user: For my machine learning research project I want to fine-tune a large language model (LLM) for a specific reinforcement learning task. In this task an agent is a LLM augmented by execution environment (e.g. Python REPL). The agent interacts with a formal verifier. The goal of the agent is to solve an engineering task. I want to start with a pre-trained model satisfying the following conditions:
* The model can be efficiently fine-tuned using A100 Nvidia GPUs in under 100 GPU-hours;
* Trained on academic papers (e.g. arxiv) and code (e.g. Github);
* Publicly available.

Please, do the following:
1. What kind of model is best suited for my purpose? Please, don't repeat my conditions; instead give an advise on what else I should be looking at when picking a model.
2. Describe how to find such models, e.g. provide a reference to a website with a list of pre-trained LLM models or a good search query to find such a list.
3. Provide a few (3..5) options for me to use, listing advantages and disadvantages of each for my specific purpose.

assistant: Model size: A smaller model will be faster to fine-tune, but may not have the capacity to solve more complex engineering tasks. On the other hand, a larger model will have more capacity, but may require more computational resources to fine-tune.
Pretraining task: The pretraining task should be similar to the fine-tuning task in terms of the type of data and the type of tasks the model will be used for. For example, if the fine-tuning task involves solving engineering problems, a model that was pretrained on a corpus of engineering texts may perform better.
Performance on benchmark datasets: You should look at the performance of the model on benchmark datasets relevant to your fine-tuning task, such as the GLUE benchmark for language understanding.
You can find pre-trained models by searching for "large language models pre-trained" on websites such as OpenAI, Hugging Face, or Google's TensorFlow Research Cloud. You can also look for benchmark results on websites such as Papers with Code, which has a section on "State of the Art Language Models".

Here are a few options for you to consider for your specific purpose:

OpenAI GPT-3: One of the largest language models currently available, with 175 billion parameters. It has been trained on a diverse range of internet texts and has shown strong performance on a variety of benchmark datasets. Advantages include its large size and diverse training data, while a disadvantage is the high computational cost required to fine-tune it.

BERT: A transformer-based model that has been pretrained on large amounts of text data. It has been shown to perform well on a variety of benchmark datasets and is relatively fast to fine-tune. Advantages include its relatively small size and fast fine-tuning, while a disadvantage is its limited capacity compared to larger models such as GPT-3.

RoBERTa: An improvement over BERT, with a larger model size and more extensive pretraining. It has been shown to perform better than BERT on benchmark datasets and is a good option for those who want a more powerful model but still want to keep the computational cost of fine-tuning reasonable. Advantages include its improved performance compared to BERT, while a disadvantage is its larger size compared to BERT, which may increase the computational cost of fine-tuning.