---
license: creativeml-openrail-m
datasets:
- prithivMLmods/Context-Based-Chat-Summary-Plus
language:
- en
base_model:
- prithivMLmods/Llama-Chat-Summary-3.2-3B
pipeline_tag: text-generation
library_name: transformers
tags:
- safetensors
- ollama
- llama-cpp
- text-generation-inference
- chat-summary
---
### **Llama-Chat-Summary-3.2-3B: Context-Aware Summarization Model**

**Llama-Chat-Summary-3.2-3B** is a fine-tuned model designed for generating **context-aware summaries** of long conversational or text-based inputs. Built on the **meta-llama/Llama-3.2-3B-Instruct** foundation, this model is optimized to process structured and unstructured conversational data for summarization tasks.

| **File Name**                              | **Size**         | **Description**                                    | **Upload Status** |
|--------------------------------------------|------------------|--------------------------------------------------|-------------------|
| `.gitattributes`                           | 1.81 kB         | Git LFS tracking configuration.                  | Uploaded          |
| `Llama-Chat-Summary-3.2-3B.F16.gguf`       | 6.43 GB         | Full precision (F16) GGUF model file.            | Uploaded (LFS)    |
| `Llama-Chat-Summary-3.2-3B.Q4_K_M.gguf`    | 2.02 GB         | Quantized Q4_K_M GGUF model file.                | Uploaded (LFS)    |
| `Llama-Chat-Summary-3.2-3B.Q5_K_M.gguf`    | 2.32 GB         | Quantized Q5_K_M GGUF model file.                | Uploaded (LFS)    |
| `Llama-Chat-Summary-3.2-3B.Q8_0.gguf`      | 3.42 GB         | Quantized Q8_0 GGUF model file.                  | Uploaded (LFS)    |
| `Modelfile`                                | 2.03 kB         | Model configuration or build script file.        | Uploaded          |
| `README.md`                                | 42 Bytes        | Minimal commit message placeholder.              | Uploaded          |
| `config.json`                              | 31 Bytes        | Model metadata and configuration.                | Uploaded          |

### **Key Features**

1. **Conversation Summarization:**  
   - Generates concise and meaningful summaries of long chats, discussions, or threads.

2. **Context Preservation:**  
   - Maintains critical points, ensuring important details aren't omitted.

3. **Text Summarization:**  
   - Works beyond chats; supports summarizing articles, documents, or reports.

4. **Fine-Tuned Efficiency:**  
   - Trained with *Context-Based-Chat-Summary-Plus* dataset for accurate summarization of chat and conversational data.

---
### **Training Details**

- **Base Model:** [meta-llama/Llama-3.2-3B-Instruct](#)  
- **Fine-Tuning Dataset:** [prithivMLmods/Context-Based-Chat-Summary-Plus](#)  
   - Contains **98.4k** structured and unstructured conversations, summaries, and contextual inputs for robust training.

---
### **Applications**

1. **Customer Support Logs:**  
   - Summarize chat logs or support tickets for insights and reporting.

2. **Meeting Notes:**  
   - Generate concise summaries of meeting transcripts.

3. **Document Summarization:**  
   - Create short summaries for lengthy reports or articles.

4. **Content Generation Pipelines:**  
   - Automate summarization for newsletters, blogs, or email digests.

5. **Context Extraction for AI Systems:**  
   - Preprocess chat or conversation logs for downstream AI applications.

#### **Load the Model**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Llama-Chat-Summary-3.2-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
#### **Generate a Summary**
```python
prompt = """
Summarize the following conversation:
User1: Hey, I need help with my order. It hasn't arrived yet.
User2: I'm sorry to hear that. Can you provide your order number?
User1: Sure, it's 12345.
User2: Let me check... It seems there was a delay. It should arrive tomorrow.
User1: Okay, thank you!
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)

summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Summary:", summary)
```
---
### **Expected Output**
**"The user reported a delayed order (12345), and support confirmed it will arrive tomorrow."**

---
### **Deployment Notes**

- **Serverless API:**  
   This model currently lacks sufficient usage for serverless endpoints. Use **dedicated endpoints** for deployment.

- **Performance Requirements:**  
   - GPU with sufficient memory (recommended for large models).  
   - Optimization techniques like quantization can improve efficiency for inference.

---
# 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 / Test

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.

---