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.

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.

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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.

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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

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)

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Expected Output

"The user reported a delayed order (12345), and support confirmed it will arrive tomorrow."

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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.

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