NLP: LoRA as an Adapter From paper Many applications in by Suvasis Mukherjee Apr, 2024

MixLoRA further extends its fine-tuning capabilities to encompass the attention layer. Previous studies, such as ST-MoE [28], have suggested that fine-tuning the attention layer can significantly improve performance. To enhance the fine-tuning process with MixLoRA, we integrate LoRA adapters into the attention layer of the dense model.

But smaller models on the other hand cannot generalize to multiple tasks, and we end up having multiple models for multiple tasks of multiple users. This is where PEFT techniques like LoRA come in, these techniques allow you to train large models much more efficiently compared to fully finetuning them. In this blog, we will walk through LoRA, QLoRA, and other popular techniques that emerged specifically from LoRA.

So for every multi-head attention and MLP sub-block in the transformer architecture, an adapter layer is added and its weights are updated according to the downstream task. Mistral [66] innovates with grouped-query attention and sliding window attention mechanisms, enhancing inference speed and efficiency, while outperforming larger models in reasoning, mathematics, and code generation tasks. About training tasks, ARC [34] challenges systems with science questions requiring deep reasoning, underlining the need for beyond surface-level understanding. BoolQ [35] explores the complexity of natural yes/no questions, demonstrating the importance of inferential reasoning. OpenBookQA [36] assesses understanding by combining scientific facts with common knowledge, emphasizing multi-hop reasoning and the integration of external knowledge. PIQA [37] introduces physical commonsense reasoning, highlighting the gap in current systems’ ability to understand and reason about the physical world.

This is accomplished by incorporating adaptation parameters to simulate domain-specific gradient updates while freezing pretrained model weights. Consequently, some work improves LLM’s cross-task generalization ability by introducing different LoRA modules or experts to process tokens from different tasks. Large Language Models (LLMs) have showcased exceptional performance across a wide array of Natural Language Processing (NLP) tasks. Fine-tuning techniques are commonly utilized to tailor pre-trained models to specific applications.

Final weights are calculated by adding the pre-trained weights with the fine-tuned weights and the model is ready to make inference on the domain-specific task. Double quantization is the process of quantizing the quantization constants used during the quantization process in the 4-bit NF quantization. This is not important, but can save 0.5 bits per parameter on average, as mentioned in the paper. This helps with the process because https://chat.openai.com/ QLoRA uses Block-wise k-bit Quantization, meaning instead of quantizing all the weights together, we create multiple chunks or blocks of weights which are then quantized independently. LoRA is the most popular and perhaps the most used PEFT technique, but was released back in 2021 in this paper. LoRA is more of an adapter approach, where it introduces new parameters into the model to train the model through these new parameters.

Essentially, it posits that not all elements of ( Δ W ) are equally important; instead, a smaller subset of these changes can effectively encapsulate the necessary adjustments. This repo contains the source code of the Python package loralib and several examples of how to integrate it with PyTorch models, such as those in Hugging Face. As with the script parameters, a walkthrough of the training script is provided in the Text-to-image training guide. Along with this, the authors show the need to use the LoRA on the normalization and the embedding layers too for this method to work properly. In the paper, researchers provide a very detailed comparison between QLoRA, LoRA, and Full Finetuning of a network. To do QLoRA finetuning with HuggingFace, you need to install both the BitsandBytes library and the PEFT library.

Instead of fine-tuning the entire model, LoRA focuses on a smaller, low-rank representation of the model, which requires fewer computational resources and less time to adapt. These models are trained on vast amounts of textual data, which allows them to effectively generate, understand, and manipulate human-like text. LLMs, such as OpenAI’s GPT-3 or Google’s BERT, have become the backbone of modern NLP applications, including chatbots, machine translation, sentiment analysis, and more. The reset_parameters method resets the parameters of the Linear layer, ensuring they are initialized properly. It initializes lora_A with zeros and lora_B with Kaiming uniform initialization, as per the default initialization for linear layers. The dataset preprocessing code and training loop are found in the main() function, and if you need to adapt the training script, this is where you’ll make your changes.

