Text Generation
Transformers
Safetensors
English
penguinvl_qwen3
multi-modal
large-language-model
vision-language-model
vision-encoder
conversational
custom_code
Instructions to use tencent/Penguin-VL-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Penguin-VL-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tencent/Penguin-VL-8B", 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-8B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tencent/Penguin-VL-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tencent/Penguin-VL-8B" # 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-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tencent/Penguin-VL-8B
- SGLang
How to use tencent/Penguin-VL-8B 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-8B" \ --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-8B", "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-8B" \ --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-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tencent/Penguin-VL-8B with Docker Model Runner:
docker model run hf.co/tencent/Penguin-VL-8B
| """PenguinVL vision encoder model configuration.""" | |
| from transformers import Qwen3Config | |
| class PenguinVLVisionEncoderConfig(Qwen3Config): | |
| model_type = "penguinvl_vision_encoder" | |
| def __init__( | |
| self, | |
| hidden_size=1536, | |
| intermediate_size=8960, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| num_channels=3, | |
| patch_size=14, | |
| layer_norm_eps=1e-6, | |
| attention_dropout=0.0, | |
| num_key_value_heads=2, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.num_channels = num_channels | |
| self.patch_size = patch_size | |
| self.attention_dropout = attention_dropout | |
| self.num_key_value_heads = num_key_value_heads | |
| self.layer_norm_eps = layer_norm_eps | |