Improve model card: Add code-generation tag and sample usage (#2)
Browse files- Improve model card: Add code-generation tag and sample usage (10518e39a8f4ce777b2638ffbf616c3f4dadac24)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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- luzimu/WebGen-Bench
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language:
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- en
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license: mit
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metrics:
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- accuracy
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pipeline_tag: text-generation
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-
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---
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# WebGen-LM
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## Citation
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- luzimu/WebGen-Bench
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language:
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- en
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library_name: transformers
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license: mit
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metrics:
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- accuracy
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pipeline_tag: text-generation
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tags:
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- code-generation
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---
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# WebGen-LM
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## Sample Usage
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You can use this model with the Hugging Face `transformers` library.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model_id = "luzimu/WebGen-LM-7B" # This model card refers to WebGen-LM-7B
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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# Example for website generation
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user_prompt = "Generate a simple HTML page with a heading 'Hello, World!' and a paragraph of lorem ipsum text."
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messages = [
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{"role": "user", "content": user_prompt}
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]
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# Apply chat template for instruction-following format
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text_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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# Generate output
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model_inputs = tokenizer(text_input, return_tensors="pt").to(model.device)
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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)
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# Decode and print the generated code
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generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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print(generated_text)
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# Example using Hugging Face pipeline for simpler inference
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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result = generator(user_prompt, max_new_tokens=500, do_sample=True, temperature=0.01, top_k=50, top_p=0.95)
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print(result[0]['generated_text'])
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```
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## Citation
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