Instructions to use Ambition0303/Qwen-HTML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ambition0303/Qwen-HTML with Transformers:

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

pipe = pipeline("text-generation", model="Ambition0303/Qwen-HTML")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ambition0303/Qwen-HTML")
model = AutoModelForCausalLM.from_pretrained("Ambition0303/Qwen-HTML", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ambition0303/Qwen-HTML with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ambition0303/Qwen-HTML"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ambition0303/Qwen-HTML",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ambition0303/Qwen-HTML

SGLang

How to use Ambition0303/Qwen-HTML 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 "Ambition0303/Qwen-HTML" \
    --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": "Ambition0303/Qwen-HTML",
		"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 "Ambition0303/Qwen-HTML" \
        --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": "Ambition0303/Qwen-HTML",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ambition0303/Qwen-HTML with Docker Model Runner:
```
docker model run hf.co/Ambition0303/Qwen-HTML
```

Ambition0303 commited on 16 days ago

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@@ -61,7 +61,7 @@ Set `--tensor-parallel-size` to the number of GPUs used for tensor parallelism.
 Results from the Co-Scraper paper. Higher is better except Unex.f.
-| Dataset | Precision | Recall | F<sub>1</sub> | Cor<sub>p</sub> | Cor<sub>f</sub> | Unex.f |
 |---|---:|---:|---:|---:|---:|---:|
 | SWDE | 95.93 | 96.24 | 96.00 | 85.87 | 95.51 | 3.19 |
 | LiveWeb-IE | 92.29 | 92.68 | 92.40 | 82.00 | 91.28 | 6.08 |
@@ -71,7 +71,7 @@ Metric definitions:
 - **F<sub>1</sub>:** field-level extraction quality.
 - **Cor<sub>p</sub>:** page-level perfect extraction rate.
 - **Cor<sub>f</sub>:** field-level cross-page reuse success rate.
-- **Unex.f:** fraction of fields with unexecutable or invalid output.
 ## Limitations

 Results from the Co-Scraper paper. Higher is better except Unex.f.
+| Dataset | Precision | Recall | F<sub>1</sub> | Cor<sub>p</sub> | Cor<sub>f</sub> | Unex.<sub>f</sub> |
 |---|---:|---:|---:|---:|---:|---:|
 | SWDE | 95.93 | 96.24 | 96.00 | 85.87 | 95.51 | 3.19 |
 | LiveWeb-IE | 92.29 | 92.68 | 92.40 | 82.00 | 91.28 | 6.08 |
 - **F<sub>1</sub>:** field-level extraction quality.
 - **Cor<sub>p</sub>:** page-level perfect extraction rate.
 - **Cor<sub>f</sub>:** field-level cross-page reuse success rate.
+- **Unex.<sub>f</sub>:** fraction of fields with unexecutable or invalid output.
 ## Limitations