Update README.md

README.md

@@ -1,532 +1,80 @@
 ---
-base_model: google/gemma-3-270m
-license: gemma
-tags:
-- gemma3
-- gemma
-- google
-pipeline_tag: text-generation
+license: mpl-2.0
 library_name: transformers
-extra_gated_heading: Access Gemma on Hugging Face
-extra_gated_prompt: >-
-  To access Gemma on Hugging Face, you’re required to review and agree to
-  Google’s usage license. To do this, please ensure you’re logged in to Hugging
-  Face and click below. Requests are processed immediately.
-extra_gated_button_content: Acknowledge license
+tags:
+- gemma-3
+- synthetic-data
+- textbooks
+- distillation
+- utility
+- summarization
+- lightning
+- conversational
+base_model: google/gemma-3-270m
 datasets:
 - TitleOS/Spark-Lightning-Synthetic-Textbooks
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-# Gemma 3 model card
-
-**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
-
-**Resources and Technical Documentation**:
-
-* [Gemma 3 Technical Report][g3-tech-report]
-* [Responsible Generative AI Toolkit][rai-toolkit]
-* [Gemma on Kaggle][kaggle-gemma]
-* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
-
-**Terms of Use**: [Terms][terms]
-
-**Authors**: Google DeepMind
-
-## Model Information
-
-Summary description and brief definition of inputs and outputs.
-
-### Description
-
-Gemma is a family of lightweight, state-of-the-art open models from Google,
-built from the same research and technology used to create the Gemini models.
-Gemma 3 models are multimodal, handling text and image input and generating text
-output, with open weights for both pre-trained variants and instruction-tuned
-variants. Gemma 3 has a large, 128K context window, multilingual support in over
-140 languages, and is available in more sizes than previous versions. Gemma 3
-models are well-suited for a variety of text generation and image understanding
-tasks, including question answering, summarization, and reasoning. Their
-relatively small size makes it possible to deploy them in environments with
-limited resources such as laptops, desktops or your own cloud infrastructure,
-democratizing access to state of the art AI models and helping foster innovation
-for everyone.
-
-### Inputs and outputs
-
--   **Input:**
-    -  Text string, such as a question, a prompt, or a document to be summarized
-    -  Images, normalized to 896 x 896 resolution and encoded to 256 tokens
-       each, for the 4B, 12B, and 27B sizes.
-    -  Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
-       32K tokens for the 1B and 270M sizes.
-
--   **Output:**
-    -   Generated text in response to the input, such as an answer to a
-        question, analysis of image content, or a summary of a document
-    -   Total output context up to 128K tokens for the 4B, 12B, and 27B sizes,
-        and 32K tokens for the 1B and 270M sizes per request, subtracting the
-        request input tokens
-
-### Citation
-
-```none
-@article{gemma_2025,
-    title={Gemma 3},
-    url={https://arxiv.org/abs/2503.19786},
-    publisher={Google DeepMind},
-    author={Gemma Team},
-    year={2025}
-}
-```
-
-## Model Data
-
-Data used for model training and how the data was processed.
-
-### Training Dataset
-
-These models were trained on a dataset of text data that includes a wide variety
-of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
-trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens,
-the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The 
-knowledge cutoff date for the training data was August 2024. Here are the key 
-components:
-
--   Web Documents: A diverse collection of web text ensures the model is
-    exposed to a broad range of linguistic styles, topics, and vocabulary. The
-    training dataset includes content in over 140 languages.
--   Code: Exposing the model to code helps it to learn the syntax and
-    patterns of programming languages, which improves its ability to generate
-    code and understand code-related questions.
--   Mathematics: Training on mathematical text helps the model learn logical
-    reasoning, symbolic representation, and to address mathematical queries.
