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

Conversation Summarization:
- Generates concise and meaningful summaries of long chats, discussions, or threads.
Context Preservation:
- Maintains critical points, ensuring important details aren't omitted.
Text Summarization:
- Works beyond chats; supports summarizing articles, documents, or reports.
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

Customer Support Logs:
- Summarize chat logs or support tickets for insights and reporting.
Meeting Notes:
- Generate concise summaries of meeting transcripts.
Document Summarization:
- Create short summaries for lengthy reports or articles.
Content Generation Pipelines:
- Automate summarization for newsletters, blogs, or email digests.
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.

Download and Install Ollama
Steps to Run GGUF Models
Running the Model
Sample Usage

Download and Install Ollama🦙

To get started, download Ollama from 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:

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:

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:

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:

ollama run metallama

Sample Usage / Test

In the command prompt, you can execute:

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.

prithivMLmods/Llama-Chat-Summary-3.2-3B-GGUF

Llama-Chat-Summary-3.2-3B: Context-Aware Summarization Model