Update README.md

README.md

@@ -5,34 +5,43 @@ tags:
 - trl
 - sft
 - generated_from_trainer
+- Dolly
+- ipex
+- Max Series GPU
 base_model: meta-llama/Llama-2-7b-hf
+datasets:
+- databricks/databricks-dolly-15k
 model-index:
 - name: My_AGI_llama_2_7B
   results: []
+language:
+- en
+metrics:
+- accuracy
+- bertscore
+- bleu
+pipeline_tag: question-answering
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
 # My_AGI_llama_2_7B
 
-This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
 
-## Model description
+**Model Type:** Fine-Tuned
 
-More information needed
+**Model Base:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
 
-## Intended uses & limitations
+**Datasets Used:** [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
 
-More information needed
+**Author:** [Yuri Achermann](https://huggingface.co/yuriachermann)
 
-## Training and evaluation data
+**Date:** June 03, 2024
 
-More information needed
+-------------------------
 
 ## Training procedure
 
-### Training hyperparameters
+### Training Hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
@@ -48,8 +57,51 @@ The following hyperparameters were used during training:
 
 ### Framework versions
 
-- PEFT 0.11.1
-- Transformers 4.41.2
-- Pytorch 2.1.0.post0+cxx11.abi
-- Datasets 2.19.2
-- Tokenizers 0.19.1
+- PEFT==0.11.1
+- Transformers==4.41.2
+- Pytorch==2.1.0.post0+cxx11.abi
+- Datasets==2.19.2
+- Tokenizers==0.19.1
+
+-------------------------
+
+## Intended uses & limitations
+
+**Primary Use Case:** The model is intended for generating human-like responses in conversational applications, like chatbots or virtual assistants.
+
+**Limitations:** The model may generate inaccurate or biased content as it reflects the data it was trained on. It is essential to evaluate the generated responses in context and use the model responsibly.
+
+-------------------------
+
+## Evaluation
+
+The evaluation platform consists of Gaudi Accelerators and Xeon CPUs running benchmarks from the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness)
+
+| Average | ARC   | HellaSwag | MMLU  | TruthfulQA | Winogrande |
+|:-------:|:-----:|:---------:|:-----:|:----------:|:----------:|
+| 54.904  | 45.65 | 76.8      | 42.02 | 40.2       | 69.85      |
+
+-------------------------
+
+## Ethical Considerations
+
+The model may inherit biases present in the training data. It is crucial to use the model in a way that promotes fairness and mitigates potential biases.
+
+-------------------------
+
+## Acknowledgments
+
+This fine-tuning effort was made possible by the support of Intel, that provided the computing resources, and [Eduardo Alvarez](https://huggingface.co/eduardo-alvarez).
+Additional shout-out to the creators of the Llama-2-7b-hf model and the contributors to the databricks-dolly-15k dataset.
+
+-------------------------
+
+## Contact Information
+
+For questions or feedback about this model, please contact **[Yuri Achermann](mailto:yuri.achermann@gmail.com)**.
+
+-------------------------
+
+## License
+
+This model is distributed under **Apache 2.0 License**.