--   Images: A wide range of images enables the model to perform image
-    analysis and visual data extraction tasks.
-
-The combination of these diverse data sources is crucial for training a powerful
-multimodal model that can handle a wide variety of different tasks and data
-formats.
-
-### Data Preprocessing
-
-Here are the key data cleaning and filtering methods applied to the training
-data:
-
--   CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
-    was applied at multiple stages in the data preparation process to ensure
-    the exclusion of harmful and illegal content.
--   Sensitive Data Filtering: As part of making Gemma pre-trained models
-    safe and reliable, automated techniques were used to filter out certain
-    personal information and other sensitive data from training sets.
--   Additional methods: Filtering based on content quality and safety in
-    line with [our policies][safety-policies].
-
-## Implementation Information
-
-Details about the model internals.
-
-### Hardware
-
-Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
-TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
-computational power. TPUs, designed specifically for matrix operations common in
-machine learning, offer several advantages in this domain:
-
--   Performance: TPUs are specifically designed to handle the massive
-    computations involved in training VLMs. They can speed up training
-    considerably compared to CPUs.
--   Memory: TPUs often come with large amounts of high-bandwidth memory,
-    allowing for the handling of large models and batch sizes during training.
-    This can lead to better model quality.
--   Scalability: TPU Pods (large clusters of TPUs) provide a scalable
-    solution for handling the growing complexity of large foundation models.
-    You can distribute training across multiple TPU devices for faster and more
-    efficient processing.
--   Cost-effectiveness: In many scenarios, TPUs can provide a more
-    cost-effective solution for training large models compared to CPU-based
-    infrastructure, especially when considering the time and resources saved
-    due to faster training.
--   These advantages are aligned with
-    [Google's commitments to operate sustainably][sustainability].
-
-### Software
-
-Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
-
-JAX allows researchers to take advantage of the latest generation of hardware,
-including TPUs, for faster and more efficient training of large models. ML
-Pathways is Google's latest effort to build artificially intelligent systems
-capable of generalizing across multiple tasks. This is specially suitable for
-foundation models, including large language models like these ones.
-
-Together, JAX and ML Pathways are used as described in the
-[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
-controller' programming model of Jax and Pathways allows a single Python
-process to orchestrate the entire training run, dramatically simplifying the
-development workflow."*
-
-## Evaluation
-
-Model evaluation metrics and results.
-
-### Benchmark Results
-
-These models were evaluated against a large collection of different datasets and
-metrics to cover different aspects of text generation. Evaluation results marked
-with **IT** are for instruction-tuned models. Evaluation results marked with
-**PT** are for pre-trained models.
-
-#### Gemma 3 270M
-
-| **Benchmark**             |  **n-shot**   | **Gemma 3 PT 270M** |
-| :------------------------ | :-----------: | ------------------: |
-| [HellaSwag][hellaswag]    |    10-shot    |                40.9 |
-| [BoolQ][boolq]            |    0-shot     |                61.4 |
-| [PIQA][piqa]              |    0-shot     |                67.7 |
-| [TriviaQA][triviaqa]      |    5-shot     |                15.4 |
-| [ARC-c][arc]              |    25-shot    |                29.0 |
-| [ARC-e][arc]              |    0-shot     |                57.7 |
-| [WinoGrande][winogrande]  |    5-shot     |                52.0 |
-
-[hellaswag]: https://arxiv.org/abs/1905.07830
-[boolq]: https://arxiv.org/abs/1905.10044
