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Model: dejanseo/reverse-prompter Source: Original Platform
2026-10-25 02:14:17 +08:00
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+---
+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
+
+![Training Loss](assets/train-loss.png)
+
+## 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/)