Instructions to use HuggingFaceM4/VLM_WebSight_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

VLM_WebSight_finetuned / config.json

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	{
	"_commit_hash": null,
	"_name_or_path": "None",
	"additional_vocab_size": 2,
	"alpha_initializer": "zeros",
	"alpha_type": "float",
	"alphas_initializer_range": 0.0,
	"architectures": [
	"VMistralForVisionText2Text"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_vmistral.VMistralConfig",
	"AutoModelForCausalLM": "modeling_vmistral.VMistralForVisionText2Text"
	},
	"bos_token_id": 1,
	"cross_layer_interval": 1,
	"eos_token_id": 2,
	"freeze_lm_head": false,
	"freeze_text_layers": false,
	"freeze_text_module_exceptions": [],
	"freeze_vision_layers": false,
	"freeze_vision_module_exceptions": [],
	"hidden_act": "silu",
	"hidden_size": 4096,
	"image_token_id": 32001,
	"initializer_range": 0.02,
	"intermediate_size": 14336,
	"max_position_embeddings": 32768,
	"model_type": "vmistral",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"pad_token_id": 0,
	"perceiver_config": {
	"resampler_depth": 3,
	"resampler_head_dim": 96,
	"resampler_n_heads": 16,
	"resampler_n_latents": 64,
	"qk_layer_norms_perceiver": true
	},
	"qk_layer_norms": true,
	"rms_norm_eps": 1e-05,
	"rope_theta": 10000.0,
	"sliding_window": 4096,
	"tie_word_embeddings": false,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.34.0.dev0",
	"use_cache": true,
	"use_resampler": true,
	"vision_config": {
	"hidden_size": 1152,
	"image_size": 960,
	"intermediate_size": 4304,
	"model_type": "vmistral",
	"num_attention_heads": 16,
	"num_hidden_layers": 27,
	"patch_size": 14
	},
	"vocab_size": 32000
	}