-[piqa]: https://arxiv.org/abs/1911.11641
-[triviaqa]: https://arxiv.org/abs/1705.03551
-[arc]: https://arxiv.org/abs/1911.01547
-[winogrande]: https://arxiv.org/abs/1907.10641
-
-| **Benchmark**             |  **n-shot**   | **Gemma 3 IT 270m** |
-| :------------------------ | :-----------: | ------------------: |
-| [HellaSwag][hellaswag]    |    0-shot     |                37.7 |
-| [PIQA][piqa]              |    0-shot     |                66.2 |
-| [ARC-c][arc]              |    0-shot     |                28.2 |
-| [WinoGrande][winogrande]  |    0-shot     |                52.3 |
-| [BIG-Bench Hard][bbh]     |   few-shot    |                26.7 |
-| [IF Eval][ifeval]         |    0-shot     |                51.2 |
-
-[hellaswag]: https://arxiv.org/abs/1905.07830
-[piqa]: https://arxiv.org/abs/1911.11641
-[arc]: https://arxiv.org/abs/1911.01547
-[winogrande]: https://arxiv.org/abs/1907.10641
-[bbh]: https://paperswithcode.com/dataset/bbh
-[bbh]: https://paperswithcode.com/dataset/bbh
-[ifeval]: https://arxiv.org/abs/2311.07911
-
-#### Gemma 3 1B, 4B, 12B & 27B
-
-##### Reasoning and factuality
-
-| Benchmark                      | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|--------------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
-| [GPQA][gpqa] Diamond           | 0-shot |      19.2     |      30.8     |      40.9      |      42.4      |
-| [SimpleQA][simpleqa]           | 0-shot |      2.2      |      4.0      |       6.3      |      10.0      |
-| [FACTS Grounding][facts-grdg]  |    -   |      36.4     |      70.1     |      75.8      |      74.9      |
-| [BIG-Bench Hard][bbh]          | 0-shot |      39.1     |      72.2     |      85.7      |      87.6      |
-| [BIG-Bench Extra Hard][bbeh]   | 0-shot |      7.2      |      11.0     |      16.3      |      19.3      |
-| [IFEval][ifeval]               | 0-shot |      80.2     |      90.2     |      88.9      |      90.4      |
-
-| Benchmark                      | n-shot   | Gemma 3 PT 1B  | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------ |----------|:--------------:|:-------------:|:--------------:|:--------------:|
-| [HellaSwag][hellaswag]         | 10-shot  |      62.3      |      77.2     |      84.2      |      85.6      |
-| [BoolQ][boolq]                 | 0-shot   |      63.2      |      72.3     |      78.8      |      82.4      |
-| [PIQA][piqa]                   | 0-shot   |      73.8      |      79.6     |      81.8      |      83.3      |
-| [SocialIQA][socialiqa]         | 0-shot   |      48.9      |      51.9     |      53.4      |      54.9      |
-| [TriviaQA][triviaqa]           | 5-shot   |      39.8      |      65.8     |      78.2      |      85.5      |
-| [Natural Questions][naturalq]  | 5-shot   |      9.48      |      20.0     |      31.4      |      36.1      |
-| [ARC-c][arc]                   | 25-shot  |      38.4      |      56.2     |      68.9      |      70.6      |
-| [ARC-e][arc]                   | 0-shot   |      73.0      |      82.4     |      88.3      |      89.0      |
-| [WinoGrande][winogrande]       | 5-shot   |      58.2      |      64.7     |      74.3      |      78.8      |
-| [BIG-Bench Hard][bbh]          | few-shot |      28.4      |      50.9     |      72.6      |      77.7      |
-| [DROP][drop]                   | 1-shot   |      42.4      |      60.1     |      72.2      |      77.2      |
-
-[gpqa]: https://arxiv.org/abs/2311.12022
-[simpleqa]: https://arxiv.org/abs/2411.04368
-[facts-grdg]: https://goo.gle/FACTS_paper
-[bbeh]: https://github.com/google-deepmind/bbeh
-[ifeval]: https://arxiv.org/abs/2311.07911
-[hellaswag]: https://arxiv.org/abs/1905.07830
-[boolq]: https://arxiv.org/abs/1905.10044
-[piqa]: https://arxiv.org/abs/1911.11641
-[socialiqa]: https://arxiv.org/abs/1904.09728
-[triviaqa]: https://arxiv.org/abs/1705.03551
-[naturalq]: https://github.com/google-research-datasets/natural-questions
-[arc]: https://arxiv.org/abs/1911.01547
-[winogrande]: https://arxiv.org/abs/1907.10641
-[bbh]: https://paperswithcode.com/dataset/bbh
-[drop]: https://arxiv.org/abs/1903.00161
-
-##### STEM and code
