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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

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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

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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, beta2=0.95, max_norm=None, min_lr=6e-05)}Seed set to 1337Number of trainable parameters: 12,226,560Number of non-trainable parameters: 2,779,683,840The longest sequence length in the train data is 512, the model's maximum sequence length is 512 and context length is 2048Validating ...Recommend a movie for me to watch during the weekend and explain the reason.Below is an instruction that describes a task. Write a response that appropriately completes the request.### Instruction:Recommend a movie for me to watch during the weekend and explain the reason.### Response:I recommend you watch "Parasite" because it's a critically acclaimed movie that won multiple awards, including the Academy Award for Best Picture. It's a thought-provoking and suspenseful film that will keep you on the edge of your seat. The movie also tackles social and economic inequalities, making it a must-watch for anyone interested in meaningful storytelling./home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MeanMetric was called before the ``update`` method which may lead to errors, as metric states have not yet been updated. warnings.warn(*args, **kwargs) # noqa: B028Missing logger folder: out/finetune/lora-phi-2/logs/csvEpoch 1 | iter 1 step 0 | loss train: 1.646, val: n/a | iter time: 820.31 msEpoch 1 | iter 2 step 1 | loss train: 1.660, val: n/a | iter time: 548.72 ms (step)Epoch 1 | iter 3 step 1 | loss train: 1.687, val: n/a | iter time: 300.07 msEpoch 1 | iter 4 step 2 | loss train: 1.597, val: n/a | iter time: 595.27 ms (step)Epoch 1 | iter 5 step 2 | loss train: 1.640, val: n/a | iter time: 260.75 msEpoch 1 | iter 6 step 3 | loss train: 1.703, val: n/a | iter time: 568.22 ms (step)Epoch 1 | iter 7 step 3 | loss train: 1.678, val: n/a | iter time: 511.70 msEpoch 1 | iter 8 step 4 | loss train: 1.741, val: n/a | iter time: 514.14 ms (step)Epoch 1 | iter 9 step 4 | loss train: 1.689, val: n/a | iter time: 423.59 msEpoch 1 | iter 10 step 5 | loss train: 1.524, val: n/a | iter time: 603.03 ms (step)Training time: 11.20sMemory used: 13.90 GBSaving LoRA weights to 'out/finetune/lora-phi-2/final/lit_model.pth.lora'Saved merged weights to 'out/finetune/lora-phi-2/final/lit_model.pth'Notice that the LoRA script saves both the LoRA weights ('out/finetune/lora-phi-2/final/lit_model.pth.lora') and the LoRA weight merged back into the original model ('out/finetune/lora-phi-2/final/lit_model.pth') for convenience. This allows us to

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Target domain data. This process involves adjusting the model parameters using a smaller dataset relevant to the desired domain, which enables the model to learn domain-specific knowledge and vocabulary.However, as LLMs are "large," updating multiple layers in a transformer model can be very expensive, so researchers started developing parameter-efficient alternatives.In this article, we discussed several parameter-efficient alternatives to the conventional LLM finetuning mechanism. In particular, we discussed how to insert and finetune additional adapter layers to improve the predictive performance of an LLM compared to training the original model parameters.Below are additional experiments where I implemented the adapter method and ran a comparison to finetune a DistilBERT model for sentiment classification:finetuning only the last two layers as a performance baseline;inserting and finetuning adapter layers;finetuning all layers of the original model;inserting adapter layers and finetuning all layers as a control experiment.All code examples are available here on GitHub. As a thanks to those who supported the newsletter in the previous months, I included a bonus section below discussing the code examples. Thanks again for your support!First, let's establish a performance baseline by only finetuning the last layers of a DistilBERT model on a movie review dataset. Here, we will only look at the relevant lines of code, omitting the non-finetuning specific code for brevity. However, as mentioned above, the full code examples are available here.First, after loading the pretrained DistilBERT model, let's look at the architecture:For this performance baseline, we only finetune the last two layers, which comprise 592,130 parameters. The simplest way to do that is to freeze all parameters and then unfreeze the last two layers via the code below:# Freeze all layersfor param in model.parameters(): param.requires_grad = False # Unfreeze the two output layersfor param in model.pre_classifier.parameters(): param.requires_grad = Truefor param in model.classifier.parameters(): param.requires_grad = TrueThen, after training this model for 3 epochs, we get the following results:Training time: 2.89 minTraining accuracy: 86.7%Validation accuracy: 87.2%Test accuracy: 86.4%Next, let's add the adapter layers to the model. Notice that DistilBERT has 6 transformer blocks. As discussed earlier, the adapter method inserts 2 adapter modules into each of the 6 transformer

