Model Card for Model ID
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on tatsu-lab/alpaca dataset.
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
Model Details
Model Description
This is a fine-tuned version of the meta-llama/Meta-Llama-3-8B-Instruct model using Parameter Efficient Fine Tuning (PEFT) with Low Rank Adaptation (LoRA) on the Intel Gaudi 2 AI accelerator. This model can be used for various text generation tasks including chatbots, content creation, and other NLP applications.
- Developed by: Migara Amarasinghe
- Model type: LLM
- Language(s) (NLP): English
- Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct
Uses
Direct Use
This model can be used for text generation tasks such as:
- Chatbots
- Automated content creation
- Text completion and augmentation
Out-of-Scope Use
- Use in real-time applications where latency is critical
- Use in highly sensitive domains without thorough evaluation and testing
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Hyperparameters
- Training regime: Mixed precision training using bf16
- Number of epochs: 3
- Learning rate: 1e-4
- Batch size: 16
- Seq length: 512
Technical Specifications
Compute Infrastructure
Hardware
- Intel Gaudi 2 AI Accelerator
- Intel(R) Xeon(R) Platinum 8380 CPU @ 2.30GHz
Software
- Transformers library
- Optimum Habana library
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: Intel Gaudi 2 AI Accelerator
- Hours used: < 1 hour