-
-| Benchmark                  | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|----------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
-| [MMLU][mmlu] (Pro)         | 0-shot |      14.7     |      43.6     |      60.6      |      67.5      |
-| [LiveCodeBench][lcb]       | 0-shot |      1.9      |      12.6     |      24.6      |      29.7      |
-| [Bird-SQL][bird-sql] (dev) |    -   |      6.4      |      36.3     |      47.9      |      54.4      |
-| [Math][math]               | 0-shot |      48.0     |      75.6     |      83.8      |      89.0      |
-| HiddenMath                 | 0-shot |      15.8     |      43.0     |      54.5      |      60.3      |
-| [MBPP][mbpp]               | 3-shot |      35.2     |      63.2     |      73.0      |      74.4      |
-| [HumanEval][humaneval]     | 0-shot |      41.5     |      71.3     |      85.4      |      87.8      |
-| [Natural2Code][nat2code]   | 0-shot |      56.0     |      70.3     |      80.7      |      84.5      |
-| [GSM8K][gsm8k]             | 0-shot |      62.8     |      89.2     |      94.4      |      95.9      |
-
-| Benchmark                      | n-shot         | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
-| [MMLU][mmlu]                   | 5-shot         |      59.6     |      74.5      |      78.6      |
-| [MMLU][mmlu] (Pro COT)         | 5-shot         |      29.2     |      45.3      |      52.2      |
-| [AGIEval][agieval]             | 3-5-shot       |      42.1     |      57.4      |      66.2      |
-| [MATH][math]                   | 4-shot         |      24.2     |      43.3      |      50.0      |
-| [GSM8K][gsm8k]                 | 8-shot         |      38.4     |      71.0      |      82.6      |
-| [GPQA][gpqa]                   | 5-shot         |      15.0     |      25.4      |      24.3      |
-| [MBPP][mbpp]                   | 3-shot         |      46.0     |      60.4      |      65.6      |
-| [HumanEval][humaneval]         | 0-shot         |      36.0     |      45.7      |      48.8      |
-
-[mmlu]: https://arxiv.org/abs/2009.03300
-[agieval]: https://arxiv.org/abs/2304.06364
-[math]: https://arxiv.org/abs/2103.03874
-[gsm8k]: https://arxiv.org/abs/2110.14168
-[gpqa]: https://arxiv.org/abs/2311.12022
-[mbpp]: https://arxiv.org/abs/2108.07732
-[humaneval]: https://arxiv.org/abs/2107.03374
-[lcb]: https://arxiv.org/abs/2403.07974
-[bird-sql]: https://arxiv.org/abs/2305.03111
-[nat2code]: https://arxiv.org/abs/2405.04520
-
-#### Multilingual
-
-| Benchmark                            | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|--------------------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
-| [Global-MMLU-Lite][global-mmlu-lite] | 0-shot |      34.2     |      54.5     |      69.5      |      75.1      |
-| [ECLeKTic][eclektic]                 | 0-shot |      1.4      |      4.6      |      10.3      |      16.7      |
-| [WMT24++][wmt24pp]                   | 0-shot |      35.9     |      46.8     |      51.6      |      53.4      |
-
-| Benchmark                            | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
-| [MGSM][mgsm]                         |      2.04     |      34.7     |      64.3     |      74.3     |
-| [Global-MMLU-Lite][global-mmlu-lite] |      24.9     |      57.0     |      69.4     |      75.7     |
-| [WMT24++][wmt24pp] (ChrF)            |      36.7     |      48.4     |      53.9     |      55.7     |
-| [FloRes][flores]                     |      29.5     |      39.2     |      46.0     |      48.8     |
-| [XQuAD][xquad] (all)                 |      43.9     |      68.0     |      74.5     |      76.8     |
-| [ECLeKTic][eclektic]                 |      4.69     |      11.0     |      17.2     |      24.4     |
-| [IndicGenBench][indicgenbench]       |      41.4     |      57.2     |      61.7     |      63.4     |
-
-[mgsm]: https://arxiv.org/abs/2210.03057
-[flores]: https://arxiv.org/abs/2106.03193
-[xquad]: https://arxiv.org/abs/1910.11856v3
-[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
-[wmt24pp]: https://arxiv.org/abs/2502.12404v1
-[eclektic]: https://arxiv.org/abs/2502.21228
