Instructions to use XiaoduoAILab/Xmodel_VLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XiaoduoAILab/Xmodel_VLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="XiaoduoAILab/Xmodel_VLM", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("XiaoduoAILab/Xmodel_VLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use XiaoduoAILab/Xmodel_VLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "XiaoduoAILab/Xmodel_VLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "XiaoduoAILab/Xmodel_VLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/XiaoduoAILab/Xmodel_VLM
- SGLang
How to use XiaoduoAILab/Xmodel_VLM 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 "XiaoduoAILab/Xmodel_VLM" \ --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": "XiaoduoAILab/Xmodel_VLM", "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 "XiaoduoAILab/Xmodel_VLM" \ --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": "XiaoduoAILab/Xmodel_VLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use XiaoduoAILab/Xmodel_VLM with Docker Model Runner:
docker model run hf.co/XiaoduoAILab/Xmodel_VLM
| { | |
| "_name_or_path": "/data1/liuyang/checkpoints/e_line/xl/iter-0504000/", | |
| "architectures": [ | |
| "XmodelLMForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_xmodel.XModelConfig", | |
| "AutoModelForCausalLM": "modeling_xmodel.XModelForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "freeze_mm_mlp_adapter": false, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "image_aspect_ratio": "pad", | |
| "image_grid_pinpoints": null, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5632, | |
| "max_position_embeddings": 32768, | |
| "mm_hidden_size": 1024, | |
| "mm_projector_lr": null, | |
| "mm_projector_type": "xdpnetv10_4", | |
| "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": "/data1/xuwanting/LLaVA/llava/model/clip-vit-large-patch14-336/", | |
| "model_type": "xmodelvlm", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 4, | |
| "pad_token_id": 0, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.37.2", | |
| "tune_mm_mlp_adapter": false, | |
| "use_cache": true, | |
| "use_mm_proj": true, | |
| "vision_tower_type": "clip", | |
| "vocab_size": 32000 | |
| } | |