keerthi4
/

llama_3_8B_openassistant-guanaco

@@ -1,199 +1,85 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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 ---
 library_name: transformers
+tags:
+- ipex
+- intel
+- gaudi
+- PEFT
+license: apache-2.0
+datasets:
+- timdettmers/openassistant-guanaco
 ---
 # Model Card for Model ID
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [timdettmers/openassistant-guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
 ## Model Details
 ### Model Description
+This is a fine-tuned version of the [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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:** Keerthi Nalabotu
+- **Model type:** LLM
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Llama-2-7b-hf)
 ## 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
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 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
+<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- 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 8368 CPU
 #### Software
+- Transformers library
+- Optimum Habana library
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+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).
+- **Hardware Type:** Intel Gaudi AI Accelerator
+- **Hours used:** < 1 hour