Update README.md

README.md

@@ -13,6 +13,20 @@ pipeline_tag: text-generation
 
 This instruction model was built via parameter-efficient QLoRA finetuning of [llama-2-13b](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the first 100k rows of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) (an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)). Finetuning was executed on a single A6000 (48 GB) for roughly 18 hours on the [Lambda Labs](https://cloud.lambdalabs.com/instances) platform.
 
+### Benchmark metrics
+
+| Metric                | Value |
+|-----------------------|-------|
+| MMLU (5-shot)         | 55.76 |
+| ARC (25-shot)         | 59.98 |
+| HellaSwag (10-shot)   | 82.43 |
+| TruthfulQA (0-shot)   | 42.59 |
+| Avg.                  | 60.19 |
+
+We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+
+### Helpful Links
+
 * Model license: Llama 2 Community License Agreement
 * Basic usage: [notebook](assets/basic_inference_llama_2_13b_dolphin.ipynb)
 * Finetuning script: [script](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/one_gpu/llama-2/dolphin/sft-llama-2-13b-dolphin-peft.py)