-[indicgenbench]: https://arxiv.org/abs/2404.16816
-
-##### Multimodal
-
-| Benchmark                         | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|-----------------------------------|:-------------:|:--------------:|:--------------:|
-| [MMMU][mmmu] (val)                |      48.8     |      59.6      |      64.9      |
-| [DocVQA][docvqa]                  |      75.8     |      87.1      |      86.6      |
-| [InfoVQA][info-vqa]               |      50.0     |      64.9      |      70.6      |
-| [TextVQA][textvqa]                |      57.8     |      67.7      |      65.1      |
-| [AI2D][ai2d]                      |      74.8     |      84.2      |      84.5      |
-| [ChartQA][chartqa]                |      68.8     |      75.7      |      78.0      |
-| [VQAv2][vqav2] (val)              |      62.4     |      71.6      |      71.0      |
-| [MathVista][mathvista] (testmini) |      50.0     |      62.9      |      67.6      |
-
-| Benchmark                      | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------ |:-------------:|:--------------:|:--------------:|
-| [COCOcap][coco-cap]            |      102      |      111       |      116       |
-| [DocVQA][docvqa] (val)         |      72.8     |      82.3      |      85.6      |
-| [InfoVQA][info-vqa] (val)      |      44.1     |      54.8      |      59.4      |
-| [MMMU][mmmu] (pt)              |      39.2     |      50.3      |      56.1      |
-| [TextVQA][textvqa] (val)       |      58.9     |      66.5      |      68.6      |
-| [RealWorldQA][realworldqa]     |      45.5     |      52.2      |      53.9      |
-| [ReMI][remi]                   |      27.3     |      38.5      |      44.8      |
-| [AI2D][ai2d]                   |      63.2     |      75.2      |      79.0      |
-| [ChartQA][chartqa]             |      63.6     |      74.7      |      76.3      |
-| [VQAv2][vqav2]                 |      63.9     |      71.2      |      72.9      |
-| [BLINK][blinkvqa]              |      38.0     |      35.9      |      39.6      |
-| [OKVQA][okvqa]                 |      51.0     |      58.7      |      60.2      |
-| [TallyQA][tallyqa]             |      42.5     |      51.8      |      54.3      |
-| [SpatialSense VQA][ss-vqa]     |      50.9     |      60.0      |      59.4      |
-| [CountBenchQA][countbenchqa]   |      26.1     |      17.8      |      68.0      |
-
-[coco-cap]: https://cocodataset.org/#home
-[docvqa]: https://www.docvqa.org/
-[info-vqa]: https://arxiv.org/abs/2104.12756
-[mmmu]: https://arxiv.org/abs/2311.16502
-[textvqa]: https://textvqa.org/
-[realworldqa]: https://paperswithcode.com/dataset/realworldqa
-[remi]: https://arxiv.org/html/2406.09175v1
-[ai2d]: https://allenai.org/data/diagrams
-[chartqa]: https://arxiv.org/abs/2203.10244
-[vqav2]: https://visualqa.org/index.html
-[blinkvqa]: https://arxiv.org/abs/2404.12390
-[okvqa]: https://okvqa.allenai.org/
-[tallyqa]: https://arxiv.org/abs/1810.12440
-[ss-vqa]: https://arxiv.org/abs/1908.02660
-[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
-[mathvista]: https://arxiv.org/abs/2310.02255
-
-## Ethics and Safety
-
-Ethics and safety evaluation approach and results.
-
-### Evaluation Approach
-
-Our evaluation methods include structured evaluations and internal red-teaming
-testing of relevant content policies. Red-teaming was conducted by a number of
-different teams, each with different goals and human evaluation metrics. These
-models were evaluated against a number of different categories relevant to
-ethics and safety, including:
-
--   **Child Safety**: Evaluation of text-to-text and image to text prompts
-    covering child safety policies, including child sexual abuse and
-    exploitation.
--   **Content Safety:** Evaluation of text-to-text and image to text prompts
-    covering safety policies including, harassment, violence and gore, and hate
-    speech.
--   **Representational Harms**: Evaluation of text-to-text and image to text
-    prompts covering safety policies including bias, stereotyping, and harmful
-    associations or inaccuracies.
+# Spark-270M
 
