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

Libraries

How to use CountingSheep/vev-9b with Transformers:

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

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CountingSheep/vev-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": "CountingSheep/vev-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/CountingSheep/vev-9b

SGLang

How to use CountingSheep/vev-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 "CountingSheep/vev-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": "CountingSheep/vev-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 "CountingSheep/vev-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": "CountingSheep/vev-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 CountingSheep/vev-9b with Docker Model Runner:
```
docker model run hf.co/CountingSheep/vev-9b
```

CountingSheep commited on 28 days ago

Commit

048cfaa

verified ·

1 Parent(s): 228bc0b

Model card: adapter loading that also works offline; link the prompt description

Browse files

Files changed (1) hide show

README.md +4 -3

README.md CHANGED Viewed

@@ -96,9 +96,10 @@ processor = AutoProcessor.from_pretrained("CountingSheep/vev-9b")
 `config.json` keeps `mtp_num_hidden_layers` from the base model, but the multi-token-prediction weights are not
 included; reading answers does not use them.
-This gives the language model only. Vev reads its answers from the next-token probabilities of the answer tokens
-(Yes/No, option letters, level digits) at the end of a fixed prompt, which is built in
-[vev/model.py](https://github.com/Xiaooolong/vev/blob/main/vev/model.py).
 ## Evaluation

 `config.json` keeps `mtp_num_hidden_layers` from the base model, but the multi-token-prediction weights are not
 included; reading answers does not use them.
+This gives the model only. Vev reads its answers from the next-token probabilities of the answer tokens (Yes/No,
+option letters, level digits) at the end of a fixed prompt; the prompt template and the readout are described in
+[spec/systemone-api.md, section
+10](https://github.com/Xiaooolong/vev/blob/main/spec/systemone-api.md#10-how-answers-are-computed).
 ## Evaluation