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--- |
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license: creativeml-openrail-m |
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datasets: |
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- prithivMLmods/Context-Based-Chat-Summary-Plus |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.2-3B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- safetensors |
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- chat-summary |
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- 3B |
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- Ollama |
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- text-generation-inference |
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- trl |
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- Llama3.2 |
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--- |
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### **Llama-Chat-Summary-3.2-3B: Context-Aware Summarization Model** |
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**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. |
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| **File Name** | **Size** | **Description** | **Upload Status** | |
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|--------------------------------------------|------------------|--------------------------------------------------|-------------------| |
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| `.gitattributes` | 1.57 kB | Git LFS tracking configuration. | Uploaded | |
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| `README.md` | 42 Bytes | Initial commit with minimal documentation. | Uploaded | |
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| `config.json` | 1.03 kB | Model configuration settings. | Uploaded | |
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| `generation_config.json` | 248 Bytes | Generation-specific configurations. | Uploaded | |
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| `pytorch_model-00001-of-00002.bin` | 4.97 GB | Part 1 of the PyTorch model weights. | Uploaded (LFS) | |
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| `pytorch_model-00002-of-00002.bin` | 1.46 GB | Part 2 of the PyTorch model weights. | Uploaded (LFS) | |
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| `pytorch_model.bin.index.json` | 21.2 kB | Index file for the model weights. | Uploaded | |
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| `special_tokens_map.json` | 477 Bytes | Mapping of special tokens for the tokenizer. | Uploaded | |
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| `tokenizer.json` | 17.2 MB | Pre-trained tokenizer file. | Uploaded (LFS) | |
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| `tokenizer_config.json` | 57.4 kB | Configuration file for the tokenizer. | Uploaded | |
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### **Key Features** |
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1. **Conversation Summarization:** |
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- Generates concise and meaningful summaries of long chats, discussions, or threads. |
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2. **Context Preservation:** |
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- Maintains critical points, ensuring important details aren't omitted. |
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3. **Text Summarization:** |
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- Works beyond chats; supports summarizing articles, documents, or reports. |
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4. **Fine-Tuned Efficiency:** |
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- Trained with *Context-Based-Chat-Summary-Plus* dataset for accurate summarization of chat and conversational data. |
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--- |
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### **Training Details** |
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- **Base Model:** [meta-llama/Llama-3.2-3B-Instruct](#) |
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- **Fine-Tuning Dataset:** [prithivMLmods/Context-Based-Chat-Summary-Plus](#) |
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- Contains **98.4k** structured and unstructured conversations, summaries, and contextual inputs for robust training. |
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--- |
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### **Applications** |
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1. **Customer Support Logs:** |
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- Summarize chat logs or support tickets for insights and reporting. |
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2. **Meeting Notes:** |
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- Generate concise summaries of meeting transcripts. |
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3. **Document Summarization:** |
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- Create short summaries for lengthy reports or articles. |
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4. **Content Generation Pipelines:** |
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- Automate summarization for newsletters, blogs, or email digests. |
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5. **Context Extraction for AI Systems:** |
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- Preprocess chat or conversation logs for downstream AI applications. |
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#### **Load the Model** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Llama-Chat-Summary-3.2-3B" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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``` |
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#### **Generate a Summary** |
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```python |
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prompt = """ |
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Summarize the following conversation: |
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User1: Hey, I need help with my order. It hasn't arrived yet. |
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User2: I'm sorry to hear that. Can you provide your order number? |
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User1: Sure, it's 12345. |
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User2: Let me check... It seems there was a delay. It should arrive tomorrow. |
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User1: Okay, thank you! |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=100, temperature=0.7) |
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summary = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print("Summary:", summary) |
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``` |
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--- |
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### **Expected Output** |
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**"The user reported a delayed order (12345), and support confirmed it will arrive tomorrow."** |
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--- |
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### **Deployment Notes** |
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- **Serverless API:** |
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This model currently lacks sufficient usage for serverless endpoints. Use **dedicated endpoints** for deployment. |
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- **Performance Requirements:** |
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- GPU with sufficient memory (recommended for large models). |
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- Optimization techniques like quantization can improve efficiency for inference. |
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--- |
