Instructions to use Ftest/VTLlava with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ftest/VTLlava with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ftest/VTLlava")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Ftest/VTLlava", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ftest/VTLlava with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ftest/VTLlava" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ftest/VTLlava", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ftest/VTLlava
- SGLang
How to use Ftest/VTLlava 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 "Ftest/VTLlava" \ --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": "Ftest/VTLlava", "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 "Ftest/VTLlava" \ --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": "Ftest/VTLlava", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ftest/VTLlava with Docker Model Runner:
docker model run hf.co/Ftest/VTLlava
| { | |
| "_name_or_path": "liuhaotian/llava-v1.6-mistral-7b", | |
| "architectures": [ | |
| "LlavaMistralForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "freeze_mm_mlp_adapter": false, | |
| "freeze_mm_vision_resampler": false, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "image_aspect_ratio": "anyres", | |
| "image_crop_resolution": 224, | |
| "image_grid_pinpoints": [ | |
| [ | |
| 336, | |
| 672 | |
| ], | |
| [ | |
| 672, | |
| 336 | |
| ], | |
| [ | |
| 672, | |
| 672 | |
| ], | |
| [ | |
| 1008, | |
| 336 | |
| ], | |
| [ | |
| 336, | |
| 1008 | |
| ] | |
| ], | |
| "image_split_resolution": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 14336, | |
| "max_position_embeddings": 32768, | |
| "mm_hidden_size": 1024, | |
| "mm_patch_merge_type": "spatial_unpad", | |
| "mm_projector_lr": null, | |
| "mm_projector_type": "mlp2x_gelu", | |
| "mm_resampler_type": null, | |
| "mm_use_im_patch_token": false, | |
| "mm_use_im_start_end": false, | |
| "mm_vision_select_feature": "patch", | |
| "mm_vision_select_layer": -2, | |
| "mm_vision_tower": "openai/clip-vit-large-patch14-336", | |
| "mm_vision_tower_lr": 2e-06, | |
| "model_type": "llava_mistral", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "rms_norm_eps": 1e-05, | |
| "rope_theta": 1000000.0, | |
| "sliding_window": null, | |
| "tie_word_embeddings": false, | |
| "tokenizer_model_max_length": 4096, | |
| "tokenizer_padding_side": "left", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.37.2", | |
| "tune_mm_mlp_adapter": false, | |
| "tune_mm_vision_resampler": false, | |
| "unfreeze_mm_vision_tower": true, | |
| "use_cache": true, | |
| "use_mm_proj": true, | |
| "vocab_size": 32000 | |
| } | |