---
license: creativeml-openrail-m
datasets:
- prithivMLmods/Context-Based-Chat-Summary-Plus
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- safetensors
- chat-summary
- 3B
- Ollama
- text-generation-inference
- trl
- Llama3.2
---

### **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`                           | 1.57 kB         | Git LFS tracking configuration.                  | Uploaded          |
| `README.md`                                | 42 Bytes        | Initial commit with minimal documentation.       | Uploaded          |
| `config.json`                              | 1.03 kB         | Model configuration settings.                    | Uploaded          |
| `generation_config.json`                   | 248 Bytes       | Generation-specific configurations.              | Uploaded          |
| `pytorch_model-00001-of-00002.bin`         | 4.97 GB         | Part 1 of the PyTorch model weights.             | Uploaded (LFS)    |
| `pytorch_model-00002-of-00002.bin`         | 1.46 GB         | Part 2 of the PyTorch model weights.             | Uploaded (LFS)    |
| `pytorch_model.bin.index.json`             | 21.2 kB         | Index file for the model weights.                | Uploaded          |
| `special_tokens_map.json`                  | 477 Bytes       | Mapping of special tokens for the tokenizer.     | Uploaded          |
| `tokenizer.json`                           | 17.2 MB         | Pre-trained tokenizer file.                      | Uploaded (LFS)    |
| `tokenizer_config.json`                    | 57.4 kB         | Configuration file for the tokenizer.            | 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.

---