Minueza 2 96M

🚀 Minueza-2-96M

Minueza-2-96M is a compact language model based on the Llama architecture, trained on English and Portuguese datasets. It offers a lightweight foundation for specific applications, despite its limitations compared to larger models.

🚀 Quick Start

Installation

pip install transformers==4.50.0 torch==2.6.0

Usage

from transformers import pipeline, TextStreamer
import torch

prompt = "This book tells the story"

generate_text = pipeline(
    "text-generation",
    model="Felladrin/Minueza-2-96M",
    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)

generate_text(
    prompt,
    streamer=TextStreamer(generate_text.tokenizer, skip_special_tokens=True),
    do_sample=True,
    max_new_tokens=512,
    temperature=0.8,
    top_p=0.95,
    top_k=0,
    min_p=0.05,
    repetition_penalty=1.1,
)

✨ Features

• Compact Size: With only 96 million parameters, it can be run on mobile web browsers via Wllama and Transformers.js, and run fast on machines without GPU.
• Multilingual Training: Trained from scratch on English and Portuguese datasets.
• Fine - Tunable: Can serve as a base for fine - tunes using ChatML format.

📦 Installation

pip install transformers==4.50.0 torch==2.6.0

💻 Usage Examples

Basic Usage

from transformers import pipeline, TextStreamer
import torch

prompt = "This book tells the story"

generate_text = pipeline(
    "text-generation",
    model="Felladrin/Minueza-2-96M",
    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)

generate_text(
    prompt,
    streamer=TextStreamer(generate_text.tokenizer, skip_special_tokens=True),
    do_sample=True,
    max_new_tokens=512,
    temperature=0.8,
    top_p=0.95,
    top_k=0,
    min_p=0.05,
    repetition_penalty=1.1,
)

📚 Documentation

Summary

Minueza-2-96M is a compact language model based on the Llama architecture. It was trained from scratch on English and Portuguese datasets, utilising a context length of 4096 tokens and processing 185 billion tokens during the training process. With a parameter count of only 96 million, this model serves as a lightweight foundation that can be subsequently fine-tuned for specific applications.

Due to its compact size, the model has significant limitations in reasoning, factual knowledge, and general capabilities compared to larger models. It may generate incorrect, irrelevant, or nonsensical outputs. Furthermore, as it was trained on internet text data, it may harbour biases and potentially produce inappropriate content.

Intended Uses

This model was created with the following objectives in mind:

• Run on mobile web browsers via Wllama and Transformers.js.
• Run fast on machines without GPU.
• Serve as a base for fine - tunes using ChatML format.

🔧 Technical Details

Model Architecture

This is a transformer model with the Llama architecture, trained on a context window of 4096 tokens.

The pretraining was made with these hyperparameters:

📄 License

This model is licensed under the Apache License 2.0.