Instructions to use luzimu/WebGen-LM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luzimu/WebGen-LM-7B with Transformers:

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

pipe = pipeline("text-generation", model="luzimu/WebGen-LM-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luzimu/WebGen-LM-7B")
model = AutoModelForCausalLM.from_pretrained("luzimu/WebGen-LM-7B")
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

vLLM

How to use luzimu/WebGen-LM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luzimu/WebGen-LM-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luzimu/WebGen-LM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luzimu/WebGen-LM-7B

SGLang

How to use luzimu/WebGen-LM-7B 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 "luzimu/WebGen-LM-7B" \
    --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": "luzimu/WebGen-LM-7B",
		"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 "luzimu/WebGen-LM-7B" \
        --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": "luzimu/WebGen-LM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luzimu/WebGen-LM-7B with Docker Model Runner:
```
docker model run hf.co/luzimu/WebGen-LM-7B
```

Improve model card: Add code-generation tag and sample usage

by nielsr HF Staff - opened Aug 12, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+37

-1

Files changed (1) hide show

README.md +37 -1

README.md CHANGED Viewed

@@ -5,11 +5,13 @@ datasets:
 - luzimu/WebGen-Bench
 language:
 - en
 license: mit
 metrics:
 - accuracy
 pipeline_tag: text-generation
-library_name: transformers
 ---
 # WebGen-LM
@@ -30,6 +32,40 @@ The WebGen-LM family of models are as follows:
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b0bfef2f2f9c345b87e673/ADt1JdvKw-IZ_xnS17adL.png)
 ## Citation

 - luzimu/WebGen-Bench
 language:
 - en
+library_name: transformers
 license: mit
 metrics:
 - accuracy
 pipeline_tag: text-generation
+tags:
+- code-generation
 ---
 # WebGen-LM
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b0bfef2f2f9c345b87e673/ADt1JdvKw-IZ_xnS17adL.png)
+## Sample Usage
+You can use this model with the Hugging Face `transformers` library.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+model_id = "luzimu/WebGen-LM-7B" # This model card refers to WebGen-LM-7B
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+# Example for website generation
+user_prompt = "Generate a simple HTML page with a heading 'Hello, World!' and a paragraph of lorem ipsum text."
+messages = [
+    {"role": "user", "content": user_prompt}
+]
+# Apply chat template for instruction-following format
+text_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+# Generate output
+model_inputs = tokenizer(text_input, return_tensors="pt").to(model.device)
+generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=500, do_sample=True, temperature=0.01, top_k=50, top_p=0.95)
+# Decode and print the generated code
+generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(generated_text)
+# Example using Hugging Face pipeline for simpler inference
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+result = generator(user_prompt, max_new_tokens=500, do_sample=True, temperature=0.01, top_k=50, top_p=0.95)
+print(result[0]['generated_text'])
+```
 ## Citation