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--- |
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library_name: transformers |
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tags: |
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- ipex |
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- intel |
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- gaudi |
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- guanaco |
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- PEFT |
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- optimum-habana |
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license: apache-2.0 |
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datasets: |
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- timdettmers/openassistant-guanaco |
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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 is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on [timdettmers/openassistant-guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). |
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## Model Details |
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### Model Description |
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This is a fine-tuned version of the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/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. |
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- **Developed by:** Migara Amarasinghe |
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- **Model type:** LLM |
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- **Language(s) (NLP):** English |
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- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |
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## Uses |
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### Direct Use |
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This model can be used for text generation tasks such as: |
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- Chatbots |
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- Automated content creation |
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- Text completion and augmentation |
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### Out-of-Scope Use |
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- Use in real-time applications where latency is critical |
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- Use in highly sensitive domains without thorough evaluation and testing |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## Training Details |
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### Training Hyperparameters |
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<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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- Training regime: Mixed precision training using bf16 |
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- Number of epochs: 3 |
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- Learning rate: 1e-4 |
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- Batch size: 16 |
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- Seq length: 512 |
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## Technical Specifications |
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### Compute Infrastructure |
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#### Hardware |
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- Intel Gaudi 2 AI Accelerator |
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- Intel(R) Xeon(R) Platinum 8380 CPU @ 2.30GHz |
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#### Software |
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- Transformers library |
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- Optimum Habana library |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** Intel Gaudi AI Accelerator |
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- **Hours used:** < 1 hour |
