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Support prompts that require multiple input lines. More information and additional resourcestutorials/download_model_weights: A more comprehensive download tutorial, tips for GPU memory limitations, and moreFinetune LLMsLitGPT supports several methods of supervised instruction finetuning, which allows you to finetune models to follow instructions.Datasets for Instruction-finetuning are usually formatted in the following way:Alternatively, datasets for instruction finetuning can also contain an 'input' field:In an instruction-finetuning context, "full" finetuning means updating all model parameters as opposed to only a subset. Adapter and LoRA (short for low-rank adaptation) are methods for parameter-efficient finetuning that only require updating a small fraction of the model weights.Parameter-efficient finetuning is much more resource-efficient and cheaper than full finetuning, and it often results in the same good performance on downstream tasks.In the following example, we will use LoRA for finetuning, which is one of the most popular LLM finetuning methods. (For more information on how LoRA works, please see Code LoRA from Scratch.)Before we start, we have to download a model as explained in the previous "Download pretrained model" section above:litgpt download microsoft/phi-2The LitGPT interface can be used via command line arguments and configuration files. We recommend starting with the configuration files from the config_hub and either modifying them directly or overriding specific settings via the command line. For example, we can use the following setting to train the downloaded 2.7B parameter microsoft/phi-2 model, where we set --max_steps 5 for a quick test run.If you have downloaded or cloned the LitGPT repository, you can provide the config file via a relative path:litgpt finetune_lora microsoft/phi-2\ --config config_hub/finetune/phi-2/lora.yaml \ --train.max_steps 5Alternatively, you can provide a URL:litgpt finetune_lora microsoft/phi-2\ --config \ --train.max_steps 5TipNote that the config file above will finetune the model on the Alpaca2k dataset on 1 GPU and save the resulting files in an out/finetune/lora-phi-2 directory. All of these settings can be changed via a respective command line argument or by changing the config file.To see more options, execute litgpt finetune_lora --help.Running the previous finetuning command will initiate the finetuning process, which should only take about a minute on a GPU due to the --train.max_steps 5 setting., ignore_index=-100, seed=42, num_workers=4, download_dir=PosixPath('data/alpaca2k')), 'devices': 1, 'eval': EvalArgs(interval=100, max_new_tokens=100, max_iters=100), 'logger_name': 'csv', 'lora_alpha': 16, 'lora_dropout': 0.05, 'lora_head': True, 'lora_key': True, 'lora_mlp': True, 'lora_projection': True, 'lora_query': True, 'lora_r': 8, 'lora_value': True, 'num_nodes': 1, 'out_dir': PosixPath('out/finetune/lora-phi-2'), 'precision': 'bf16-true', 'quantize': None, 'seed': 1337, 'train': TrainArgs(save_interval=800, log_interval=1, global_batch_size=8, micro_batch_size=4, lr_warmup_steps=10, epochs=1, max_tokens=None, max_steps=5, max_seq_length=512, tie_embeddings=None, learning_rate=0.0002, weight_decay=0.0, beta1=0.9,

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Use the finetuned model via the chat function directly:litgpt chat out/finetune/lora-phi-2/final/> Prompt: Why are LLMs so useful?>> Reply: LLMs are useful because they can be trained to perform various natural language tasks, such as language translation, text generation, and question-answering. They are also able to understand the context of the input data, which makes them particularly useful for tasks such as sentiment analysis and text summarization. Additionally, because LLMs can learn from large amounts of data, they are able to generalize well and perform well on new data.Time for inference: 2.15 sec total, 39.57 tokens/sec, 85 tokens>> Prompt:">Now chatting with phi-2.To exit, press 'Enter' on an empty prompt.Seed set to 1234>> Prompt: Why are LLMs so useful?>> Reply: LLMs are useful because they can be trained to perform various natural language tasks, such as language translation, text generation, and question-answering. They are also able to understand the context of the input data, which makes them particularly useful for tasks such as sentiment analysis and text summarization. Additionally, because LLMs can learn from large amounts of data, they are able to generalize well and perform well on new data.Time for inference: 2.15 sec total, 39.57 tokens/sec, 85 tokens>> Prompt:More information and additional resourcestutorials/prepare_dataset: A summary of all out-of-the-box supported datasets in LitGPT and utilities for preparing custom datasetstutorials/finetune: An overview of the different finetuning methods supported in LitGPTtutorials/finetune_full: A tutorial on full-parameter finetuningtutorials/finetune_lora: Options for parameter-efficient finetuning with LoRA and QLoRAtutorials/finetune_adapter: A description of the parameter-efficient Llama-Adapter methods supported in LitGPTtutorials/oom: Tips for dealing with out-of-memory (OOM) errorsconfig_hub/finetune: Pre-made config files for finetuning that work well out of the boxLLM inferenceTo use a downloaded or finetuned model for chat, you only need to provide the corresponding checkpoint directory containing the model and tokenizer files. For example, to chat with the phi-2 model from Microsoft, download it as follows, as described in the "Download pretrained model" section:litgpt download microsoft/phi-2model-00001-of-00002.safetensors: 100%|████████████████████████████████| 5.00G/5.00G [00:40Then, chat with the model using the following command:litgpt chat microsoft/phi-2> Prompt: What is the main difference between a large language model and a traditional search engine?>> Reply: A large language model uses deep learning algorithms to analyze and generate natural language, while a traditional search engine uses algorithms to retrieve information from web pages.Time for inference: 1.14 sec total, 26.26 tokens/sec, 30 tokens">Now chatting with phi-2.To exit, press 'Enter' on an empty prompt.Seed set to 1234>> Prompt: What is the

ProGen2 Finetuning 🦾 🧬 🧪Accompanying code for my bachelor thesis and paper.Ever wanted to finetune a generative protein language model on protein families of your choice? No? Well, now you can!UsageWe describe a simple workflow, in which we finetune the ProGen2-small (151M) model illustrate the usage of the provided python scripts.Install dependenciesFirst of all, we need to install the required dependencies. Use a virtual environment to avoid conflicts with the system-wide packages.cd srcpython3 -m venv venvsource venv/bin/activatepip3 install -r requirements.txtDownloading dataSelect a few families from the Pfam database, which you want to train the model on. Use their Pfam codes to download the data in FASTA format. The downloaded files will be saved into the downloads/ directory. This may take a while, depending on the size of the downloaded families.Example code to dowlnoad three, relatively small, protein families:python3 download_pfam.py PF00257 PF02680 PF12365 Preprocessing the dataBefore finetuning the model, we need to preprocess the data to include the special famliy tokens, and the 1 and 2 tokens at the beginning and end of sequence. We also remove the FASTA headers.We specify the paths to the downloaded FASTA files using the --input_files option.Optionally, we may define the names of output train and test data files in which the data will be stored. We can also specify the ratio of train and test data split (default is 0.8) and using a boolean flag --bidirectional we can save the sequences also in reverse, if we want to train a bidirectional model.python3 prepare_data.py \ --input_files