Instructions to use Mantis-VL/mfuyu_llava50k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mantis-VL/mfuyu_llava50k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Mantis-VL/mfuyu_llava50k")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("Mantis-VL/mfuyu_llava50k") model = AutoModelForCausalLM.from_pretrained("Mantis-VL/mfuyu_llava50k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Mantis-VL/mfuyu_llava50k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mantis-VL/mfuyu_llava50k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mantis-VL/mfuyu_llava50k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mantis-VL/mfuyu_llava50k
- SGLang
How to use Mantis-VL/mfuyu_llava50k 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 "Mantis-VL/mfuyu_llava50k" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mantis-VL/mfuyu_llava50k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Mantis-VL/mfuyu_llava50k" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mantis-VL/mfuyu_llava50k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Mantis-VL/mfuyu_llava50k with Docker Model Runner:
docker model run hf.co/Mantis-VL/mfuyu_llava50k
| { | |
| "_name_or_path": "adept/fuyu-8b", | |
| "architectures": [ | |
| "MFuyuForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 71013, | |
| "hidden_act": "relu2", | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 4096, | |
| "image_size": 300, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 16384, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 16384, | |
| "model_type": "fuyu", | |
| "num_attention_heads": 64, | |
| "num_channels": 3, | |
| "num_hidden_layers": 36, | |
| "partial_rotary_factor": 0.5, | |
| "patch_size": 30, | |
| "qk_layernorm": true, | |
| "rope_scaling": null, | |
| "rope_theta": 25000.0, | |
| "text_config": { | |
| "model_type": "persimmon", | |
| "vocab_size": 262146 | |
| }, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.37.0", | |
| "use_cache": true, | |
| "vocab_size": 262146 | |
| } | |