orca_mini_v9_6_3 B Instruct

pankajmathur

Introduction

Orca_Mini_v9_6_Llama-3.2-3B-Instruct is a text generation model built using the Llama-3.2-3B-Instruct framework. It is intended as a general-purpose conversational AI, allowing for further customization and enhancement through various tuning processes.

Architecture

The model is based on Meta's Llama-3.2-3B-Instruct architecture, with training conducted using datasets like pankajmathur/orca_mini_v1_dataset and pankajmathur/orca_mini_v8_sharegpt_format. It is implemented using the transformers library and supports PyTorch.

Training

The model is trained using a diverse set of datasets for fine-tuning, improving its conversational and inference capabilities. The training incorporates safety measures and dataset quality control to ensure robust and reliable responses.

Guide: Running Locally

To run the model locally:

  1. Environment Setup:

    • Ensure you have Python installed.
    • Install the transformers, torch, and bitsandbytes libraries.
  2. Model Loading:

    • Use the transformers pipeline to load the model with the following code snippet in different precision formats (bfloat16, 4-bit, or 8-bit) as per your requirement.
  3. Execution:

    • Define your input messages and execute the text generation pipeline.
  4. Cloud GPUs:

    • For enhanced performance, consider using cloud GPUs like Google Colab (with T4 GPU support).
import torch
from transformers import pipeline

model_slug = "pankajmathur/orca_mini_v9_6_3B-Instruct"
pipeline = pipeline(
    "text-generation",
    model=model_slug,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
]
outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.01, top_k=100, top_p=0.95)
print(outputs[0]["generated_text"][-1])

License

The model is provided under the llama3.2 license. Users are encouraged to credit the original authors when using the model for further development or research.

More Related APIs in Text Generation