|
--- |
|
license: mit |
|
datasets: |
|
- avaliev/chat_doctor |
|
language: |
|
- en |
|
base_model: |
|
- prithivMLmods/Llama-Doctor-3.2-3B-Instruct |
|
pipeline_tag: text-generation |
|
library_name: transformers |
|
tags: |
|
- pytorch |
|
- Llama-Cpp |
|
- Llama3.2 |
|
- Llama-Instruct |
|
- Llama-Doctor |
|
--- |
|
## Llama-Doctor-3.2-3B-Instruct-GGUF |
|
|
|
| File Name | Size | Description | Upload Status | |
|
|----------------------------------------|------------|--------------------------------------|----------------| |
|
| `.gitattributes` | 1.82 kB | Git attributes file | Uploaded | |
|
| `README.md` | 242 Bytes | Updated README file | Uploaded | |
|
| `config.json` | 31 Bytes | Model configuration | Uploaded | |
|
| `Llama-Doctor-3.2-3B-Instruct.F16.gguf` | 6.43 GB | PyTorch model file (F16) | Uploaded (LFS) | |
|
| `Llama-Doctor-3.2-3B-Instruct.Q4_K_M.gguf` | 2.02 GB | PyTorch model file (Q4_K_M) | Uploaded (LFS) | |
|
| `Llama-Doctor-3.2-3B-Instruct.Q5_K_M.gguf` | 2.32 GB | PyTorch model file (Q5_K_M) | Uploaded (LFS) | |
|
| `Llama-Doctor-3.2-3B-Instruct.Q8_0.gguf` | 3.42 GB | PyTorch model file (Q8_0) | Uploaded (LFS) | |
|
| `Modelfile` | 2.04 kB | Model file (unknown format) | Uploaded | |
|
|
|
The **Llama-Doctor-3.2-3B-Instruct** model is designed for **text generation** tasks, particularly in contexts where instruction-following capabilities are needed. This model is a fine-tuned version of the base **Llama-3.2-3B-Instruct** model and is optimized for understanding and responding to user-provided instructions or prompts. The model has been trained on a specialized dataset, **avaliev/chat_doctor**, to enhance its performance in providing conversational or advisory responses, especially in medical or technical fields. |
|
|
|
### Key Use Cases: |
|
1. **Conversational AI**: Engage in dialogue, answering questions, or providing responses based on user instructions. |
|
2. **Text Generation**: Generate content, summaries, explanations, or solutions to problems based on given prompts. |
|
3. **Instruction Following**: Understand and execute instructions, potentially in complex or specialized domains like medical, technical, or academic fields. |
|
|
|
The model leverages a **PyTorch-based architecture** and comes with various files such as configuration files, tokenizer files, and special tokens maps to facilitate smooth deployment and interaction. |
|
|
|
### Intended Applications: |
|
- **Chatbots** for customer support or virtual assistants. |
|
- **Medical Consultation Tools** for generating advice or answering medical queries (given its training on the **chat_doctor** dataset). |
|
- **Content Creation** tools, helping generate text based on specific instructions. |
|
- **Problem-solving Assistants** that offer explanations or answers to user queries, particularly in instructional contexts. |
|
|
|
# Run with Ollama [ Ollama Run ] |
|
|
|
## Overview |
|
|
|
Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes. |
|
|
|
## Table of Contents |
|
|
|
- [Download and Install Ollama](#download-and-install-ollama) |
|
- [Steps to Run GGUF Models](#steps-to-run-gguf-models) |
|
- [1. Create the Model File](#1-create-the-model-file) |
|
- [2. Add the Template Command](#2-add-the-template-command) |
|
- [3. Create and Patch the Model](#3-create-and-patch-the-model) |
|
- [Running the Model](#running-the-model) |
|
- [Sample Usage](#sample-usage) |
|
|
|
## Download and Install Ollama🦙 |
|
|
|
To get started, download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your Windows or Mac system. |
|
|
|
## Steps to Run GGUF Models |
|
|
|
### 1. Create the Model File |
|
First, create a model file and name it appropriately. For example, you can name your model file `metallama`. |
|
|
|
### 2. Add the Template Command |
|
In your model file, include a `FROM` line that specifies the base model file you want to use. For instance: |
|
|
|
```bash |
|
FROM Llama-3.2-1B.F16.gguf |
|
``` |
|
|
|
Ensure that the model file is in the same directory as your script. |
|
|
|
### 3. Create and Patch the Model |
|
Open your terminal and run the following command to create and patch your model: |
|
|
|
```bash |
|
ollama create metallama -f ./metallama |
|
``` |
|
|
|
Once the process is successful, you will see a confirmation message. |
|
|
|
To verify that the model was created successfully, you can list all models with: |
|
|
|
```bash |
|
ollama list |
|
``` |
|
|
|
Make sure that `metallama` appears in the list of models. |
|
|
|
--- |
|
|
|
## Running the Model |
|
|
|
To run your newly created model, use the following command in your terminal: |
|
|
|
```bash |
|
ollama run metallama |
|
``` |
|
|
|
### Sample Usage |
|
|
|
In the command prompt, you can execute: |
|
|
|
```bash |
|
D:\>ollama run metallama |
|
``` |
|
|
|
You can interact with the model like this: |
|
|
|
```plaintext |
|
>>> write a mini passage about space x |
|
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration. |
|
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in |
|
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have |
|
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented |
|
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes |
|
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without |
|
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X |
|
plays a pivotal role in pushing the boundaries of human exploration and settlement. |
|
``` |
|
|
|
--- |
|
|
|
## Conclusion |
|
|
|
With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient. |
|
|
|
|
|
- This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include. |