4 Analysis of Efficiency

In summary, the experiment suggests that increasing $r$ does not cover a more meaningful subspace, which suggests that a low-rank adaptation matrix is sufficient. Also, the paper evaluates the overlap of the subspaces learned by different rank $r$ and random seeds, based on the Grassmann distance. Furthermore, as the AI community becomes more conscious of the environmental impact of large-scale models, LoRA’s lower energy consumption will likely contribute to a more sustainable and eco-friendly approach to AI development.

This approach significantly reduces the computational resources, time, and energy required for model adaptation, making it more efficient and accessible compared to traditional fine-tuning methods. This involves updating the parameters of the reconstructed model on the task-specific dataset, similar to traditional fine-tuning methods. Fine-tuning is the process of adjusting the weights of a pre-trained model by continuing its training on a smaller, task-specific dataset. This allows the model to better adapt to the nuances of the target task, improving its accuracy and relevance.

PEFT methods have emerged as an efficient approach to fine-tune pretrained LLMs while significantly reducing the number of trainable parameters. These techniques balance computational efficiency and task performance, making it feasible to fine-tune even the largest LLMs without compromising on quality. Recently, instruction fine-tuning of large language models (LLMs) [1, 2, 3, 4, 5] for various downstream tasks has achieved impressive proficiency in Natural Language Processing (NLP) [6, 7, 8]. As the scale of parameters increases, LLMs have been demonstrated to be able to identify complex linguistic patterns, thereby enabling the emergence of powerful cross-task generalization capabilities [9]. The paradigm of instruction tuning leads to a trade-off between the computational resources required and the performance achieved on downstream tasks, which has been a valuable facet. The magic of LoRA lies in its efficiency in fine-tuning, which might seem paradoxical since the additional weights seem to double the parameter count.

All of the parameters and their descriptions are found in the parse_args() function. Default values are provided for most parameters that work pretty well, but you can also set your own values in the training command Chat GPT if you’d like. Solutions like Aporia Guardrails and fine-tuning techniques such as LoRA help address many challenges. But, LLM researchers are still figuring out the possibilities where an LLM can go wrong.

The first step in the LoRA process involves decomposing the pre-trained large language model. In the context of machine learning, low-rank approximation can be employed to compress large models, making them more efficient without sacrificing their predictive power. Low-rank approximation is a mathematical technique used to simplify complex matrices without losing a significant amount of information. By reducing the rank of a matrix, we can decrease its size, making it easier to manipulate and store.

Now, let’s take a deep dive into the technical understanding of how LoRA operates, what low rank and adaptation mean in LoRA, and how it updates trainable parameters. QA LoRA is another fine-tuning technique built on top of QLoRA, introduced in this paper. QALoRA was mainly released for finetuning diffusion models, but can easily be generalized for training any type of models, just like LoRA. Language Models like GPT-4 have become the de facto standard in the NLP industry for building products and applications. These models are capable of performing a plethora of tasks and can easily adapt to new tasks using Prompt Engineering Techniques.

The trick is in how the new params are introduced and merged back into the model, without increasing the total number of params in the model. Additionally, to get the most out of LoRA, practitioners such as Sebastian Raschka have provided thorough guides that detail optimal hyperparameter settings and strategies for utilizing these methods. LoRA provides one of the best and easiest ways to reduce LLM parameter counts and memory usage and increase the speed of fine-tuning and inference. The availability and ease-of-use of open source implementations, such as those provided by HuggingFace, allow for plug-and-play adaptations of LoRA and PEFT methods to any LLM.

LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models

As the low-rank representation is much smaller than the original model, this adaptation process is considerably faster and requires fewer computational resources than traditional fine-tuning methods. Another approach is prefix tuning, which automates prompt engineering by adding input vectors initialized randomly without specific word representations. The vectors, known as prefixes, are adjusted through backpropagation until the model produces the correct output. However, prefix tuning reduces the effective input size and is challenging to optimize due to the uncertainty in choosing the number of trainable parameters. Diffusers uses ~peft.LoraConfig from the PEFT library to set up the parameters of the LoRA adapter such as the rank, alpha, and which modules to insert the LoRA weights into. The adapter is added to the UNet, and only the LoRA layers are filtered for optimization in lora_layers.

