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app.py

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+import gradio as gr
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+# Load a user-specified model
+def load_user_model(repo_id, model_file):
+    print(f"Downloading model {model_file} from repository {repo_id}...")
+    local_path = hf_hub_download(repo_id=repo_id, filename=model_file)
+    print(f"Model downloaded to: {local_path}")
+    return Llama(model_path=local_path, n_ctx=2048, n_threads=8)
+
+# Generate a response using the specified model and prompt
+def generate_response(model, prompt):
+    response = model(prompt, max_tokens=512, temperature=0.5)
+    return response["choices"][0]["text"]
+
+# Evaluate responses using the LoRA evaluation model
+def evaluate_responses(prompt, repo_a, model_a, repo_b, model_b, evaluation_criteria):
+    if len(evaluation_criteria) > 3:
+        return "Error: Please select up to 3 evaluation criteria only."
+
+    # Load models
+    model_a_instance = load_user_model(repo_a, model_a)
+    model_b_instance = load_user_model(repo_b, model_b)
+    
+    # Generate responses
+    response_a = generate_response(model_a_instance, prompt)
+    response_b = generate_response(model_b_instance, prompt)
+    
+    # Display generated responses
+    print(f"Response A: {response_a}")
+    print(f"Response B: {response_b}")
+    
+    # Format the evaluation prompt
+    criteria_list = ", ".join(evaluation_criteria)
+    evaluation_prompt = f"""
+Prompt: {prompt}
+
+Response A: {response_a}
+Response B: {response_b}
+
+Evaluation Criteria: {criteria_list}
+
+Please evaluate the responses based on the selected criteria. For each criterion, rate both responses on a scale from 1 to 4 and provide a justification. Finally, declare the winner (or 'draw' if they are equal).
+"""
+    # Use the LoRA model to evaluate the responses
+    evaluation_response = lora_model.create_completion(
+        prompt=evaluation_prompt,
+        max_tokens=512,
+        temperature=0.5
+    )
+    evaluation_results = evaluation_response["choices"][0]["text"]
+    
+    # Combine results for display
+    final_output = f"""
+Evaluation Results:\n{evaluation_results}
+"""
+    return final_output, response_a, response_b
+
+# Load the LoRA evaluation model
+def load_lora_model():
+    repo_id = "KolumbusLindh/LoRA-4100"
+    model_file = "unsloth.F16.gguf"
+    print(f"Downloading LoRA evaluation model from repository {repo_id}...")
+    local_path = hf_hub_download(repo_id=repo_id, filename=model_file)
+    print(f"LoRA evaluation model downloaded to: {local_path}")
+    return Llama(model_path=local_path, n_ctx=2048, n_threads=8)
+
+lora_model = load_lora_model()
+print("LoRA evaluation model loaded successfully!")
+
+# Gradio interface
+with gr.Blocks(title="LLM as a Judge") as demo:
+    gr.Markdown("## LLM as a Judge 🧐")
+    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")
+    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")
+
+    # Prompt and criteria inputs
+    prompt_input = gr.Textbox(label="Enter Prompt", placeholder="Enter the prompt here...", lines=3)
+    criteria_dropdown = gr.CheckboxGroup(
+        label="Select Up to 3 Evaluation Criteria",
+        choices=["Clarity", "Completeness", "Accuracy", "Relevance", "User-Friendliness", "Depth", "Creativity"]
+    )
+
+    # Button and outputs
+    evaluate_button = gr.Button("Evaluate Models")
+
+    with gr.Row():
+        with gr.Column():
+            response_a = gr.Textbox(
+                label="Response A",
+                placeholder="The response for Model A will appear here...",
+                lines=20,
+                interactive=False
+            )
+
+        with gr.Column():
+            response_b = gr.Textbox(
+                label="Response B",
+                placeholder="The response for Model B will appear here...",
+                lines=20,
+                interactive=False
+            )
+
+    evaluation_output = gr.Textbox(
+        label="Evaluation Results",
+        placeholder="The evaluation results will appear here...",
+        lines=20,
+        interactive=False
+    )
+
+    # Link evaluation function
+    evaluate_button.click(
+        fn=evaluate_responses,
+        inputs=[prompt_input, repo_a_input, model_a_input, repo_b_input, model_b_input, criteria_dropdown],
+        outputs=[evaluation_output, response_a, response_b]
+    )
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+huggingface_hub==0.25.2
+gradio
+llama-cpp-python