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
```

Qwen-HTML / README.md

Ambition0303

Update README.md

2c75ac5 verified 1 day ago

preview code

Raw History Blame Contribute Delete

2.94 kB

	---
	library_name: transformers
	license: apache-2.0
	base_model: Qwen/Qwen3-4B
	tags:
	- web-scraping
	- information-extraction
	- qwen
	- qwen3
	- co-scraper
	pipeline_tag: text-generation
	---

	# Qwen-HTML 4B

	Qwen-HTML is a lightweight HTML-specialized model for Co-Scraper, a query-aware web data extraction framework. It is initialized from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) and trained for two complementary tasks:

	1. Node identification: select the DOM nodes most likely to contain query-relevant records and attributes.
	2. Scraper synthesis: generate reusable Python extraction code over the pruned HTML.

	The model is designed to reduce the cost and failure rate of web information extraction by working with compact, query-aware DOM views instead of full raw pages.

	## Model description

	- Base model: Qwen3-4B
	- Architecture: `Qwen3ForCausalLM`
	- Precision: bfloat16
	- Context length: 131,072 tokens

	## Intended use

	Deploy the model with [vLLM](https://docs.vllm.ai/) as an OpenAI-compatible service:

	```bash
	pip install vllm

	vllm serve Ambition0303/Qwen-HTML \
	--served-model-name qwen-html \
	--dtype bfloat16 \
	--tensor-parallel-size 1 \
	--max-model-len 131072 \
	--port 8000
	```

	Then use the endpoint with the [Co-Scraper](https://github.com/Ambition-0303/Co-Scraper) inference pipeline:

	```bash
	export OPENAI_BASE_URL="http://localhost:8000/v1"
	export OPENAI_API_KEY="not-required"

	coscraper \
	"Extract the camera model and price." \
	schema.json \
	seed_1.html seed_2.html seed_3.html \
	--model qwen-html
	```

	Set `--tensor-parallel-size` to the number of GPUs used for tensor parallelism. If GPU memory is insufficient for the full 131,072-token context, reduce `--max-model-len` according to your deployment budget.

	## Evaluation

	Results from the Co-Scraper paper. Higher is better except Unex.f.

	\| Dataset \| Precision \| Recall \| F1 \| 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 \|

	Metric definitions:

	- F1 ： 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

	Generated scrapers should still be reviewed before being executed on untrusted pages. Model performance can vary with HTML quality, site structure, and query clarity.

	## Citation

	If you use this model, please cite the Co-Scraper work:

	```bibtex
	@misc{coscraper2026,
	title={Co-Scraper: Query-Aware DOM Pruning and Reusable Scraper Synthesis for Lightweight Web Data Extraction},
	author={Wang, Shoupeng and Qiu, Jiantao and Zhang, Wuyang and He, Conghui},
	year={2026},
	url={https://arxiv.org/abs/2606.14821}