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README.md

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+---
+library_name: transformers
+license: cc-by-sa-3.0
+base_model: Falconsai/text_summarization
+tags:
+- generated_from_trainer
+- summarization
+- compression
+- prompt-summarization
+- prompt-compression
+- text_summarization
+- text_compression
+metrics:
+- rouge
+model-index:
+- name: very-small-prompt-compression
+  results: []
+datasets:
+- gravitee-io/dolly-15k-prompt-compression
+---
+
+# very-small-prompt-compression
+
+Interactive demo: [Very Small Prompt Compression (Space)](https://huggingface.co/spaces/gravitee-io/very-small-prompt-compression-demo)
+
+This model is a fine-tuned version of [Falconsai/text_summarization](https://huggingface.co/Falconsai/text_summarization) on the [gravitee-io/dolly-15k-prompt-compression](https://huggingface.co/datasets/gravitee-io/dolly-15k-prompt-compression) dataset.  
+It achieves the following results on the evaluation set:
+- Loss: 2.1583
+- Rouge1: 0.8190
+- Rouge2: 0.6452
+- Rougel: 0.7792
+- Rougelsum: 0.7792
+- Comp Ratio Mean: 0.7395
+- Comp Ratio P90: 0.9091
+- Pct Violations: 0.0004
+
+## Model description
+The `gravitee-io/very-small-prompt-compression` checkpoint distills a compact sequence-to-sequence model that trims short user prompts (≤64 tokens) before they are forwarded to a larger assistant. Instead of full summarization, the decoder focuses on deleting optional tokens, rewriting phrases more succinctly, and stripping trailing punctuation while keeping intent, modality, polarity, entities, and numeric constraints intact. The model targets sub‑100 ms latency on modern GPUs and is intended to run as a lightweight preprocessing stage in front of higher-capacity LLMs.
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import re
+
+model_id = "gravitee-io/very-small-prompt-compression"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+
+def compress_prompt(text: str) -> str:
+    # Split longer prompts on sentence punctuation so each segment stays within the sweet spot.
+    parts = re.split(r"([.?!])", text)
+    segments = ["".join(parts[i:i+2]).strip() for i in range(0, len(parts), 2)]
+    outputs = []
+    for segment in segments:
+        if not segment:
+            continue
+        encoded = tokenizer(segment, return_tensors="pt", truncation=True)
+        generated = model.generate(**encoded, max_new_tokens=96, num_beams=4)
+        compressed = tokenizer.decode(generated[0], skip_special_tokens=True).rstrip("?.!,;:")
+        outputs.append(compressed)
+    return " ".join(outputs).strip()
+```
+
+## Intended uses & limitations
+### Intended uses
+- Prompt compression layer for human → LLM chat, API gateways, or routing pipelines where reducing prompt tokens improves throughput and latency.
+- Automatic rewrite step that preserves explicit instructions while trimming pleasantries and redundant phrasing on short English prompts.
+- Building blocks for prompt-budgeting systems; the model can be paired with token-price heuristics to decide whether to forward the compressed or original prompt.
+
+### Use with caution
+- For prompts longer than 64 tokens, split on punctuation (as in the example above), compress each clause independently, then restore punctuation in order.
+- Downstream systems should keep the original prompt available; if compression confidence falls below a threshold (e.g., cosine similarity or minimum token savings), fall back to the original.
+
+### Limitations
+- Training data is English-only and derived from Dolly-style instructions; performance on other languages or domain-specific jargon may degrade.
+- The model inherits stylistic biases from the `gpt-5-nano` generations used to bootstrap the dataset.
+- Average compression on the held-out set is ~26 % token savings (comp ratio 0.7395). Do not expect aggressive summarization or reasoning compression on much longer documents.
+
+## Training and evaluation data
+- **Source corpus:** [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) instructions served as the base prompts.
