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--- |
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library_name: transformers |
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tags: |
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- Text Generation |
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- gaudi |
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- alpaca |
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- llm |
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- optimum-habana |
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- gaudi2 |
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- llama |
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- llama3 |
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- TextGeneration |
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license: apache-2.0 |
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datasets: |
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- tatsu-lab/alpaca |
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language: |
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- en |
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--- |
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# Model Card for Model ID |
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This model was fine-tuned from the VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct |
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## Model Details |
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### Model Description |
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The gopalakrishnan-d/Llama3-8b-VAGO-Gaudi-Alpaca-v1 model is a fine-tuned variant of the Llama3 architecture with 8 billion parameters. |
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This version has been specifically enhanced for better performance on diverse language tasks, utilizing the Gaudi 2 Accelerator to optimize the training process. |
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- **Hardware Type:** Intel Gaudi2 Accelerator |
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- **Cloud Provider:** Intel® Tiber™ Developer Cloud |
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- **Developed by:** gopalakrishnan-d |
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- **Model type:** Fine-Tuned LLM |
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- **Language(s) (NLP):** English |
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- **License:** **Apache 2.0 License** |
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- **Finetuned from model [optional]:** VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct |
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## Uses |
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Conversational Agents |
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Description: Develop advanced chatbots or virtual assistants for customer service, support, and interactive help desks. |
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Content Creation |
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Description: Assist in generating high-quality written content, including articles, marketing copy, and creative writing. |
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Educational Tools |
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Description: Create interactive educational tools for tutoring, providing explanations, and generating study materials. |
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Instruction-Based Applications |
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Description: Implement systems that follow and execute detailed user commands, such as workflow automation tools and interactive guides. |
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Personalized Recommendations |
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Description: Provide tailored suggestions for products, services, or content based on user preferences and interactions. |
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### Direct Use |
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Customer Service Chatbots |
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Content Generation Tools |
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Educational Tutoring Systems |
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Workflow Automation Systems |
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Personalized Recommendation Engines |
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### Training Data |
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tatsu-lab/alpaca |
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#### Training Hyperparameters |
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- learning_rate: 5e-06 (Low Rate) |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 100 |
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- gradient_accumulation_steps: 1 |
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- optimizer: Adam |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- lora_rank=16 |
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- lora_alpha=32 |
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## Evaluation |
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epoch = 3.0 |
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eval_accuracy = 0.6859 |
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eval_loss = 1.3052 |
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### Results |
