Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
+base_model: Meta/Meta-Llama-3.1-8B
 language:
 - en
 license: apache-2.0
@@ -9,14 +9,38 @@ tags:
 - unsloth
 - llama
 - gguf
+datasets:
+- student-abdullah/BigPharma_Generic_Dataset
 ---
 
-# Uploaded  model
+
+# Uploaded model
 
 - **Developed by:** student-abdullah
 - **License:** apache-2.0
-- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
-
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+- **Finetuned from model:** Meta/Meta-Llama-3.1-8B
 
+---
+# 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 model is fine-tuned from the Meta/Meta-Llama-3.1-8B base model to enhance its capabilities in generating relevant and accurate responses related to generic medications under the PMBJP scheme. The fine-tuning process included the following hyperparameters:
+
+- Max Tokens: 512
+- LoRA Alpha: 12
+- LoRA Rank (r): 128
+- Gradient Accumulation Steps: 32
+- Batch Size: 2
+- Qunatization: 8 bits
+
+---
+# Model Quantitative Performace
+- Training Quantitative Loss: 0.262 (at final 160th epoch)
+
+---
+# Limitations
+- Token Limitations: With a max token limit of 512, the model might not handle very long queries or contexts effectively.
+- Training Data Limitations: The model’s performance is contingent on the quality and coverage of the fine-tuning dataset, which may affect its generalizability to different contexts or medications not covered in the dataset.
+- Potential Biases: As with any model fine-tuned on specific data, there may be biases based on the dataset used for training.