-In addition to development level evaluations, we conduct "assurance
-evaluations" which are our 'arms-length' internal evaluations for responsibility
-governance decision making. They are conducted separately from the model
-development team, to inform decision making about release. High level findings
-are fed back to the model team, but prompt sets are held-out to prevent
-overfitting and preserve the results' ability to inform decision making.
-Assurance evaluation results are reported to our Responsibility & Safety Council
-as part of release review.
+**Spark-270M** is a highly compact, utility-focused language model with **270 million parameters**. It is a fine-tune of Google's [Gemma 3 270M](https://huggingface.co/google/gemma-3-270m), designed to punch significantly above its weight class by leveraging high-quality synthetic data distillation.
 
-### Evaluation Results
+The model functions as a "dense information engine"—specializing in generating concise title summaries, search engine queries, and logical follow-up questioning—while retaining the creative conversational flair inherited from its teacher model's lineage.
 
-For all areas of safety testing, we saw major improvements in the categories of
-child safety, content safety, and representational harms relative to previous
-Gemma models. All testing was conducted without safety filters to evaluate the
-model capabilities and behaviors. For both text-to-text and image-to-text, and
-across all model sizes, the model produced minimal policy violations, and showed
-significant improvements over previous Gemma models' performance with respect
-to ungrounded inferences. A limitation of our evaluations was they included only
-English language prompts.
+## ⚡ Model Details
 
-## Usage and Limitations
+- **Model Name:** Spark-270M
+- **Base Architecture:** [Google Gemma 3 270M](https://huggingface.co/google/gemma-3-270m)
+- **Parameters:** 270M active parameters
+- **Context Window:** 32k tokens
+- **Teacher Model:** Lightning-1.7B (Custom model fine-tuned on Hermes 3)
+- **Training Type:** Synthetic "Textbook" Distillation (SFT)
 
-These models have certain limitations that users should be aware of.
+## 📚 Training Methodology: "Textbooks Are All You Need"
 
-### Intended Usage
+Spark-270M was trained using a distinct data pipeline inspired by the *Textbooks Are All You Need* (Microsoft Phi) research paper. 
 
-Open vision-language models (VLMs) models have a wide range of applications
-across various industries and domains. The following list of potential uses is
-not comprehensive. The purpose of this list is to provide contextual information
-about the possible use-cases that the model creators considered as part of model
-training and development.
+Instead of training on raw web scrapes, Spark-270M was fine-tuned exclusively on a series of **synthetic textbooks** generated by a larger parent model, **Lightning-1.7B**. 
 
--   Content Creation and Communication
-    -   Text Generation: These models can be used to generate creative text
-        formats such as poems, scripts, code, marketing copy, and email drafts.
-    -   Chatbots and Conversational AI: Power conversational interfaces
-        for customer service, virtual assistants, or interactive applications.
-    -   Text Summarization: Generate concise summaries of a text corpus,
-        research papers, or reports.
-    -   Image Data Extraction: These models can be used to extract,
-        interpret, and summarize visual data for text communications.
--   Research and Education
-    -   Natural Language Processing (NLP) and VLM Research: These
-        models can serve as a foundation for researchers to experiment with VLM
-        and NLP techniques, develop algorithms, and contribute to the
-        advancement of the field.
-    -   Language Learning Tools: Support interactive language learning
-        experiences, aiding in grammar correction or providing writing practice.
-    -   Knowledge Exploration: Assist researchers in exploring large
-        bodies of text by generating summaries or answering questions about
-        specific topics.
+### The Teacher: Lightning-1.7B
+The data generator, Lightning-1.7B, was itself fine-tuned on the [Hermes 3 dataset](https://huggingface.co/nousresearch/hermes-3-llama-3.1-8b). This lineage allows Spark-270M to inherit specific behavioral traits from Hermes 3—namely creativity, steerability, and a refusal to be "boring"—despite being distilled into a rigid textbook format.
 
-### Limitations
+The synthetic data focused on:
+1.  **High-density reasoning chains:** Explaining complex topics in compressed formats.
+2.  **Utility Tasks:** Converting conversational fluff into actionable queries.
+3.  **Socratic Dialogue:** Modeling inquisitive follow-up questioning.
 