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Beta2=0.95, max_norm=None, min_lr=6e-05)}Seed set to 1337Number of trainable parameters: 12,226,560Number of non-trainable parameters: 2,779,683,840The longest sequence length in the train data is 512, the model's maximum sequence length is 512 and context length is 2048Validating ...Recommend a movie for me to watch during the weekend and explain the reason.Below is an instruction that describes a task. Write a response that appropriately completes the request.### Instruction:Recommend a movie for me to watch during the weekend and explain the reason.### Response:I recommend you watch "Parasite" because it's a critically acclaimed movie that won multiple awards, including the Academy Award for Best Picture. It's a thought-provoking and suspenseful film that will keep you on the edge of your seat. The movie also tackles social and economic inequalities, making it a must-watch for anyone interested in meaningful storytelling./home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MeanMetric was called before the ``update`` method which may lead to errors, as metric states have not yet been updated. warnings.warn(*args, **kwargs) # noqa: B028Missing logger folder: out/finetune/lora-phi-2/logs/csvEpoch 1 | iter 1 step 0 | loss train: 1.646, val: n/a | iter time: 820.31 msEpoch 1 | iter 2 step 1 | loss train: 1.660, val: n/a | iter time: 548.72 ms (step)Epoch 1 | iter 3 step 1 | loss train: 1.687, val: n/a | iter time: 300.07 msEpoch 1 | iter 4 step 2 | loss train: 1.597, val: n/a | iter time: 595.27 ms (step)Epoch 1 | iter 5 step 2 | loss train: 1.640, val: n/a | iter time: 260.75 msEpoch 1 | iter 6 step 3 | loss train: 1.703, val: n/a | iter time: 568.22 ms (step)Epoch 1 | iter 7 step 3 | loss train: 1.678, val: n/a | iter time: 511.70 msEpoch 1 | iter 8 step 4 | loss train: 1.741, val: n/a | iter time: 514.14 ms (step)Epoch 1 | iter 9 step 4 | loss train: 1.689, val: n/a | iter time: 423.59 msEpoch 1 | iter 10 step 5 | loss train: 1.524, val: n/a | iter time: 603.03 ms (step)Training time: 11.20sMemory used: 13.90 GBSaving LoRA weights to 'out/finetune/lora-phi-2/final/lit_model.pth.lora'Saved merged weights to 'out/finetune/lora-phi-2/final/lit_model.pth'">{'checkpoint_dir': PosixPath('checkpoints/microsoft/phi-2'), # TODO 'data': Alpaca2k(mask_prompt=False, val_split_fraction=0.03847, prompt_style=, 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':. Download DJ Finetune - Party With Finetune Rooftop Mixtape Free download Finetune Finetune for Mac OS X. Finetune is a professional way to fix your music files.

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Blocks, as shown in the figure below:Each adapter module consists of 2 fully connected layers with a nonlinear activation in-between. In code, we can define a make_adapter function that creates such an adapter module as follows:def make_adapter(in_dim, bottleneck_dim, out_dim): adapter_layers = torch.nn.Sequential( torch.nn.Linear(in_dim, bottleneck_dim), torch.nn.GELU(), torch.nn.Linear(bottleneck_dim, out_dim), ) return adapter_layersThen, we can use the make_adapter function to insert the adapter layers into the 6 transformer blocks, as shown below:total_size = 0bottleneck_size = 32 # hyperparameterfor block_idx in range(6): ################################################### # insert 1st adapter layer into transformer block ################################################### orig_layer_1 = model.distilbert.transformer.layer[block_idx].attention.out_lin adapter_layers_1 = make_adapter( in_dim=orig_layer_1.out_features, bottleneck_dim=bottleneck_size, out_dim=orig_layer_1.out_features) new_1 = torch.nn.Sequential(orig_layer_1, *adapter_layers_1) model.distilbert.transformer.layer[block_idx].attention.out_lin = new_1 total_size += count_parameters(adapter_layers_1) ################################################### # insert 2nd adapter layer into transformer block ################################################### orig_layer_2 = model.distilbert.transformer.layer[block_idx].ffn.lin2 adapter_layers_2 = make_adapter( in_dim=orig_layer_2.out_features, bottleneck_dim=bottleneck_size, out_dim=orig_layer_2.out_features) new_2 = torch.nn.Sequential(orig_layer_2, *adapter_layers_2) model.distilbert.transformer.layer[block_idx].ffn.lin2 = new_2 total_size += count_parameters(adapter_layers_2) print("Number of adapter parameters added:", total_size)Number of adapter parameters added: 599,424The modified DistilBERT architecture is shown in the figure below:Notice that using a bottleneck size of 32, we added 599,424 new parameters to the model. In comparison, the 2 fully connected layers we finetuned earlier have 592,130 parameters in total, which is approximately the same number of parameters to finetune. If we finetune this modified model, where all layers except the adapter layers are frozen, we get the following results:Training time: 5.69 minTraining accuracy: 90.0%Validation accuracy: 89.1%Test accuracy: 88.4%Now, for comparison, let's look at the results from finetuning all layers. For this, we are loading the DistilBERT model and training it as is (without freezing any layers).from transformers import AutoModelForSequenceClassificationmodel = AutoModelForSequenceClassification.from_pretrained( "distilbert-base-uncased", num_labels=2)def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad)num_param = count_parameters(model.pre_classifier) + count_parameters(model.classifier)print("Parameters in last 2 layers:", num_param)66955010The result from finetuning all 66.9 million parameters are as follows:Training time: 7.12 minTraining accuracy: 96.6%Validation accuracy: 92.9%Test accuracy: 93.0%Lastly, let's add a control experiment, where we train the model modified with the adapter layers in Section 2, but making all parameters trainable. That's 599,424 + 66,955,010 = 67,554,434 in total.Training time: 7.62 minTraining accuracy: 98.4%Validation accuracy: 91.5%Test accuracy: 91.1%Now that we gathered all the results via the experiments above, let's look at. Download DJ Finetune - Party With Finetune Rooftop Mixtape Free download Finetune Finetune for Mac OS X. Finetune is a professional way to fix your music files.

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