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Model: prithivMLmods/Llama-Chat-Summary-3.2-3B Source: Original Platform
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README.md
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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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---
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42
config.json
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config.json
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{
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"_name_or_path": "meta-llama/Llama-3.2-3B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"use_cache": true,
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||||
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user