--   Training Data
-    -   The quality and diversity of the training data significantly
-        influence the model's capabilities. Biases or gaps in the training data
-        can lead to limitations in the model's responses.
-    -   The scope of the training dataset determines the subject areas
-        the model can handle effectively.
--   Context and Task Complexity
-    -   Models are better at tasks that can be framed with clear
-        prompts and instructions. Open-ended or highly complex tasks might be
-        challenging.
-    -   A model's performance can be influenced by the amount of context
-        provided (longer context generally leads to better outputs, up to a
-        certain point).
--   Language Ambiguity and Nuance
-    -   Natural language is inherently complex. Models might struggle
-        to grasp subtle nuances, sarcasm, or figurative language.
--   Factual Accuracy
-    -   Models generate responses based on information they learned
-        from their training datasets, but they are not knowledge bases. They
-        may generate incorrect or outdated factual statements.
--   Common Sense
-    -   Models rely on statistical patterns in language. They might
-        lack the ability to apply common sense reasoning in certain situations.
+## 🛠️ Intended Use & Capabilities
 
-### Ethical Considerations and Risks
+Spark-270M is designed to be a lightweight **Utility Model**. It is ideal for edge devices, rapid prototyping, or functioning as a specific "node" in a larger agentic system (e.g., the summarizer node or the query-generator node).
 
-The development of vision-language models (VLMs) raises several ethical
-concerns. In creating an open model, we have carefully considered the following:
+### Primary Capabilities
+* **Dense Title Summarization:** Converting long conversation threads into information-dense, short titles or abstracts.
+* **Search Query Generation:** Formulating precise, keyword-rich search queries based on vague user input.
+* **Proactive Questioning:** Generating relevant follow-up questions to clarify user intent or deepen a topic.
 
--   Bias and Fairness
-    -   VLMs trained on large-scale, real-world text and image data can
-        reflect socio-cultural biases embedded in the training material. These
-        models underwent careful scrutiny, input data pre-processing described
-        and posterior evaluations reported in this card.
--   Misinformation and Misuse
-    -   VLMs can be misused to generate text that is false, misleading,
-        or harmful.
-    -   Guidelines are provided for responsible use with the model, see the
-        [Responsible Generative AI Toolkit][rai-toolkit].
--   Transparency and Accountability:
-    -   This model card summarizes details on the models' architecture,
-        capabilities, limitations, and evaluation processes.
-    -   A responsibly developed open model offers the opportunity to
-        share innovation by making VLM technology accessible to developers and
-        researchers across the AI ecosystem.
+## 💻 Example Usage
 
-Risks identified and mitigations:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
--   **Perpetuation of biases**: It's encouraged to perform continuous
-    monitoring (using evaluation metrics, human review) and the exploration of
-    de-biasing techniques during model training, fine-tuning, and other use
-    cases.
--   **Generation of harmful content**: Mechanisms and guidelines for content
-    safety are essential. Developers are encouraged to exercise caution and
-    implement appropriate content safety safeguards based on their specific
-    product policies and application use cases.
--   **Misuse for malicious purposes**: Technical limitations and developer
-    and end-user education can help mitigate against malicious applications of
-    VLMs. Educational resources and reporting mechanisms for users to flag
-    misuse are provided. Prohibited uses of Gemma models are outlined in the
-    [Gemma Prohibited Use Policy][prohibited-use].
--   **Privacy violations**: Models were trained on data filtered for removal
-    of certain personal information and other sensitive data. Developers are
-    encouraged to adhere to privacy regulations with privacy-preserving
-    techniques.
+model_id = "TitleOS/Spark-270M"
 
-### Benefits
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
 
-At the time of release, this family of models provides high-performance open
-vision-language model implementations designed from the ground up for
-responsible AI development compared to similarly sized models.
+# Example: Generating a search query from a user problem
+input_text = """
+User: I need to fix my sink, it's leaking from the bottom pipe where the U-shape thing is.
+Task: Generate 3 search engine queries for this problem.
+Response:
+"""
 
-Using the benchmark evaluation metrics described in this document, these models
-have shown to provide superior performance to other, comparably-sized open model
-alternatives.
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-[g3-tech-report]: https://arxiv.org/abs/2503.19786
-[rai-toolkit]: https://ai.google.dev/responsible
-[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
-[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
-[terms]: https://ai.google.dev/gemma/terms
-[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
-[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
-[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
-[sustainability]: https://sustainability.google/operating-sustainably/
-[jax]: https://github.com/jax-ml/jax
-[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
-[sustainability]: https://sustainability.google/operating-sustainably/
-[gemini-2-paper]: https://arxiv.org/abs/2312.11805
+outputs = model.generate(**input_ids, max_new_tokens=128)
+print(tokenizer.d ecode(outputs[0]))
+