Model Card for YoussefAhmed26/SmolLM3-NL2Prompt-3B
A 3B-parameter prompt-generation model built to convert natural-language instructions into structured, high-quality prompts.
This model was developed using curated data generated from multiple frontier LLMs, including ChatGPT-5, Claude Sonnet 4.5, and Gemini 2.5 Pro.
Model Description
SmolLM3-NL2Prompt-3B is a lightweight prompt-generation model designed to take free-form user instructions and transform them into optimized, structured prompts for downstream LLMs.
It was created using a dataset of prompt examples produced by several top-tier AI systems (ChatGPT-5, Claude Sonnet 4.5, and Gemini 2.5 Pro), normalized and aligned using custom formatting rules.
- Developed by: Youssef Ahmed
- Model type: Prompt-generation LLM
- Size: 3B parameters
- Language(s): English
- License: MIT (or your chosen license)
- Finetuned from: SmolLM3 base model
- Model ID:
YoussefAhmed26/SmolLM3-NL2Prompt-3B
Model Sources
- Repository: https://huggingface.co/YoussefAhmed26/SmolLM3-NL2Prompt-3B
- Paper: None
- Demo: (optional)
Uses
Direct Use
- Convert natural language instructions into optimized prompts
- Standardize user inputs for agents, chatbots, or pipelines
- Improve clarity and structure before querying larger LLMs
- Reduce ambiguity in user queries
Downstream Use
- Used as a preprocessing module in LLM-based applications
- Helps maintain consistent quality in multi-agent systems
- Useful for developers building internal tools or automated workflows
Out-of-Scope Use
- Factual reasoning
- Safety-critical or legal/medical uses
- Attempts to imitate proprietary models used during prompt collection
Bias, Risks, and Limitations
- Inherits stylistic preferences from ChatGPT, Claude, and Gemini
- May generate overly structured or overly formal prompts
- Not responsible for fact-checking or content correctness
- Outputs depend on user input clarity
Recommendations
- Always review generated prompts before using them
- Use additional filtering in production systems
- Ensure compliance with the terms of the LLMs whose outputs were used for building the dataset
How to Get Started
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "YoussefAhmed26/SmolLM3-NL2Prompt-3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
input_text = "Write a prompt for summarizing a scientific article."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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