JEV-27B-VL | **AUC 0.727** | collaborative filtering from 59,045 users' logs 0.728 · titles only 0.649 |
+| **Biomedical research questions** (PubMedQA test, 500) · JEV-27B-VL | **77.8% accuracy**, zero-shot | human experts 78.0% · GPT-4 zero-shot 75.2% · BioBERT 68.1% |
 | **Response judge** (RewardBench, 2,985 pairs) | **89.9** | Gemini 1.5 Pro 88.2 · GPT-4o 86.7 · Claude 3.5 Sonnet 84.2 as judges |
 | **Hallucination guard** (TriviaQA, answer the trusted half) | **96.4% accuracy** | answering everything 71.2% · model's own stated confidence 87.2% |
 | **News recommendation, zero-shot** (MIND) | **AUC 0.642** | best zero-shot baseline 0.606 · LightGBM trained on MIND 0.616 |