gemma-3-1b-it-ttt-longalpaca-full

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This Gemma3-1B-it model had its base model frozen and then had some of its MLP layers modified (global attention layers) to support In-Place Test Time Training. This was trained on 2 epochs of 12k samples of the LongAlpaca-12k dataset. The code can be found in this repo.

Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Chang Min Bark and Hung Ngo
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• Model type: Dense
• Language(s) (NLP): English
• License: Apache-2.0
• Finetuned from model [optional]: google/gemma-3-1b-it

Model Sources [optional]

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• Repository: https://github.com/changminbark/In-Place-Test-Time-Training
• Paper [optional]:
• Demo [optional]:

Uses

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Direct Use

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This was developed for the final project at AI with Neural Networks class at Bucknell University.

Downstream Use [optional]

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[More Information Needed]

Out-of-Scope Use

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This model cannot be used for any use cases outlined for google/gemma-3-1b-it.

Bias, Risks, and Limitations

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This model carries all of the same biases, risks, and limitations carried by google/gemma-3-1b-it.

Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Check the GitHub repo for more information.

Training Details

Training Data

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This was trained using LongAlpaca.

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: | Hyperparameter | TinyStories | LongAlpaca | Flag | | ---------------------- | ----------- | ---------- | ------------------- | | max_length | 1024 | 4096 | --max-length | | epochs | 1.0 | 2.0 | --epochs | | batch_size | 8 | 1 | --batch-size | | grad_accum | 8 | 16 | --grad-accum | | effective batch | 64 | 16 | — | | lr | 1e-4 | 5e-5 | --lr | | weight_decay | 0.1 | 0.1 | --weight-decay | | warmup_steps | 500 | 100 | --warmup-steps | | max_grad_norm | 1.0 | 1.0 | --max-grad-norm |

Shared optimizer / schedule (not per-dataset): AdamW with (β1, β2) = (0.9, 0.95), cosine LR decay, bf16 (fp16 fallback on non-bf16 GPUs), gradient checkpointing off.

TTT-module knobs (defaults match the In-Place TTT paper):

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Tested using synthetic NVIDIA RULER.

Factors

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• Context length

Metrics

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• Accuracy

Results

Summary

The vanilla base google/gemma-3-1b-it performed better than the In-Place TTT augmented model.

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

Based on google/gemma-3-1b-it.

Compute Infrastructure

Run using Google Colab.

Hardware

Run using NVIDIA A100 GPU in Google Colab.

Software

Run using Google Colab.

Citation [optional]

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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