Update README.md

README.md

@@ -1,22 +1,39 @@
 ---
-base_model: unsloth/Llama-3.2-1B
+base_model: meta-llama/Llama-3.2-1B
 language:
 - en
-license: apache-2.0
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
+- hi
+datasets:
+- student-abdullah/BigPharma_Generic_Q-A_Format_Augemented_Hinglish_Dataset
 ---
-
-# Uploaded  model
+# LoRA Adapter Layers!
+# Uploaded model
 
 - **Developed by:** student-abdullah
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Llama-3.2-1B
-
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+- **Finetuned from model:** meta-llama/Llama-3.2-1B
+- **Created on:** 29th September, 2024
+- **Full model:** student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09
 
+---
+# Acknowledgement
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+---
+# Model Description
+This LoRA adapter layer model is fine-tuned from the meta-llama/Llama-3.2-1B base model to specialisation related to generic medications under the PMBJP scheme. The fine-tuning process included the following hyperparameters:
+
+- Fine Tuning Template: Llama Q&A
+- Max Tokens: 512
+- LoRA Alpha: 32
+- LoRA Rank (r): 128
+- Learning rate: 1.5e-4
+- Gradient Accumulation Steps: 4
+- Batch Size: 8
+
+---
+# Model Quantitative Performace
+- Training Quantitative Loss: 0.1207 (at final 800th epoch)
+
+---
+# Limitations
+- This is not a fully compiled model, rather just LoRA layers