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Sweelol-ai/
finetuned-pruned-gemma3-270m-dolly

Name: finetuned-pruned-gemma3-270m-dolly
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Sweelol-ai/finetuned-pruned-gemma3-270m-dolly

Model Description

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]

Key Details:

Base Model: google/gemma-3-270m
Training Data: Databricks Dolly-15k (subset)

Use a pipeline as a high-level helper

$ pip install -U transformers

python

from transformers import pipeline

pipe = pipeline("text-generation", model="Sweelol-ai/finetuned-pruned-gemma3-270m-dolly")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

Load model directly

$ pip install -U transformers

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sweelol-ai/finetuned-pruned-gemma3-270m-dolly")
model = AutoModelForCausalLM.from_pretrained("Sweelol-ai/finetuned-pruned-gemma3-270m-dolly")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

This is a placeholder README. A detailed model card with full results and usage instructions will be added shortly.

Evaluation Results

This table compares the performance of this Finetuned-Pruned model against the original, un-tuned google/gemma-3-270m base model.

Benchmark Task	Sweelol Finetuned-Pruned	Baseline (Gemma-3-270m)	Change
Average MMLU (5 tasks)	25.18%	24.88%	+0.30%
HellaSwag (Common Sense)	29.50%	43.50%	-14.00%
----------------------------------	----------	----------	--------
MMLU Sub-task Breakdown:
MMLU - Formal Logic	28.57%	25.40%	+3.17%
MMLU - High School Computer Science	25.00%	24.00%	+1.00%
MMLU - Professional Law	25.00%	27.00%	-2.00%
MMLU - Abstract Algebra	22.00%	22.00%	0.00%
MMLU - High School Mathematics	21.00%	26.00%	-5.00%

Summary of Findings

Fine-tuning the pruned model resulted in a solid overall improvement on MMLU, particularly in formal logic. However, like the pruned-only baseline, it suffered a significant drop in common-sense reasoning (HellaSwag).

Evaluation

Testing Data & Metrics

All models were 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 all models created in the Sweelol AI Comparative Study. All fine-tuned models were trained on a subset of the databricks/databricks-dolly-15k dataset.

Model	Technique	Average MMLU	HellaSwag	MMLU CompSci	MMLU Logic	MMLU Law	MMLU Math	MMLU Algebra
Baseline	(Pre-trained)	24.88%	43.50%	24.00%	25.40%	27.00%	26.00%	22.00%
Pruned-Baseline	Pruning	26.17%	29.50%	28.00%	29.37%	26.00%	24.50%	23.00%
Prompt-Tune	PEFT	25.77%	39.00%	27.00%	29.37%	27.50%	22.00%	23.00%
Finetuned-Pruned	Pruning + FT	25.18%	29.50%	25.00%	28.57%	25.00%	21.00%	22.00%
LoRA	PEFT	24.60%	26.00%	25.00%	28.57%	25.00%	21.00%	22.00%
KD-Pruned	Distillation	23.98%	33.00%	26.00%	25.40%	25.00%	21.50%	22.00%
Full-Finetune	Full FT	22.60%	39.00%	26.00%	23.02%	23.50%	21.50%	19.00%

Summary of Key Findings

Pruning is a Superpower for Logic: The Pruned-Baseline model, with no fine-tuning, was the undisputed champion on average MMLU performance. It achieved the highest scores in Formal Logic and Computer Science, suggesting that pruning enhances the model's core, pre-trained reasoning abilities.
Prompt Tuning is the Efficiency King: The Prompt-Tune model was the second-best performer on MMLU and retained strong common-sense performance (HellaSwag). This makes it the most efficient and effective overall technique, delivering top-tier results with minimal training.
The "Alignment Tax" is Real: Both Full-Finetune and KD-Pruned models, while trained on instruction data, showed a significant drop in performance on the MMLU reasoning tasks compared to the baseline. This is a classic example of the "alignment tax," where teaching a model to be a helpful assistant can sometimes dilute its raw, academic reasoning capabilities.
Common Sense is Fragile: Techniques that heavily modified the model's structure or weights (Pruning, LoRA) resulted in a significant drop in performance on the HellaSwag common-sense benchmark. The Baseline model remains the champion of common sense.

This comprehensive benchmark provides a clear, data-driven guide for selecting the right optimization technique for a given task.

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

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	10-shot	40.9
BoolQ	0-shot	61.4
PIQA	0-shot	67.7
TriviaQA	5-shot	15.4
ARC-c	25-shot	29.0
ARC-e	0-shot	57.7
WinoGrande	5-shot	52.0

Benchmark	n-shot	Gemma 3 IT 270m
HellaSwag	0-shot	37.7
PIQA	0-shot	66.2
ARC-c	0-shot	28.2
WinoGrande	0-shot	52.3
BIG-Bench Hard	few-shot	26.7
IF Eval	0-shot	51.2

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 Diamond	0-shot	19.2	30.8	40.9	42.4
SimpleQA	0-shot	2.2	4.0	6.3	10.0
FACTS Grounding	-	36.4	70.1	75.8	74.9
BIG-Bench Hard	0-shot	39.1	72.2	85.7	87.6
BIG-Bench Extra Hard	0-shot	7.2	11.0	16.3	19.3
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	10-shot	62.3	77.2	84.2	85.6
BoolQ	0-shot	63.2	72.3	78.8	82.4
PIQA	0-shot	73.8	79.6	81.8	83.3
SocialIQA	0-shot	48.9	51.9	53.4	54.9
TriviaQA	5-shot	39.8	65.8	78.2	85.5
Natural Questions	5-shot	9.48	20.0	31.4	36.1
ARC-c	25-shot	38.4	56.2	68.9	70.6
ARC-e	0-shot	73.0	82.4	88.3	89.0
WinoGrande	5-shot	58.2	64.7	74.3	78.8
BIG-Bench Hard	few-shot	28.4	50.9	72.6	77.7
DROP	1-shot	42.4	60.1	72.2	77.2

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	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