+- **Synthetic targets:** Each instruction was rewritten by `gpt-5-nano` under a policy that emphasized constraint preservation while removing optional words, helper verbs, and trailing punctuation. Responses that failed validation were regenerated.
+- **Normalization:** Compressed outputs were post-processed to drop trailing punctuation marks and optional English articles (`a`, `an`, `the`) when those edits did not change intent.
+- **Splits:** The resulting dataset ([gravitee-io/dolly-15k-prompt-compression](https://huggingface.co/datasets/gravitee-io/dolly-15k-prompt-compression)) was filtered to prompts ≤64 tokens, then split 90/10 into train and evaluation subsets. Metadata includes original and compressed token counts, ROUGE scores, compression ratios, and cosine similarity diagnostics.
+
+## Training procedure
+The model was fine-tuned for five epochs using Hugging Face `Seq2SeqTrainer`, starting from `Falconsai/text_summarization`. Length-aware generation settings (beam search with `num_beams=4` and mild length penalty) and a “no-worse-than-original” post-filter ensured outputs stayed shorter than their sources. Training monitored ROUGE-1/2/L, average compression ratio, and violation rate (percentage of generations longer than the input). The best checkpoint—selected by lowest validation loss—was pushed to the Hugging Face Hub as `gravitee-io/very-small-prompt-compression`.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step  | Comp Ratio Mean | Comp Ratio P90 | Validation Loss | Pct Violations | Rouge1 | Rouge2 | Rougel | Rougelsum |
+|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:---------------:|:--------------:|:------:|:------:|:------:|:---------:|
+| 2.8959        | 1.0   | 4044  | 0.7438          | 0.9167         | 2.3356          | 0.0002         | 0.7994 | 0.6082 | 0.7545 | 0.7543    |
+| 2.4157        | 2.0   | 8088  | 0.7393          | 0.9119         | 2.2255          | 0.0005         | 0.8121 | 0.6299 | 0.7696 | 0.7696    |
+| 2.3277        | 3.0   | 12132 | 0.7391          | 0.9091         | 2.1815          | 0.0004         | 0.8168 | 0.6386 | 0.7757 | 0.7756    |
+| 2.2933        | 4.0   | 16176 | 0.7395          | 0.9091         | 2.1669          | 0.0004         | 0.8182 | 0.6416 | 0.7774 | 0.7773    |
+| 2.2815        | 5.0   | 20220 | 0.7395          | 0.9091         | 2.1583          | 0.0004         | 0.8190 | 0.6452 | 0.7792 | 0.7792    |
+
+### Framework versions
+
+- Transformers 4.57.1
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.1
+
+## Evaluation
+- **Held-out compression:** On the ≤64 token evaluation split the model reaches a mean compression ratio of 0.7395 (≈26 % token reduction) with only 0.04 % of generations exceeding the original length.
+- **Semantic fidelity:** Cosine similarity between original and compressed embeddings (`text-embedding-3-small`) averages above 0.90, indicating that key semantics are preserved.
+- **Instruction alignment:** ROUGE-L of 0.7792 against synthetic targets shows the model closely matches the policy-compliant outputs produced during data generation.
+
+## License
+This model is released under the CC-BY-SA 3.0 License.
+
+## Acknowledgments
+- Training data sourced from [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the compressed derivative [gravitee-io/dolly-15k-prompt-compression](https://huggingface.co/datasets/gravitee-io/dolly-15k-prompt-compression)
+- Base model: [Falconsai/text_summarization](https://huggingface.co/Falconsai/text_summarization)
+
+## Citation
+If you use this model in your research, please cite:
+
+```
+@misc{very_small_prompt_compression_2025,
+  title={Very Small Prompt Compression Model},
+  author={Derek Thompson - Gravitee.io},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/gravitee-io/very-small-prompt-compression}}
+}
+```
+
+## Contact
+For questions, issues, or contributions, please open an issue on the model repository.
+
+---
+Generated by [dotslashderek](https://huggingface.co/dotslashderek) on 2025-10-31