LoRA aims to reduce the number of trainable parameters and the computational burden while maintaining or improving the model’s performance on downstream tasks. In the exciting world of natural language processing, large-scale pre-trained language models (LLMs) have revolutionized the field. However, fine-tuning such enormous models on specific tasks has proven challenging due to the high computational costs and storage requirements. Researchers have delved into Parameter-Efficient Fine-Tuning (PEFT) techniques to achieve high task performance with fewer trainable parameters to address this. LoRA reduces the number of trainable parameters by learning pairs of rank-decompostion matrices while freezing the original weights. This vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment all without introducing inference latency.

LoRA also outperforms several other adaptation methods including adapter, prefix-tuning, and fine-tuning. The Mixture-of-LoRA-Experts, abbreviated as MixLoRA, is a parameter-efficient fine-tuning (PEFT) method used for creating sparse mixture-of-experts models through fine-tuning on dense models such as LLaMA and Mistral. MixLoRA achieves the MoE structure with lower overhead by inserting multiple different LoRA adapters and a top-k router on top of the FFN block of dense models. Furthermore, as analyzed by ST-MoE [28], MixLoRA enhances model performance by utilizing independently configurable attention layer LoRA adapters. Building upon this, we further design a resource-efficient framework for MixLoRA and other LoRA-MoE methods based on m-LoRA [], which can be employed for parallel training multiple LoRA experts in the MoE layer. Then, by considering the costs of fine-tuning, our framework also supports multiple MixLoRA training at the same time, thus reducing 41% of GPU memory consumption and 17% of latency during the training process.

We

initialize two dense layers, A and B, of shapes n x rank, and rank x n,

respectively. In this new method, we freeze the original weights of the model and don’t modify them during the fine-tuning process. Instead, we apply the modifications to a separate set of weights and we add their new values to the original parameters. It then predicts the next tokens and compares its output with the ground truth. By doing this over and over, the LLM becomes fine-tuned to the downstream task. Large language models (LLM) are known for being expensive to train, fine-tune, and run.

Lora_A is initialized using Kaiming uniform initialization, and lora_B is initialized to zeros. In this equation, ( W ) remains frozen (i.e., it is not updated during training). The matrices ( B ) and ( A ) are of lower dimensionality, with their product ( BA ) representing a low-rank approximation of ( Δ W ).

Parameter-Efficient Transfer Learning for NLP

Potential advancements in low-rank approximation techniques, decomposition methods, and domain-specific adaptation strategies will further enhance the performance and efficiency of LoRA-based language model adaptation. As the demand for advanced natural language processing capabilities continues to grow, the need for efficient and accessible adaptation methods for large language models becomes increasingly critical. The modification to the output of the layer is computed as A×B, where A and BB are learned during training, allowing adaptation with fewer parameters compared to modifying the entire weight matrix.

PEFT brings several practical benefits, such as reduced memory usage, storage cost, and inference latency. It allows multiple tasks to share the same pre-trained model, minimizing the need for maintaining independent instances. However, PEFT might introduce additional training time compared to traditional fine-tuning methods, and its performance could be sensitive to hyperparameter choices. LoRA is based on the idea that updates to the weights of the pre-trained

language model have a low “intrinsic rank” since pre-trained language models are

over-parametrized.

Low-rank adaptation involves determining the number of linearly independent columns within a matrix. If a column can be obtained by combining others in the matrix, it’s considered linearly dependent. Removing such columns reduces the matrix’s dimension without losing information since the information was already present in other columns.

During fine-tuning, the model’s parameters are adjusted to optimize its performance for the target task. Introduced in 2019, adapters are another popular LLM fine-tuning technique that adds only a few trainable parameters for a downstream task. They inject new lightweight modules or layers between the layers of the original pre-trained model.

