Instructions to use MeerDevelopment/Qevi-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MeerDevelopment/Qevi-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="MeerDevelopment/Qevi-2B")
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("MeerDevelopment/Qevi-2B")
model = AutoModelForMultimodalLM.from_pretrained("MeerDevelopment/Qevi-2B", 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 MeerDevelopment/Qevi-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MeerDevelopment/Qevi-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MeerDevelopment/Qevi-2B",
		"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/MeerDevelopment/Qevi-2B

SGLang

How to use MeerDevelopment/Qevi-2B 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 "MeerDevelopment/Qevi-2B" \
    --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": "MeerDevelopment/Qevi-2B",
		"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 "MeerDevelopment/Qevi-2B" \
        --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": "MeerDevelopment/Qevi-2B",
		"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 MeerDevelopment/Qevi-2B with Docker Model Runner:
```
docker model run hf.co/MeerDevelopment/Qevi-2B
```

zandervdm commited on 6 days ago

Commit

2682c6e

verified ·

1 Parent(s): 1150501

Credit TypeSafe Jev as the inspiration; add structured-output tags

Browse files

Files changed (1) hide show

EXPLANATION.md +6 -0

EXPLANATION.md CHANGED Viewed

@@ -3,6 +3,12 @@
 A short explainer for why Qevi-2B answers questions the way it does. Assumes no background.
 For usage, see the [model card](README.md).
 ## What a normal vision model does
 Show a model a photo and ask "is there a ladder?" and it *writes* an answer: it picks the most

 A short explainer for why Qevi-2B answers questions the way it does. Assumes no background.
 For usage, see the [model card](README.md).
+> **Where the idea came from.** [TypeSafe](https://typesafe.ai) published the case for models that
+> return typed, calibrated values instead of generated text, in their System One model **Jev**.
+> Jev works on text; this project asked whether the same trick works on images, and began life
+> named JEVI (*Jev for images*). It is an independent implementation, not affiliated with
+> TypeSafe and not using their method or code.
 ## What a normal vision model does
 Show a model a photo and ask "is there a ladder?" and it *writes* an answer: it picks the most