Instructions to use HuggingFaceM4/VLM_WebSight_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/VLM_WebSight_finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceM4/VLM_WebSight_finetuned", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("HuggingFaceM4/VLM_WebSight_finetuned", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceM4/VLM_WebSight_finetuned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceM4/VLM_WebSight_finetuned" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceM4/VLM_WebSight_finetuned", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceM4/VLM_WebSight_finetuned
- SGLang
How to use HuggingFaceM4/VLM_WebSight_finetuned 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 "HuggingFaceM4/VLM_WebSight_finetuned" \ --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": "HuggingFaceM4/VLM_WebSight_finetuned", "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 "HuggingFaceM4/VLM_WebSight_finetuned" \ --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": "HuggingFaceM4/VLM_WebSight_finetuned", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceM4/VLM_WebSight_finetuned with Docker Model Runner:
docker model run hf.co/HuggingFaceM4/VLM_WebSight_finetuned
Update vision.py
Browse filesWhen flash attention IS available: The code imports the required modules as before
When flash attention is NOT available: The code now raises a clear ImportError with installation instructions
Benefits:
Provides immediate feedback when flash attention is missing
Gives clear installation instructions
Prevents silent failures or confusing errors later in the code
vision.py
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@@ -34,10 +34,16 @@ from .configuration_vmistral import VMistralVisionConfig
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logger = logging.get_logger(__name__)
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-
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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logger = logging.get_logger(__name__)
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# is_flash_attn_2_available() checks if flash_attn is available, if yes, it imports it
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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# if flash_attn is not available, it raises an ImportError, intimating the user to install flash_attn
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else:
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raise ImportError(
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"Flash Attention 2.0 is not available. Please install flash-attn>=2.1.0: "
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"`pip install flash-attn>=2.1.0` or use an alternative attention implementation."
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)
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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