A proof of concept generating captions using Google Gemma 3 on Google Colab Free Tier for captioning prompts akin to training data of FLUX Chroma: https://huggingface.co/lodestones/Chroma
Try the Chroma model at: https://tensor.art/models/891236315830428357
This dataset was built using 200 images from Redcaps : https://huggingface.co/datasets/lodestones/pixelprose
And 200 LLM captioned e621 images: https://huggingface.co/datasets/lodestones/e621-captions/tree/main
The total trained images are just 400 total , randomly selected , so this LoRa adaptation is very basic! You can likely train a better version yourself with listed tools on Google Colab Free Tier T4.
Want to train your own LoRa from a JSON or .parquet set if data? Use this notebook found in this repo: https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/train_on_parquet.ipynb
//----//
I made some .parquets of the captions here for easier browsing: https://huggingface.co/datasets/codeShare/chroma_prompts
To use this Gemma LoRa adaptation got to the Google Colab Jupyter notebook in this repo: https://huggingface.co/codeShare/flux_chroma_image_captioner/blob/main/gemma_image_captioner.ipynb
To train your own LoRa adaptation of the Gemma on Google Colab Free Tier T4 , visit : https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B)-Vision.ipynb
base_model: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth
Model Card for Model ID
Model Details
Model Description
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
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
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Framework versions
- PEFT 0.16.0