Duc1212/DucGPT-v0.1

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

DucGPT v0.1 is an incomplete, continuously developing GPT-style language model created from curiosity and a desire to understand how modern language models work. It is an independent project developed by a 17-year-old student, covering the process from dataset preprocessing and tokenization to Transformer architecture, training, inference, and user accessibility. DucGPT is not intended to represent a finished or production-ready language model. Instead, it serves as an ongoing exploration of language-model engineering, with each version reflecting what I have learned and experimented with along the way.

• Developed by: Duc1212
• Funded by [optional]: Self-funded
• Shared by [optional]: Duc1212
• Model type: GPT-style decoder-only Transformer language model
• Language(s) (NLP): English
• License: MIT
• Finetuned from model [optional]: None

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

Install the required dependencies:

pip install torch transformers sentencepiece

Then load DucGPT using the Hugging Face Transformers interface:

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(
    "Duc1212/DucGPT-v0.1",
    trust_remote_code=True
)

model = AutoModelForCausalLM.from_pretrained(
    "Duc1212/DucGPT-v0.1",
    trust_remote_code=True
)

inputs = tokenizer(
    "The capital of France is",
    return_tensors="pt"
)

output = model.GENERATE(
    inputs["input_ids"],
    max_length=50
)
print(tokenizer.decode(output))

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]