EpistemeAI/PD_gemma-3n-E2B

Mode card

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.* This Plant Disease Gemma model is fine tuned version of the Gemma 3n E2B, it is fine-tuned with the plant disease dataset. This model specializes in the scientific analysis of plant diseases in image of plants. Most models lack accurate information on plant diseases. The purpose is to fine-tune the Gemma 3n model to specialize in scientific plant disease.

Inputs and outputs*

• Input:
  • Text string, such as a question, a prompt, or a document to be summarized
  • Images, normalized to 256x256, 512x512, or 768x768 resolution and encoded to 256 tokens each
  • Audio data encoded to 6.25 tokens per second from a single channel
  • Total input context of 32K tokens
• 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 length up to 32K tokens, subtracting the request input tokens

Usage

Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3n is supported starting from transformers 4.53.0.

$ pip install -U transformers

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

Running with the pipeline API

You can initialize the model and processor for inference with pipeline as follows.

from transformers import pipeline
import torch
pipe = pipeline(
    "image-text-to-text",
    model="EpistemeAI/PD_gemma-3n-E2B",
    device="cuda",
    torch_dtype=torch.bfloat16,
)

With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a helpful assistant."}]
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    }
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])
# Okay, let's take a look!
# Based on the image, the animal on the candy is a **turtle**.
# You can see the shell shape and the head and legs.

this is the demo Demo

Model parameter:

Model size: 8.39B Tensor type: BF16

Training Dataset

• Dataset name: minhhungg/plant-disease-dataset
  • 70,295 rows
  • 70,295 24bit, 256x256 images of plant disease, questions and answers

LoRa and Training Parameters

• LoRA Adapter Parameters

  • r = 32, lora_alpha = 32, lora_dropout = 0, bias = "none", random_state = 3407

• Training Parameters

  • per_device_train_batch_size = 1, gradient_accumulation_steps = 4, gradient_checkpointing = True, gradient_checkpointing_kwargs = {"use_reentrant": False},
  • max_grad_norm = 0.3, warmup_ratio = 0.03, max_steps = 60, learning_rate = 2e-4, logging_steps = 1, save_strategy="steps", optim = "adamw_torch_fused", weight_decay = 0.01,
  • lr_scheduler_type = "cosine", seed = 3407,

Usage and Limitations*

These models have certain limitations that users should be aware of.

Intended Usage*

Open generative models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.

• Content Creation and Communication
  • Text Generation: Generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.
  • Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications.
  • Text Summarization: Generate concise summaries of a text corpus, research papers, or reports.
  • Image Data Extraction: Extract, interpret, and summarize visual data for text communications.
  • Audio Data Extraction: Transcribe spoken language, translate speech to text in other languages, and analyze sound-based data.
• Research and Education
  • Natural Language Processing (NLP) and generative model Research: These models can serve as a foundation for researchers to experiment with generative models and NLP techniques, develop algorithms, and contribute to the advancement of the field.
  • Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
  • Knowledge Exploration: Assist researchers in exploring large bodies of data by generating summaries or answering questions about specific topics.

Limitations*

• Training Data
  • The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses.
  • The scope of the training dataset determines the subject areas the model can handle effectively.
• Context and Task Complexity
  • Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging.
  • A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point).
• Language Ambiguity and Nuance
  • Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language.
• Factual Accuracy
  • Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements.
• Common Sense
  • Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations.

Ethical Considerations and Risks*

The development of generative models raises several ethical concerns. In creating an open model, we have carefully considered the following:

• Bias and Fairness
  • Generative models trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card.
• Misinformation and Misuse
  • Generative models can be misused to generate text that is false, misleading, or harmful.
  • Guidelines are provided for responsible use with the model, see the Responsible Generative AI Toolkit.
• Transparency and Accountability:
  • This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
  • A responsibly developed open model offers the opportunity to share innovation by making generative model technology accessible to developers and researchers across the AI ecosystem.

Risks identified and mitigations:

• Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases.
• Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases.
• Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of generative models. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the Gemma Prohibited Use Policy.
• Privacy violations: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques.

Reference:

• From Gemma-3n-E4B model card

special fine-tuned for vision plant disease detection and scientific solution.

Uploaded finetuned model

• Developed by: EpistemeAI
• License: apache-2.0
• Finetuned from model : unsloth/gemma-3n-e2b-unsloth-bnb-4bit

This gemma3n model was trained 2x faster with Unsloth and Huggingface's TRL library.