Update README.md

README.md

@@ -4,4 +4,46 @@ datasets:
 pipeline_tag: text-generation
 tags:
 - llama
----
+---
+
+Sure, here's an updated version of the model card with the inclusion of hypothetical performance metrics for the fine-tuned Llama 2 model:
+
+---
+
+# Model Card: Fine-tuning Llama 2 for AI2SQL Query Generation
+
+This model card outlines the fine-tuning of the Llama 2 model to generate SQL queries for AI2SQL tasks.
+
+## Model Details
+- **Original Model:** NousResearch/Llama-2-7b-chat-hf
+- **Model Type:** Large Language Model
+- **Fine-tuning Task:** AI2SQL (SQL Query Generation)
+- **Fine-tuned Model Name:** llama-2-7b-miniguanaco
+
+## Implementation
+- **Environment Requirement:** GPU-supported platform with minimum 20GB RAM.
+- **Dependencies:** accelerate==0.21.0, peft==0.4.0, bitsandbytes==0.40.2, transformers==4.31.0, trl==0.4.7
+- **GPU Specification:** T4 or equivalent (as of 24 Aug 2023)
+
+## Training Details
+- **Dataset:** WikiSQL
+- **Method:** Supervised Fine-Tuning (SFT)
+- **Epochs:** 1
+- **Batch Size:** 4 per GPU
+- **Optimization:** AdamW with cosine learning rate schedule
+- **Learning Rate:** 2e-4
+- **Special Features:**
+  - LoRA for efficient parameter adjustment.
+  - 4-bit precision model loading with BitsAndBytes.
+  - Gradient checkpointing and clipping.
+
+## Performance Metrics
+- **Accuracy:** 85% (on a held-out test set from WikiSQL)
+- **Query Generation Time:** Average of 0.5 seconds per query
+- **Resource Efficiency:** Demonstrates 30% reduced memory usage compared to the base model
+
+## Usage and Applications
+TBD
+
+
+Note: The performance metrics provided here are hypothetical and for illustrative purposes only. Actual performance would depend on various factors, including the specifics of the dataset and training regimen.