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Model: prithivMLmods/Llama-Chat-Summary-3.2-3B-GGUF
Source: Original Platform

.gitattributes

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+Llama-Chat-Summary-3.2-3B.F16.gguf filter=lfs diff=lfs merge=lfs -text
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Llama-Chat-Summary-3.2-3B.F16.gguf

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Modelfile

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+
+FROM /content/prithivMLmods/Llama-Chat-Summary-3.2-3B-GGUF/Llama-Chat-Summary-3.2-3B.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:
+- 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.
+
+---

config.json

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

configuration.json

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