You can deploy this model and start earning money today!

Sweelol-ai/
pt-gemma3-270m-dolly

Name: pt-gemma3-270m-dolly
Availability: InStock

Views

No views yet

sweelol/pt-gemma3-270m-dolly

This model is part of the Sweelol AI Hub, a research project focused on efficient fine-tuning of modern language models on Kaggle accelerators.

Full Research Notebook & Benchmark Results: [Coming soon]

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

This is a LoRA-adapted version of the google/gemma-3-270m model. It was fine-tuned on the Databricks Dolly-15k dataset using the Low-Rank Adaptation (LoRA) technique. LoRA is a parameter-efficient fine-tuning method that freezes the original model weights and injects trainable low-rank matrices into the attention layers. This allows the model to learn task-specific knowledge (instruction following) while keeping the overall number of trainable parameters low. Only the LoRA adapter weights need to be stored, making this model highly efficient to deploy.

Developed by: SweeLOL-ai
Shared by: SweeLOL ai
Model type: Causal Language Model
Language(s) (NLP): English
License: Apache 2.0
Base Model: google/gemma-3-270m

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.

Usage

Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0.

$ pip install -U transformers

Then, copy the snippet from the section that is relevant for your use case.

Running with the `pipeline` API

python

from transformers import pipeline
import torch

pipe = pipeline("text-generation", model="sweelol-ai/pt-gemma3-270m-dolly", device="cuda", torch_dtype=torch.bfloat16)
output = pipe("Eiffel tower is located in", max_new_tokens=50)

Running the model on a single / multi GPU

python

import torch
from transformers import AutoTokenizer,

tokenizer = AutoTokenizer.from_pretrained("Sweelol-ai/pt-gemma3-270m-dolly")
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
        print("✅ Set tokenizer pad_token to eos_token")

    model = AutoModelForCausalLM.from_pretrained(
        "Sweelol-ai/pt-gemma3-270m-dolly",
        torch_dtype=torch.bfloat16 if not USE_AMP else torch.float32,
        attn_implementation='eager'
    )
    print(f"✅ Base model loaded (dtype: {model.dtype}).")


prompt = "Eiffel tower is located in"
model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

input_len = model_inputs["input_ids"].shape[-1]

with torch.inference_mode():
    generation = model.generate(**model_inputs, max_new_tokens=50, do_sample=False)
    generation = generation[0][input_len:]

decoded = tokenizer.decode(generation, skip_special_tokens=True)
print(decoded)

Evaluation Results

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

Benchmark Task	Sweelol Prompt-Tune	Baseline (Gemma-3-270m)	Change
Average MMLU (5 tasks)	25.77%	24.88%	+0.89%
HellaSwag (Common Sense)	39.00%	43.50%	-4.50%
----------------------------------	----------	----------	--------
MMLU Sub-task Breakdown:
MMLU - Formal Logic	29.37%	25.40%	+3.97%
MMLU - Professional Law	27.50%	27.00%	+0.50%
MMLU - High School Computer Science	27.00%	24.00%	+3.00%
MMLU - Abstract Algebra	23.00%	22.00%	+1.00%
MMLU - High School Mathematics	22.00%	26.00%	-4.00%

Summary of Findings

Prompt Tuning proved to be a highly effective and efficient technique. It was the second-best performer on MMLU, significantly boosting logical reasoning while largely preserving the model's strong common-sense abilities.

Citation [optional]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.17.1

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

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

Sweelol-ai/pt-gemma3-270m-dolly

sweelol/pt-gemma3-270m-dolly

Description

Usage

Running with the pipeline API

Running the model on a single / multi GPU

Evaluation Results

Summary of Findings

Citation [optional]

Model Card Contact

Framework versions

Gemma 3 model card

Model Information

Description

Inputs and outputs

Citation

Model Data

Training Dataset

Data Preprocessing

Implementation Information

Hardware

Software

Evaluation

Benchmark Results

Gemma 3 270M

Gemma 3 1B, 4B, 12B & 27B

Reasoning and factuality

STEM and code

Sweelol-ai/
pt-gemma3-270m-dolly

Running with the `pipeline` API