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 4 days ago

Commit

87e9886

verified ·

1 Parent(s): 79621d1

Fix metric rendering and document tensor parallelism

Browse files

Files changed (1) hide show

README.md +8 -7

README.md CHANGED Viewed

@@ -37,6 +37,7 @@ pip install vllm
 vllm serve Ambition0303/Qwen-HTML \
   --served-model-name qwen-html \
   --dtype bfloat16 \
   --max-model-len 131072 \
   --port 8000
 ```
@@ -54,23 +55,23 @@ coscraper \
   --model qwen-html
 ```
-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 $\mathrm{Unex.f}$.
-| Dataset | Precision | Recall | $F_1$ | $\mathrm{Cor}_p$ | $\mathrm{Cor}_f$ | $\mathrm{Unex.f}$ |
 |---|---:|---:|---:|---:|---:|---:|
 | 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 |
 Metric definitions:
-- **$F_1$:** field-level extraction quality.
-- **$\mathrm{Cor}_p$:** page-level perfect extraction rate.
-- **$\mathrm{Cor}_f$:** field-level cross-page reuse success rate.
-- **$\mathrm{Unex.f}$:** fraction of fields with unexecutable or invalid output.
 ## Limitations

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