Update README.md

README.md

@@ -23,8 +23,6 @@ General instruction-following llm finetuned from [mistralai/Mistral-7B-v0.1](htt
 
 ## Model Details
 
-### Model Description
-
 This instruction-following llm was built via parameter-efficient QLoRA finetuning of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the first 5k rows of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin). Finetuning was executed on 1x A100 (40 GB SXM) for roughly 1 hour on Google Colab.
 
 - **Developed by:** Daniel Furman
@@ -33,11 +31,11 @@ This instruction-following llm was built via parameter-efficient QLoRA finetunin
 - **License:** Yi model license
 - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 
-### Model Sources 
+## Model Sources 
 
 - **Repository:** [github.com/daniel-furman/sft-demos](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/one_gpu/mistral/sft-mistral-7b-instruct-peft.ipynb)
 
-### Evaluation
+## Evaluation Results
 
 | Metric                | Value |
 |-----------------------|-------|
@@ -49,9 +47,9 @@ This instruction-following llm was built via parameter-efficient QLoRA finetunin
 
 We use Eleuther.AI's [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
 
-## How to Get Started with the Model
+## Basic Usage
 
-Use the code below to get started with the model.
+*Note*: Use the code below to get started with the sft models herein, as ran on 1x A100.  
 
 ```python
 !pip install -q -U transformers peft torch accelerate bitsandbytes einops sentencepiece
@@ -132,7 +130,7 @@ Remember, when writing emails, always keep in mind your audience and their prefe
 
 </details>
 
-### Speeds, Sizes, Times 
+## Speeds, Sizes, Times 
 
 | runtime / 50 tokens (sec) | GPU             | attn | torch dtype | VRAM (GB) |
 |:-----------------------------:|:----------------------:|:---------------------:|:-------------:|:-----------------------:|
@@ -153,7 +151,7 @@ You are a helpful assistant.<|im_end|>
 <|im_start|>assistant
 ```
 
-### Training Hyperparameters
+## Training Hyperparameters
 
 
 We use the [SFTTrainer](https://huggingface.co/docs/trl/main/en/sft_trainer) from `trl` to fine-tune llms on instruction-following datasets.