Instructions to use lbasile/llava-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lbasile/llava-2000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="lbasile/llava-2000")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("lbasile/llava-2000") model = AutoModelForImageTextToText.from_pretrained("lbasile/llava-2000") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lbasile/llava-2000 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lbasile/llava-2000" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lbasile/llava-2000", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lbasile/llava-2000
- SGLang
How to use lbasile/llava-2000 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 "lbasile/llava-2000" \ --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": "lbasile/llava-2000", "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 "lbasile/llava-2000" \ --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": "lbasile/llava-2000", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lbasile/llava-2000 with Docker Model Runner:
docker model run hf.co/lbasile/llava-2000
File size: 964 Bytes
5e725f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"architectures": [
"LlavaForConditionalGeneration"
],
"ignore_index": -100,
"image_token_id": 32000,
"image_token_index": 32000,
"model_type": "llava",
"pad_token_id": 32001,
"projector_hidden_act": "gelu",
"text_config": {
"_name_or_path": "lmsys/vicuna-7b-v1.5",
"architectures": [
"LlamaForCausalLM"
],
"max_position_embeddings": 4096,
"model_type": "llama",
"pad_token_id": 0,
"rms_norm_eps": 1e-05,
"torch_dtype": "float16",
"vocab_size": 32064
},
"torch_dtype": "bfloat16",
"transformers_version": "4.37.2",
"vision_config": {
"hidden_size": 1024,
"image_size": 336,
"intermediate_size": 4096,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"patch_size": 14,
"projection_dim": 768,
"vocab_size": 32000
},
"vision_feature_layer": -2,
"vision_feature_select_strategy": "default",
"vocab_size": 32064
}
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