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Model: prithivMLmods/Llama-Sentient-3.2-3B-Instruct-GGUF
Source: Original Platform

.gitattributes

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+Llama-Sentient-3.2-3B-Instruct.F16.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-Sentient-3.2-3B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-Sentient-3.2-3B-Instruct.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-Sentient-3.2-3B-Instruct.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

Llama-Sentient-3.2-3B-Instruct.F16.gguf

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Modelfile

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+
+FROM /content/prithivMLmods/Llama-Sentient-3.2-3B-Instruct-GGUF/Llama-Sentient-3.2-3B-Instruct.F16.gguf
+TEMPLATE """{{ if .Messages }}
+{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
+{{- if .System }}
+
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+
+You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the original use question.
+{{- end }}
+{{- end }}<|eot_id|>
+{{- range $i, $_ := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 }}
+{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
+{{- if and $.Tools $last }}
+
+Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
+
+Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
+
+{{ $.Tools }}
+{{- end }}
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
+{{- if .ToolCalls }}
+
+{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
+{{- else }}
+
+{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
+{{- end }}
+{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}
+{{- end }}
+{{- else }}
+{{- if .System }}<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"""
+PARAMETER stop "<|start_header_id|>"
+PARAMETER stop "<|end_header_id|>"
+PARAMETER stop "<|eot_id|>"
+PARAMETER stop "<|eom_id|>"
+PARAMETER temperature 1.5
+PARAMETER min_p 0.1

README.md

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+---
+license: creativeml-openrail-m
+datasets:
+- mlabonne/lmsys-arena-human-preference-55k-sharegpt
+language:
+- en
+base_model:
+- prithivMLmods/Llama-Sentient-3.2-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Llama-Cpp
+- safetensors
+- Llama3.2
+- 3B
+- Instruct
+- Sentient
+- Human-Preference
+- Ollama
+- Humane
+---
+
+## Llama-Sentient-3.2-3B-Instruct-GGUF
+
+| File Name [ Uploaded File ]                                      | Size         | Description                             | Upload Status  |
+|------------------------------------------------|--------------|-----------------------------------------|----------------|
+| `.gitattributes`                               | 1.83 kB      | Git attributes configuration file       | Uploaded       |
+| `README.md`                                    | 330 Bytes    | Updated README                          | Uploaded       |
+| `config.json`                                  | 31 Bytes     | Configuration file                      | Uploaded       |
+| `Llama-Sentient-3.2-3B-Instruct.F16.gguf`       | 6.43 GB      | Llama Sentient model (F16)              | Uploaded (LFS) |
+| `Llama-Sentient-3.2-3B-Instruct.Q4_K_M.gguf`    | 2.02 GB      | Llama Sentient model (Q4_K_M)           | Uploaded (LFS) |
+| `Llama-Sentient-3.2-3B-Instruct.Q5_K_M.gguf`    | 2.32 GB      | Llama Sentient model (Q5_K_M)           | Uploaded (LFS) |
+| `Llama-Sentient-3.2-3B-Instruct.Q8_0.gguf`      | 3.42 GB      | Llama Sentient model (Q8_0)             | Uploaded (LFS) |
+| `Modelfile`                                    | 2.04 kB      | Model file                              | Uploaded       |
+
+The **Llama-Sentient-3.2-3B-Instruct** model is a fine-tuned version of the **Llama-3.2-3B-Instruct** model, optimized for **text generation** tasks, particularly where instruction-following abilities are critical. This model is trained on the **mlabonne/lmsys-arena-human-preference-55k-sharegpt** dataset, which enhances its performance in conversational and advisory contexts, making it suitable for a wide range of applications.
+
+### Key Use Cases:
+1. **Conversational AI**: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants.
+2. **Text Generation**: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts.
+3. **Instruction Following**: Follow specific instructions with accuracy, making it ideal for tasks that require structured guidance, such as technical troubleshooting or educational assistance.
+
+The model uses a **PyTorch-based architecture** and includes a range of necessary files such as configuration files, tokenizer files, and model weight files for deployment.
+
+### Intended Applications:
+- **Chatbots** for virtual assistance, customer support, or as personal digital assistants.
+- **Content Creation Tools**, aiding in the generation of written materials, blog posts, or automated responses based on user inputs.
+- **Educational and Training Systems**, providing explanations and guided learning experiences in various domains.
+- **Human-AI Interaction** platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks.
+
+With its strong foundation in instruction-following and conversational contexts, the **Llama-Sentient-3.2-3B-Instruct** model offers versatile applications for both general and specialized domains.
+
+# Run with Ollama 🦙
+
+## 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.

config.json

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+{
+    "model_type": "llama"
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}