prompter: 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: 1. For your specific purpose, a Transformer-based language model such as BERT, GPT-3, or RoBERTa might be best suited. These models have achieved state-of-the-art results on various natural language processing tasks, including reading comprehension and text classification, and have been pre-trained on large amounts of text data. You should also consider the size of the model and its fine-tuning capabilities.

2. A good place to start looking for pre-trained language models is Hugging Face's model hub (https://huggingface.co/models). You can also find models on the websites of major AI research organizations such as OpenAI and Google AI. A search query such as "pre-trained language models for NLP" or "pre-trained transformer models" can also provide useful results.

3. Some options for you to consider include:

BERT: BERT (Bidirectional Encoder Representations from Transformers) is a popular pre-trained language model developed by Google. It has been fine-tuned on a wide range of NLP tasks and has achieved state-of-the-art results on many of them. BERT's advantages include its bidirectional nature and its ability to handle a wide range of NLP tasks, but its size may make fine-tuning on smaller datasets more challenging.

GPT-3: GPT-3 (Generative Pre-trained Transformer 2) is another pre-trained language model developed by OpenAI. It is one of the largest language models available, with over 1.5 billion parameters. GPT-2 has been fine-tuned on a variety of NLP tasks, but its large size may make fine-tuning on smaller datasets more challenging.

RoBERTa: RoBERTa (Robustly Optimized BERT Pretraining Approach) is a variant of BERT developed by Facebook AI. It is designed to outperform BERT on a wide range of NLP tasks and has been pre-trained on a much larger corpus of text data. RoBERTa's advantages include its optimized pre-training procedure and its ability to handle a wide range of NLP tasks, but its large size may make fine-tuning on smaller datasets more challenging.

These are just a few options to consider, and you may find other models that are better suited to your specific needs. It's important to thoroughly evaluate the models' capabilities and limitations, as well as the availability of pre-trained models and fine-tuning resources, before making a final decision.

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.