Hugging Face's logo

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

Text Classification
Transformers
Safetensors
English
gemma3_text
text-generation
sweelol-ai
gemma
distillation
pruning
lora
prompt-tuning
text-generation-inference
Model card Files
xet
Community
finetuned-pruned-gemma3-270m-dolly / README.md
sweelol's picture
sweelol
Update README.md
a57b074 verified
preview code
|
raw
history blame contribute delete
19.5 kB
 ---
license: gemma
tags:
- sweelol-ai
- text-generation
- gemma
- distillation
- pruning
- lora
- prompt-tuning
datasets:
- databricks/databricks-dolly-15k
language:
- en
base_model:
- google/gemma-3-270m
pipeline_tag: text-classification
library_name: transformers
---
# 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
```sh
$ 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
```sh
$ 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
1. **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.
2. **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.
3. **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.
4. **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](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 |