HyperTune is an advanced tool for optimizing and analyzing text generation using OpenAI's GPT models. It explores various hyperparameter combinations to produce high-quality responses to given prompts, and provides comprehensive analysis of the results.

Features

• Generate multiple responses to a given prompt using different hyperparameter settings
• Score responses based on coherence, relevance, and complexity
• Analyze common themes and unique insights across responses
• Visualize the impact of hyperparameters on response quality
• Provide detailed explanations of scoring and recommendations for further tuning

HyperTune uses a combination of natural language processing techniques and machine learning to generate and analyze text responses:

1. It generates multiple responses using OpenAI's GPT model with varying hyperparameters.

    def generate(self):
        results = []
        for _ in range(self.iterations):
            temperature = round(random.uniform(0.1, 1.0), 2)
            top_p = round(random.uniform(0.1, 1.0), 2)
            frequency_penalty = round(random.uniform(0.0, 2.0), 2)
            presence_penalty = round(random.uniform(0.0, 2.0), 2)

            response = client.chat.completions.create(model="gpt-4o",
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": self.prompt}
            ],
            temperature=temperature,
            top_p=top_p,
            frequency_penalty=frequency_penalty,
            presence_penalty=presence_penalty)
            results.append({
                'text': response.choices[0].message.content,
                'hyperparameters': {
                    'temperature': temperature,
                    'top_p': top_p,
                    'frequency_penalty': frequency_penalty,
                    'presence_penalty': presence_penalty
                }
            })
        return results

2. Each response is scored based on coherence, relevance to the prompt, and language complexity.
3. The tool then analyzes the responses collectively to identify common themes, unique insights, and the impact of different hyperparameters.
4. Finally, it provides a comprehensive report with visualizations to help understand the results.