Update README.md

README.md

@@ -1,9 +1,40 @@
 ---
+datasets:
+- nampdn-ai/tiny-codes
 library_name: peft
+tags:
+- llama2
+- llama2-7b
+- code generation
+- code-generation
+- code
+- instruct
+- instruct-code
+- code-alpaca
+- alpaca-instruct
+- alpaca
+- llama7b
+- gpt2
+license: apache-2.0
 ---
 ## Training procedure
+We finetuned [Llama 2 7B model](https://huggingface.co/meta-llama/Llama-2-7b-hf) from Meta on [nampdn-ai/tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes) for ~ 10,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm).
 
-### Framework versions
+This dataset contains **1.63 million rows** and is a collection of short and clear code snippets that can help LLM models learn how to reason with both natural and programming languages. The dataset covers a wide range of programming languages, such as Python, TypeScript, JavaScript, Ruby, Julia, Rust, C++, Bash, Java, C#, and Go. It also includes two database languages: Cypher (for graph databases) and SQL (for relational databases) in order to study the relationship of entities. 
+
+The finetuning session got completed in 53 hours and costed us ~ `$125` for the entire finetuning run!
 
+#### Hyperparameters & Run details:
+- Model Path: meta-llama/Llama-2-7b-hf
+- Dataset: nampdn-ai/tiny-codes
+- Learning rate: 0.0002
+- Number of epochs: 1 (10k steps)
+- Data split: Training: 90% / Validation: 10%
+- Gradient accumulation steps: 1
+
+### Framework versions
 
 - PEFT 0.4.0
+
+### Loss metrics:
+![training loss](train-loss.png "Training loss")