Update README.md

README.md

@@ -19,4 +19,56 @@ The following `bitsandbytes` quantization config was used during training:
 ### Framework versions
 
 
-- PEFT 0.5.0
+- PEFT 0.5.0
+
+- ---
+library_name: peft
+tags:
+- code
+- instruct
+- gpt2
+datasets:
+- HuggingFaceH4/no_robots
+base_model: gpt2
+license: apache-2.0
+---
+
+### Finetuning Overview:
+
+**Model Used:** gpt2
+
+**Dataset:** HuggingFaceH4/no_robots  
+
+#### Dataset Insights:
+
+[No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better.
+
+#### Finetuning Details:
+
+With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning:
+
+- Was achieved with great cost-effectiveness.
+- Completed in a total duration of 3mins 40s for 1 epoch using an A6000 48GB GPU.
+- Costed `$0.101` for the entire epoch.
+
+#### Hyperparameters & Additional Details:
+
+- **Epochs:** 1
+- **Cost Per Epoch:** $0.101
+- **Total Finetuning Cost:** $0.101
+- **Model Path:** gpt2
+- **Learning Rate:** 0.0002
+- **Data Split:** 100% train 
+- **Gradient Accumulation Steps:** 4
+- **lora r:** 32
+- **lora alpha:** 64
+
+#### Prompt Structure
+```
+<|system|> <|endoftext|> <|user|> [USER PROMPT]<|endoftext|> <|assistant|> [ASSISTANT ANSWER] <|endoftext|>
+```
+#### Training loss :
+
+![training loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/9bgb518kFwtDsFtrHzmTu.png)
+
+license: apache-2.0