How to Install and Use h2oGPT OASST1-512 30B GPTQ Model

Introduction

In this tutorial, we will walk through the steps to install and use TheBloke’s h2oGPT OASST1-512 30B GPTQ model from Hugging Face. This powerful model is based on the GPTQ (quantized) version of h2oGPT and is highly efficient for generating text with minimal hardware requirements.

Prerequisites

• Python 3.8 or later
• Access to Hugging Face account
• A machine with at least 16GB of RAM and a compatible GPU (NVIDIA recommended).

Step 1: Installing Dependencies

Before starting, make sure you have Python installed. You will also need transformers, accelerate, and huggingface_hub to work with Hugging Face models. Install them by running the following command:

pip install transformers accelerate huggingface_hub

For GPU support, also install torch with CUDA:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Step 2: Downloading the Model

We’ll download the model from Hugging Face using the Hugging Face Hub API. Make sure you’re logged in to Hugging Face CLI:

huggingface-cli login

Then, use the from_pretrained function to download and load the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "TheBloke/h2ogpt-oasst1-512-30B-GPTQ"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, 
                                             device_map="auto", 
                                             load_in_8bit=True)  # Or load_in_4bit for lower resource use

Step 3: Running the Model

Once the model is loaded, you can start generating text. Here is a simple example where the model completes a sentence:

input_text = "What are the benefits of AI in healthcare?"

inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
output = model.generate(inputs["input_ids"], max_length=200)

print(tokenizer.decode(output[0], skip_special_tokens=True))

This will generate a response based on the input text you provide. The generated output will be a continuation of your prompt.

Step 4: Fine-Tuning the Model (Optional)

If you wish to fine-tune the model with your dataset, you’ll need to prepare a dataset and use the Hugging Face Trainer API. For example:

from transformers import Trainer, TrainingArguments

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    logging_dir="./logs",
)

# Initialize trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=your_dataset,
    eval_dataset=your_eval_dataset,
)

# Fine-tune the model
trainer.train()

Step 5: Saving the Model

After fine-tuning or using the model, save it locally:

model.save_pretrained("./my_trained_model")
tokenizer.save_pretrained("./my_trained_model")

You can now load the saved model for future use without needing to download it again.

Conclusion

The h2oGPT OASST1-512 30B GPTQ model is a versatile and efficient tool for a variety of text generation tasks. By following the steps in this guide, you can set up and use this model to handle diverse language generation needs. You can also fine-tune the model on your own dataset to customize its output.