| --- |
| license: other |
| license_name: link-attribution |
| license_link: https://dejanmarketing.com/link-attribution/ |
| library_name: transformers |
| base_model: google/gemma-3-270m |
| tags: |
| - reverse-prompting |
| - prompt-reconstruction |
| - gemma |
| - text-generation |
| pipeline_tag: text-generation |
| --- |
| |
| # Reverse Prompter |
|
|
| A fine-tuned [google/gemma-3-270m](https://huggingface.co/google/gemma-3-270m) model that reconstructs the most likely prompt from an AI assistant's response. |
|
|
| Given an AI-generated text, the model generates candidate prompts that could have produced it. |
|
|
| ## How It Works |
|
|
| The model was trained on prompt-response pairs formatted as: |
|
|
| ``` |
| {response}\n###\n{prompt} |
| ``` |
|
|
| At inference time, you provide the response followed by the `\n###\n` separator, and the model generates the reconstructed prompt. |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained("dejanseo/reverse-prompter", torch_dtype="bfloat16").cuda().eval() |
| tokenizer = AutoTokenizer.from_pretrained("dejanseo/reverse-prompter") |
| |
| response_text = "Your AI-generated text here" |
| prompt = response_text.strip() + "\n###\n" |
| |
| inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=256, penalty_alpha=0.3, top_k=4) |
| |
| generated = outputs[0][inputs["input_ids"].shape[-1]:] |
| reconstructed_prompt = tokenizer.decode(generated, skip_special_tokens=True).strip() |
| print(reconstructed_prompt) |
| ``` |
|
|
| For best results, run generation across multiple contrastive search configurations and rank outputs by perplexity. See the companion Streamlit app for a full implementation. |
|
|
| ## Training Data |
|
|
| The training dataset was generated synthetically using Gemini 2.5 Flash via Vertex AI in a three-stage pipeline: |
|
|
| ### 1. Prompt Generation |
|
|
| 100,000 diverse prompts were generated across five categories (20 each per batch): |
|
|
| - Mid-tail, search query style (single or multi-faceted) |
| - Long-tail, search query style (multi-faceted) |
| - Simple, prompt-like (single-faceted) |
| - Typical, prompt-like (single or multi-faceted) |
| - Detailed, prompt-like (multi-faceted) |
|
|
| Generation was parallelized with 100 concurrent API calls in batches of 100 prompts, with results stored in SQLite. |
|
|
| ### 2. Response Generation |
|
|
| Each prompt was sent back to Gemini 2.5 Flash (with thinking disabled) to produce a corresponding AI assistant response. This was also parallelized at 100 concurrent calls. |
|
|
| ### 3. Tokenization |
|
|
| Prompt-response pairs were formatted as `{response}\n###\n{prompt}<eos>` and tokenized using the Gemma 3 tokenizer. Labels were masked (`-100`) over the response and separator tokens so the model only learns to predict the prompt portion. Tokenization was done in batches of 5,000 and concatenated into the final dataset. |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |---|---| |
| | Base model | google/gemma-3-270m | |
| | Method | Full fine-tune | |
| | Precision | bfloat16 | |
| | Epochs | 1 | |
| | Batch size | 2 | |
| | Gradient accumulation | 8 (effective batch size 16) | |
| | Learning rate | 5e-5 | |
| | Warmup steps | 100 | |
| | Max sequence length | 2048 | |
| | Optimizer | AdamW (torch fused) | |
| | Gradient checkpointing | Enabled | |
| | Training time | 4h 14m | |
| | GPU | NVIDIA GeForce RTX 4090 (24 GB) | |
| | CPU | AMD Ryzen 9 7950X3D 16-Core | |
| | RAM | 128 GB | |
|
|
| ### Training Loss |
|
|
|  |
|
|
| ## Inference Strategy |
|
|
| The companion app uses contrastive search with a sweep over configurations: |
|
|
| - `top_k`: [2, 4, 6, 15] |
| - `penalty_alpha`: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6] |
|
|
| This produces up to 24 candidate prompts per input. Candidates are deduplicated and ranked by perplexity (lower is better). Token-level probabilities provide a confidence signal for each word in the reconstruction. |
|
|
| ## Limitations |
|
|
| - Prompt reconstruction is inherently probabilistic. The model returns plausible prompts, not necessarily the exact original. |
| - Performance is best on responses typical of AI assistants. Non-standard or very short inputs may produce lower-quality reconstructions. |
| - The model inherits the capabilities and limitations of the gemma-3-270m base model. |
|
|
| ## Author |
|
|
| [Dejan AI](https://dejan.ai/) |
|
|
