my_gemma2_pt

Model Card for "my_gemma2_pt"

This is a fine-tuned model based on the Gemma language model, optimized for specific tasks to improve performance on datasets like IMDb.

Model Details

Model Description

The "my_gemma2_pt" model is a fine-tuned version of the Gemma language model, trained with a custom dataset to enhance its capability in generating human-like text and handling text generation tasks. This model has been pushed to the Hugging Face Hub for public use.

• Developed by: Madhan Kumar M
• Model type: Text Generation
• Language(s): English
• License: Open License
• Finetuned from model: Gemma

Uses

Direct Use

This model is designed to generate text in response to various prompts. It can be used for applications such as content creation, idea generation, and more.

Out-of-Scope Use

The model is not intended for use in applications that may lead to harmful outputs or misinformation. It should not be used in safety-critical applications or where high precision is required.

Bias, Risks, and Limitations

The model has been fine-tuned on specific datasets, and like any AI model, it may reflect biases present in the training data. Users should be cautious of potential limitations such as misinterpretation of text, failure to capture context, or unintentional generation of biased content.

Recommendations

Users should apply caution and continuously evaluate the model's performance for specific use cases.

How to Get Started with the Model

To start using this model, simply load it from the Hugging Face Hub using the following code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "themadankumar/my_gemma2_pt"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage:
inputs = tokenizer("Write a poem title", return_tensors="pt")
output = model.generate(inputs["input_ids"], max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training Details

Training Data

The model has been fine-tuned on a custom dataset specific to text generation tasks, including genres like movie titles, blog titles, and more.

Training Procedure

The training involved using supervised fine-tuning techniques with a focus on the specific dataset for text generation tasks. Preprocessing involved cleaning and tokenizing text data, followed by training on a cloud-based infrastructure.

Training Hyperparameters

• Training regime: fp16 mixed precision

Model Examination

Further examination of the model’s ability to generate relevant and creative outputs is recommended. This model was designed to excel in generating text prompts like titles and brief descriptions.