Qwen2.5 3B Blog Key Points

🚀 Qwen2.5-3B-blog-key-points

This model is fine - tuned from Qwen/Qwen2.5-3B-Instruct on the ncls-p/blog-key-points. It excels at extracting key points from blog articles and web content, offering concise bullet - point summaries that encapsulate the essential information.

📚 Documentation

✨ Features

Qwen2.5-3B-blog-key-points is a 3B parameter model specifically fine - tuned for extracting key points from articles. It can process an entire article and generate a concise, bullet - point summary highlighting the most crucial information.

📦 Model Details

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ncls-p/Qwen2.5-3B-blog-key-points"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

article = """
[Your article text here]
"""

prompt = f"""
Extract the key points from the following article:

{article}
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=1024)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

🔧 Technical Details

Training

The model was fine - tuned on the [blog - key - points dataset](https://huggingface.co/datasets/ncls - p/blog - key - points), which contains 200 article - summary pairs. Each pair consists of a full article and a bullet - point summary of key points extracted using AI.

Training Procedure

• Fine - tuning Framework: Unsloth
• Training Data Format:

{
    "instruction": "",
    "input": "Full article content",
    "output": "Here are the key points of the article:\n* Key point 1\n* Key point 2\n* Key point 3\n..."
}

Evaluation

The model was evaluated on its ability to extract relevant key points from articles not seen during training. Evaluation metrics focused on:

1. Relevance: How well the extracted points capture the main ideas of the article
2. Conciseness: The ability to summarize information in a clear, bullet - point format
3. Completeness: Whether all important information is captured in the summary

⚠️ Limitations and Biases

⚠️ Important Note

• The model may inherit biases present in the training data, including potential biases in the source articles or in the key point extraction process.
• Performance may vary depending on the length, complexity, and domain of the input article.
• The model is primarily trained on English - language content and may not perform well on content in other languages.
• As with any summarization model, there is a risk of omitting important information or misrepresenting the original content.

📄 License

The model is released under the CC - BY - 4.0 license.

📖 How to Cite

If you use this model in your research, please cite:

@misc{qwen25-3b-blog-key-points,
  author = {ncls-p},
  title = {Qwen2.5-3B-blog-key-points},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face model repository},
  howpublished = {\url{https://huggingface.co/ncls-p/Qwen2.5-3B-blog-key-points}},
}

📦 Dataset Creation

The dataset used to train this model was created using the [llm - to - blog - key - points - dataset](https://github.com/ncls - p/llm - to - blog - key - points - dataset), a CLI tool that extracts key points from web articles using AI and adds them to a dataset in a structured format.