SmolLM3-NL2Prompt-3B

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.

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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)

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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

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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

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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))