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---
license: mit
language:
- en
base_model:
- lkw99/unsloth-llama-3.1-8b-bnb-4bit
tags:
- medical
- biology
- chemistry
- text-generation-inference
---
# Vitals GGUF - 16-bit Model

This is the **Vitals GGUF - 16-bit** model, a finely tuned LLaMA model that has been optimized for efficient medical text generation and healthcare-related applications. The model was trained using **Unsloath** and Hugging Face's **TRL (Transformers Reinforcement Learning)** library, resulting in a training process that’s twice as fast as traditional methods.

## Model Overview

- **Developed by**: [Aviralansh](https://huggingface.co/Aviralansh)
- **License**: Apache-2.0
- **Base Model**: `unsloth/meta-llama-3.1-8b-bnb-4bit`
- **Purpose**: This model is designed for AI-driven healthcare solutions, with capabilities in generating accurate medical information, answering healthcare queries, and providing insights related to health and wellness.

This model is hosted on Hugging Face and can be downloaded for use in healthcare applications. It is available in a 16-bit GGUF format, optimized for faster inference without compromising accuracy.

## Features

- **Fast Training**: Powered by Unsloath, the model is trained at double the speed compared to standard models.
- **High-Quality Medical Language Understanding**: Ideal for applications requiring high accuracy and consistency in medical responses.
- **16-Bit Precision**: Uses GGUF format to achieve high efficiency in memory usage and faster performance.
- **Runs on Ollama**: Integrates smoothly with the Ollama service, enabling easy deployment and interaction.

## How It Works

The **Vitals GGUF - 16-bit** model is based on the **LLaMA** architecture, finetuned specifically for the medical domain. The training process incorporated datasets and optimization techniques that make it suitable for healthcare applications, especially where quick response times and detailed medical understanding are essential. This model can be leveraged for applications such as:

- **Medical Question-Answering**: Provides answers to general healthcare and wellness questions.
- **Symptom Analysis**: Helps users interpret symptoms and offers preliminary advice.
- **Healthcare Data Insights**: Analyzes healthcare-related data for trends and insights.

This model, trained with **Unsloath**, achieved a performance boost by utilizing advanced techniques such as **Reinforcement Learning (RL)**, fine-tuning it for accurate, human-like responses.


## Additional Resources

- **Unsloath**: Learn more about [Unsloath](https://unsloath.com), a tool that speeds up AI model training.
- **Hugging Face Model Page**: Visit the model on Hugging Face [here](https://huggingface.co/Aviralansh/vitals-gguf-16bit).

---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---

# Uploaded  model

- **Developed by:** Aviralansh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)