Instructions to use Yemen-JPT/WebProcessor-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yemen-JPT/WebProcessor-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Yemen-JPT/WebProcessor-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yemen-JPT/WebProcessor-v1")
model = AutoModelForCausalLM.from_pretrained("Yemen-JPT/WebProcessor-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yemen-JPT/WebProcessor-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yemen-JPT/WebProcessor-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yemen-JPT/WebProcessor-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Yemen-JPT/WebProcessor-v1

SGLang

How to use Yemen-JPT/WebProcessor-v1 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 "Yemen-JPT/WebProcessor-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yemen-JPT/WebProcessor-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Yemen-JPT/WebProcessor-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yemen-JPT/WebProcessor-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Yemen-JPT/WebProcessor-v1 with Docker Model Runner:
```
docker model run hf.co/Yemen-JPT/WebProcessor-v1
```

WebProcessor-v1

File size: 1,476 Bytes

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{
    "model": {
        "bos_token_id": 151643,
        "context_length": 40960,
        "decoder": {
            "session_options": {
                "log_id": "onnxruntime-genai",
                "provider_options": []
            },
            "filename": "model.onnx",
            "head_size": 128,
            "hidden_size": 2560,
            "inputs": {
                "input_ids": "input_ids",
                "attention_mask": "attention_mask",
                "past_key_names": "past_key_values.%d.key",
                "past_value_names": "past_key_values.%d.value"
            },
            "outputs": {
                "logits": "logits",
                "present_key_names": "present.%d.key",
                "present_value_names": "present.%d.value"
            },
            "num_attention_heads": 32,
            "num_hidden_layers": 36,
            "num_key_value_heads": 8
        },
        "eos_token_id": 151645,
        "pad_token_id": 151643,
        "type": "qwen3",
        "vocab_size": 151936
    },
    "search": {
        "diversity_penalty": 0.0,
        "do_sample": false,
        "early_stopping": true,
        "length_penalty": 1.0,
        "max_length": 40960,
        "min_length": 0,
        "no_repeat_ngram_size": 0,
        "num_beams": 1,
        "num_return_sequences": 1,
        "past_present_share_buffer": true,
        "repetition_penalty": 1.0,
        "temperature": 1.0,
        "top_k": 50,
        "top_p": 1.0
    }
}