Update README.md

README.md

@@ -1,9 +1,52 @@
 ---
-library_name: peft
+library_name: transformers
+tags:
+- code
+- instruct
+- llama2
+datasets:
+- Zangs3011/no_robots_FalconChatFormated
+base_model: llama/Llama-2-7b-hf
+license: apache-2.0
 ---
-## Training procedure
 
-### Framework versions
+### Finetuning Overview:
 
+**Model Used:** llama/Llama-2-7b-hf
+**Dataset:** Zangs3011/no_robots_FalconChatFormated  
 
-- PEFT 0.5.0
+#### Dataset Insights:
+
+The WizardLM/WizardLM_evol_instruct_70k dataset, tailored specifically for enhancing interactive capabilities, it was developed using EVOL-Instruct method.Which will basically enhance a smaller dataset, with tougher quesitons for the LLM to perform
+#### 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 39mins 4secs for 1 epoch using an A6000 48GB GPU.
+- Costed `$1.313` for the entire epoch.
+
+#### Hyperparameters & Additional Details:
+
+- **Epochs:** 1
+- **Cost Per Epoch:** $1.313
+- **Total Finetuning Cost:** $1.313
+- **Model Path:** llama/Llama-2-7b-hf
+- **Learning Rate:** 0.0002
+- **Data Split:** 99% train 1% validation
+- **Gradient Accumulation Steps:** 4
+
+---
+Prompt Structure
+```
+### INSTRUCTION:
+[instruction]
+
+### RESPONSE:
+[text]
+```
+Eval loss :
+
+![eval loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/hQM1RVC5_E2z7gImQPqF5.png)
+
+license: apache-2.0