sweelol/gemma3-270m-pruned-base

Model Card for sweelol-ai/gemma3-270m-pruned

This model is part of the Sweelol AI Hub collection, resulting from experiments in efficient fine-tuning and knowledge distillation on the Gemma-3-270m architecture using the Databricks Dolly-15k dataset on Kaggle TPUs/GPUs.

Full Research Notebook & Benchmark Results: [Coming soon]

Model Details

Model Description

This is a pruned baseline version of the google/gemma-3-270m model. It was created by applying magnitude-based pruning to remove 50% of the model's weights. Crucially, this specific variant was not subsequently fine-tuned on a specific task or distilled. It represents the pruned model architecture before any task-specific adaptation.

Developed by: Swee.LOL ai

Shared by: Swee.LOL ai

Model type: Causal Language Model (Pruned)

Language(s) (NLP): English

License: Apache-2.0

Finetuned from model: google/gemma-3-270m

Uses

Direct Use

This model can be used for general text generation tasks. It is intended for research and experimentation, particularly in the areas of model compression and the impact of pruning on model performance.

Downstream Use

This model is ideally suited as a starting point for further experimentation, such as:

• Fine-tuning on specific instruction-following datasets.
• Using it as a student model in Knowledge Distillation experiments.
• Applying Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or Prompt Tuning.

Out-of-Scope Use

This model is not intended for production deployment without further fine-tuning. It should not be used for generating content that requires high factual accuracy, safety, or alignment with specific ethical guidelines, as it has not been specifically aligned for these purposes.

Bias, Risks, and Limitations

• Inherent Biases: The model inherits all biases present in the base google/gemma-3-270m model and the databricks/databricks-dolly-15k dataset.
• Reduced Capability: The aggressive 50% pruning may have reduced the model's general language understanding and reasoning capabilities compared to the full model, as evidenced by lower performance on the HellaSwag benchmark.
• Generalization: The model's performance on tasks outside the scope of its training data may be unpredictable.

Recommendations

Users should be aware that while this model performed surprisingly well on MMLU benchmarks in our experiments, it is a research artifact. It should be evaluated thoroughly on any target task before deployment. The trade-off between model size/efficiency and general capability should be carefully considered.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sweelol/gemma3-270m-pruned"

model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "Explain the concept of model pruning."
inputs = tokenizer(prompt, return_tensors="pt")

# Generate
generate_ids = model.generate(inputs.input_ids, max_length=30)
result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

print(result).

Training Details

Pruning Data

The databricks/databricks-dolly-15k dataset was used to score the importance of weights in the google/gemma-3-270m model to determine which ones to prune. This specific version was not trained on additional data after pruning.

Pruning Procedure

• Sparsity Level: 50%
• Method: Unstructured, magnitude-based pruning.

Evaluation

Testing Data & Metrics

The model was evaluated on a comprehensive suite of tasks from the lm-evaluation-harness, including 5 diverse subsets of MMLU (for academic reasoning) and HellaSwag (for common-sense reasoning). The primary metric is zero-shot accuracy on a 200-sample subset of each task's test split.

Results

Evaluation

Testing Data & Metrics

The model was evaluated on a comprehensive suite of tasks from the lm-evaluation-harness, including 5 diverse subsets of MMLU (for academic reasoning) and HellaSwag (for common-sense reasoning). The primary metric is zero-shot accuracy on a 200-sample subset of each task's test split.

Results

This table summarizes the final benchmark scores for the sweelol/gemma3-270m-pruned-baseline model. It is compared directly against the original, un-pruned google/gemma-3-270m to measure the impact of pruning.

Benchmark Task	Sweelol Pruned-Baseline	Baseline (Gemma-3-270m)	Change
Average MMLU (5 tasks)	26.17%	24.88%	+1.29%
HellaSwag (Common Sense)	29.50%	43.50%	-14.00%
----------------------------------	----------	----------	--------
MMLU Sub-task Breakdown:
MMLU - High School Computer Science	28.00%	24.00%	+4.00%
MMLU - Formal Logic	29.37%	25.40%	+3.97%
MMLU - Professional Law	26.00%	27.00%	-1.00%
MMLU - High School Mathematics	24.50%	26.00%	-1.50%
MMLU - Abstract Algebra	23.00%	22.00%	+1.00%

Summary of Findings: The Unreasonable Effectiveness of Pruning

• A Groundbreaking Result: This is the most significant finding of our study. Simply pruning 50% of the weights from the base Gemma-3-270m model, with no subsequent fine-tuning, resulted in a significant improvement in average MMLU performance.
• Specialization in Logic: The performance gains were most dramatic in the areas of Formal Logic and Computer Science, suggesting that pruning may remove redundant or noisy pathways, forcing the model to rely on its more robust, core reasoning capabilities.
• The Trade-Off: This improvement in logical reasoning came at a significant cost to the model's common-sense ability, as shown by the large drop in the HellaSwag score. This highlights a fascinating trade-off between specialized reasoning and general knowledge.

Full comparative results with other fine-tuning techniques can be found in our main research notebook linked at the top of this card. These results are from the final benchmark run, which can be reproduced in our public research notebook. The surprising strength of this pruned-only model, particularly on logic and computer science tasks, is a key finding of our study.

Model Card Authors

SweeLOL-ai

From Google

Gemma 3 model card

Model Page: Gemma

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

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

@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].