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

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.

Key Features

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

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

1. 1.Text Summarization:
2. 2.Works beyond chats; supports summarizing articles, documents, or reports.

1. 1.Fine-Tuned Efficiency:
2. 2.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. 1.Customer Support Logs:
2. 2.Summarize chat logs or support tickets for insights and reporting.

1. 1.Meeting Notes:
2. 2.Generate concise summaries of meeting transcripts.

1. 1.Document Summarization:
2. 2.Create short summaries for lengthy reports or articles.

1. 1.Content Generation Pipelines:
2. 2.Automate summarization for newsletters, blogs, or email digests.

1. 1.Context Extraction for AI Systems:
2. 2.Preprocess chat or conversation logs for downstream AI applications.

Load the Model

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

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.