Update README.md

README.md

@@ -18,7 +18,7 @@ pipeline_tag: text-generation
 
 Welcome to **GenZ**, an advanced Large Language Model (LLM) fine-tuned on the foundation of Meta's open-source Llama V2 70B parameter model. At Bud Ecosystem, we believe in the power of open-source collaboration to drive the advancement of technology at an accelerated pace. Our vision is to democratize access to fine-tuned LLMs, and to that end, we will be releasing a series of models across different parameter counts (7B, 13B, and 70B) and quantizations (32-bit and 4-bit) for the open-source community to use, enhance, and build upon. 
 
-<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/MTBench_CompareChart_28July2023.png" width="500"></p>
+<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_compare" width="500"></p>
 
 The smaller quantization version of our models makes them more accessible, enabling their use even on personal computers. This opens up a world of possibilities for developers, researchers, and enthusiasts to experiment with these models and contribute to the collective advancement of language model technology. 
 
@@ -47,6 +47,28 @@ And this isn't the end. It's just the beginning of a journey towards creating mo
 
 ---
 
+<h2>Evaluations 🎯</h2>
+
+Evaluating our model is a key part of our fine-tuning process. It helps us understand how our model is performing and how it stacks up against other models. Here's a look at some of the key evaluations for GenZ 70B:
+
+<h3>Benchmark Comparison</h3>
+
+We've compared GenZ models to understand the improvements our fine-tuning has achieved.
+
+| Model Name | MT Bench | MMLU | Human Eval | BBH |
+|:----------:|:--------:|:----:|:----------:|:----:|
+| Genz 13B   | 6.12     | 53.62| 17.68      | 37.76|
+| Genz 13B v2| 6.79     | 53.68| 21.95      | 38.1 |
+| Genz 70B   | 7.33     | 70.32| 37.8       |54.69 |
+
+<h3>MT Bench Score</h3>
+
+A key evaluation metric we use is the MT Bench score. This score provides a comprehensive assessment of our model's performance across a range of tasks.
+
+<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_score.png" width="500"></p>
+
+
+---
 
 <h2>Getting Started on Hugging Face 🤗</h2>
 
@@ -157,29 +179,6 @@ Here are the hyperparameters we used for fine-tuning:
 | Precision | FP16 |
 | Optimizer | AdamW |
 
----
-
-<h2>Evaluations 🎯</h2>
-
-Evaluating our model is a key part of our fine-tuning process. It helps us understand how our model is performing and how it stacks up against other models. Here's a look at some of the key evaluations for GenZ 70B:
-
-<h3>Benchmark Comparison</h3>
-
-We've compared GenZ models to understand the improvements our fine-tuning has achieved.
-
-| Model Name | MT Bench | MMLU | Human Eval | Hellaswag | BBH |
-|:----------:|:--------:|:----:|:----------:|:---------:|:----:|
-| Genz 13B   | 6.12     | 53.62| 17.68      | 77.38     | 37.76|
-| Genz 13B v2| 6.79     | 53.68| 21.95      | 77.48     | 38.1 |
-| Genz 70B   | 7.33     | 70.32|            |           |      |
-
-<h3>MT Bench Score</h3>
-
-A key evaluation metric we use is the MT Bench score. This score provides a comprehensive assessment of our model's performance across a range of tasks.
-
-<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_score.png" width="500"></p>
-
-
 ---
 
 <h2>Looking Ahead 👀</h2>