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

Libraries

How to use tencent/Penguin-VL-2B with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Penguin-VL-2B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tencent/Penguin-VL-2B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tencent/Penguin-VL-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Penguin-VL-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": "tencent/Penguin-VL-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Penguin-VL-2B

SGLang

How to use tencent/Penguin-VL-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 "tencent/Penguin-VL-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": "tencent/Penguin-VL-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tencent/Penguin-VL-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": "tencent/Penguin-VL-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Penguin-VL-2B with Docker Model Runner:
```
docker model run hf.co/tencent/Penguin-VL-2B
```

lkeab

Cyril666 commited on Mar 11

Commit

26ac2ce

1 Parent(s): 7617afb

Update README.md (#6)

Browse files

- Update README.md (8fae3f448564267762a169521f47966b44af3a99)

Co-authored-by: Boqiang Zhang <Cyril666@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -101,7 +101,9 @@ inputs = processor(
     return_tensors="pt",
 )
-inputs = {k: v.to("cuda") for k, v in inputs.items() if isinstance(v, torch.Tensor)}
 output_ids = model.generate(**inputs, max_new_tokens=128)
 response = processor.decode(output_ids[0], skip_special_tokens=True)

     return_tensors="pt",
 )
+inputs = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+if "pixel_values" in inputs:
+    inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
 output_ids = model.generate(**inputs, max_new_tokens=128)
 response = processor.decode(output_ids[0], skip_special_tokens=True)