config.json

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+    },
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+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+  "num_beams": 4,
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+  "transformers_version": "4.57.1"
+}

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samples_all.txt

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+
+
+📘 Epoch 1 — 2025-10-25T00:02:36
+----------------------------------------------------------------------------------------------------
+🟢 Sample 1
+Input(26 tok): Which of the following is a database or Streaming solution? Oracle, Kinesis, Event hub, SQL Server...
+Pred (21 tok): Which is database or Streaming solution? Oracle, Kinesis, Event hub, SQL Server...
+Ref  (23 tok): Which of following is database or Streaming solution? Oracle, Kinesis, Event hub, SQL Server...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 2
+Input(11 tok): What is the fastest time control chess?...
+Pred (8 tok): Fastest time control chess...
+Ref  (8 tok): Fastest chess time control...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 3
+Input(12 tok): What is the length of an Olympic size swimming pool?...
+Pred (6 tok): Olympic size swimming pool length...
+Ref  (4 tok): Olympic pool length...
+----------------------------------------------------------------------------------------------------
+
+
+📘 Epoch 2 — 2025-10-25T00:16:41
+----------------------------------------------------------------------------------------------------
+🟢 Sample 1
+Input(20 tok): Who were the commanders of the two armies involved in the Battle of Hastings?...
+Pred (16 tok): Who were commanders of two armies involved in Battle of Hastings...
+Ref  (13 tok): Battle of Hastings: commanders of both armies...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 2
+Input(16 tok): Which countries are part of the five eyes alliance and what is its purpose?...
+Pred (12 tok): Which countries are part of five eyes alliance and its purpose...
+Ref  (8 tok): Five Eyes members and alliance purpose...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 3
+Input(13 tok): What is the highest-grossing film worldwide?...
+Pred (9 tok): highest-grossing film worldwide...
+Ref  (9 tok): Worldwide highest-grossing film...
+----------------------------------------------------------------------------------------------------
+
+
+📘 Epoch 3 — 2025-10-25T00:30:47
+----------------------------------------------------------------------------------------------------
+🟢 Sample 1
+Input(23 tok): Write a friendly complaint to an airline service about a poor experience with multiple issues and ask for points....
+Pred (17 tok): Write friendly complaint to airline service about poor experience with multiple issues and ask for points...
+Ref  (17 tok): Write friendly complaint to airline service about poor experience with multiple issues and ask for points...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 2
+Input(17 tok): Write a paragraph on why visit and stay at Marysville in Victoria Australia....
+Pred (14 tok): Write paragraph on why visit and stay at Marysville in Victoria Australia...
+Ref  (14 tok): Write paragraph on why visit and stay at Marysville in Victoria Australia...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 3
+Input(19 tok): Find the bird in this list: Arctic skua, Monkey, Lily...
+Pred (18 tok): Find bird in this list: Arctic skua, Monkey, Lily...
+Ref  (17 tok): Find bird in list: Arctic skua, Monkey, Lily...
+----------------------------------------------------------------------------------------------------
+
+
+📘 Epoch 4 — 2025-10-25T00:45:25
+----------------------------------------------------------------------------------------------------
+🟢 Sample 1
+Input(31 tok): Given this paragraph about roasting peanuts to make peanut butter, what temperature are peanuts roasted using the batch method in celsius?...
+Pred (29 tok): From this paragraph about roasting peanuts to make peanut butter, what temperature are peanuts roasted using batch method in celsius...
+Ref  (27 tok): From this paragraph about roasting peanuts for peanut butter, what batch-roasting temperature (°C) is used...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 2
+Input(17 tok): Who were Jaehaerys I Targaryen's children?...
+Pred (16 tok): Who were Jaehaerys I Targaryen's children...
+Ref  (14 tok): Jaehaerys I Targaryen's children...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 3
+Input(8 tok): Which drum kit should I buy?...
+Pred (6 tok): Which drum kit to buy...
+Ref  (6 tok): Which drum kit to buy...
+----------------------------------------------------------------------------------------------------
+
+
+📘 Epoch 5 — 2025-10-25T00:59:55
+----------------------------------------------------------------------------------------------------
+🟢 Sample 1
+Input(8 tok): Who was Bosch's dad?...
+Pred (5 tok): Bosch's dad...
+Ref  (5 tok): Bosch's dad...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 2
+Input(24 tok): Make me a list of the oldest board games that I might not know about, and where they were invented....
+Pred (16 tok): List oldest board games I might not know about, and where they were invented...
+Ref  (11 tok): List obscure oldest board games and where they were invented...
+----------------------------------------------------------------------------------------------------
+🟢 Sample 3
+Input(6 tok): Can Penguins fly?...
+Pred (5 tok): Can Penguins fly...
+Ref  (5 tok): Can Penguins fly...
+----------------------------------------------------------------------------------------------------

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+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "extra_special_tokens": {},
+  "max_length": 256,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "stride": 0,
+  "tokenizer_class": "T5Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

training_args.bin

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