update readme

README.md

@@ -8,3 +8,66 @@ sdk_version: 5.0.1
 app_file: app.py
 pinned: false
 ---
+
+This is a space where you can compare two models using the technique "LLM as a Judge". LLM as a Judge uses a LLM itself for judging the response from two LLMs, and compare them based on certain evaluation metrics which are relevant for the task.
+
+In this space, our default placeholder repos and models compare two LLMs finetuned on the same base model, [Llama 3.2 3B parameter model](unsloth/Llama-3.2-3B-Instruct). Both of them are finetuned on the FineTome-100k dataset, but they have been finetuned on a different amount of data.
+
+The models were finetuned using [Unsloth](https://unsloth.ai/), a framework which allows finetuning, training and inference with LLMs 2x faster.
+
+## Default models and their hyperparameters
+
+### forestav/LoRA-2000
+
+Finetuned on 2000 steps
+Quantization method: float16
+
+### KolumbusLindh/LoRA-4100
+
+Finetuned on 4100 steps
+Quantization method: float16
+
+### Hyperparameters
+
+Both models used the same hyperparameters during training.
+`per_device_train_batch_size = 2`
+`gradient_accumulation_steps=4`
+`learning_rate=2e-4`
+`optim="adamw_8bit"`
+`weight_decay=0.01`
+`lr_scheduler_type="linear"`
+
+We chose float16 as the quantization method as it has the fastest conversion and retains 100% accuracy. However, it is slow and memory hungry which is a disadvantage.
+Source: https://github.com/unslothai/unsloth/wiki
+
+## Judge
+
+We are using the KolumbusLindh/LoRA-4100 model as a judge. However, for better accuracy one should use a stronger model such as GPT-4, which can evaluate the responses more thoroughly.
+
+## Evaluation using GPT-4
+
+To better evaluate our fine-tuned models, we let GPT-4 be our judge, when the respective model answered the following prompts:
+
+1. Describe step-by-step how to set up a tent in a windy environment.
+
+2. How-To Guidance: "Explain how to bake a chocolate cake without using eggs."
+
+3. Troubleshooting: "Provide instructions for troubleshooting a laptop that won’t turn on."
+
+4. Educational Explanation: "Teach a beginner how to solve a Rubik’s Cube in simple steps."
+
+5. DIY Project: "Give detailed instructions for building a birdhouse using basic tools."
+
+6. Fitness Routine: "Design a beginner-friendly 15-minute workout routine that requires no equipment."
+
+7. Cooking Tips: "Explain how to properly season and cook a medium-rare steak."
+
+8. Technical Guidance: "Write a step-by-step guide for setting up a local Git repository and pushing code to GitHub."
+
+9. Emergency Response: "Provide instructions for administering first aid to someone with a sprained ankle."
+
+10. Language Learning: "Outline a simple plan for a beginner to learn Spanish in 30 days."
+
+### Results
+
+#### Prompt 1: Describe step-by-step how to set up a tent in a windy environment.

app.py

@@ -75,7 +75,7 @@ with gr.Blocks(title="LLM as a Judge") as demo:
     gr.Markdown("Welcome to the LLM as a Judge demo! This application uses the LoRA model to evaluate responses generated by two different models based on user-specified criteria. You can select up to 3 evaluation criteria and provide a prompt to generate responses from the models. The LoRA model will then evaluate the responses based on the selected criteria and determine the winner.")
 
     # Model inputs
-    repo_a_input = gr.Textbox(label="Model A Repository", placeholder="Enter the Hugging Face repo name for Model A...", value="forestav/gguf_lora_model")
+    repo_a_input = gr.Textbox(label="Model A Repository", placeholder="Enter the Hugging Face repo name for Model A...", value="forestav/LoRA-2000")
     model_a_input = gr.Textbox(label="Model A File Name", placeholder="Enter the model filename for Model A...", value="unsloth.F16.gguf")
     repo_b_input = gr.Textbox(label="Model B Repository", placeholder="Enter the Hugging Face repo name for Model B...", value="KolumbusLindh/LoRA-4100")
     model_b_input = gr.Textbox(label="Model B File Name", placeholder="Enter the model filename for Model B...", value="unsloth.F16.gguf")