MixLoRA and MixDoRA exhibit differentiated expert loadings within a multi-task learning framework. Table 3 presents the performance of MixLoRA and compares these results with outcomes obtained by employing LoRA and DoRA for fine-tuning. The results demonstrate that the language model with MixLoRA achieves commendable performance across all evaluation methods. Particularly noteworthy is that while DoRA outperforms LoRA in most evaluations, MixDoRA does not consistently exhibit superior performance compared to MixLoRA, especially with larger models (e.g., LLaMA 13B). To evaluate the effectiveness of MixLoRA, we conduct comprehensive experiments on a variety of supervised fine-tuning datasets in the area of common sense reasoning.

For other MoE based methods, MoRAL [10]addresses the challenge of adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners. LoRAMoE [12] integrates LoRAs using a router network to alleviate world knowledge forgetting. PESC [11] transitions dense models to sparse models using a Mixture-of-Experts (MoE) architecture, reducing computational costs and GPU memory requirements. MoE-LoRA [14] propose a novel parameter-efficient MoE method with Layer-wise Expert Allocation (MoLA) for Transformer-based models. To substantially reduce the computational and memory resources required by traditional fine-tuning processes, Parameter-Efficient Fine-Tuning (PEFT) methodologies have emerged [18, 19, 20, 21, 22, 23]. Among them, Low-Rank Adaption (LoRA) [23], a popular PEFT method, provides comparable performance to complete fine-tuning on various downstream tasks, and it requires less computational expense.

As said, because LLMs have become so large and complicated to serve and handle, it’s almost impossible to train specialized models for users. If you have multiple users, you can either take on the complicated task of finetuning a new model every time a new user comes in, or you can just finetune the same model on the new data for the new user. Training the same model for all users is much easier, but then the model accuracy drops significantly. Open-source LLMs such as LLaMA, Pythia, and MPT-7B are foundation models that have been pre-trained on hundreds of billions of words. Developers and machine learning engineers can download the model with the pre-trained weights and fine-tune it for downstream tasks such as instruction following. In the last part of Compression of NN[1], we delved into methods for reducing the memory footprint of large ML models, in this part let’s look into how to fine-tune these large models in consumer grade resources with less memory.

By applying LoRA to large language models, developers can create more efficient summarization systems that generate coherent and informative summaries, even in specialized fields or for niche topics. LoRA can be effectively used to adapt large language models for conversational AI applications, such as chatbots and virtual assistants. LoRA’s efficiency in adapting large language models ultimately contributes to enhanced accessibility of these powerful tools.

How does LoRA contribute to the democratization of AI?

Therefore, LoRA significantly reduces the number of fine-tuning parameters, $dk$ into $dr + rk$. If the rank $r$ of LoRA converges to the rank of $W_0$, the expressiveness of full fine-tuning will be recovered. LoRA is one of several techniques that can help reduce the costs of training open-source LLMs. It has more technical details and nuances, such as which types of weights it applies to and the hyperparameters that it has.

Fine-tuning large pre-trained models is computationally challenging, often involving adjustment of millions of parameters. This traditional fine-tuning approach, while effective, demands substantial computational resources and time, posing a bottleneck for adapting these models to specific tasks. LoRA presented an effective solution to this problem by decomposing the update matrix during finetuing. Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs). While promising, it raises significant challenges due to the heterogeneous resources and data distributions of clients. FlexLoRA allows for dynamic adjustment of local LoRA ranks, fostering the development of a global model imbued with broader, less task-specific knowledge.

This helps in more efficient training as there is no need for the conversion step to update the models during the backpropagation process. As the number of trainable parameters decreases, you have to spend less time on training. With less trainable parameters, you can train models much faster, and as a result test models much faster too.

Consider a common pre-trained LLM built on top of the transformer architecture containing multi-head attention and multi-layer perceptron (MLP) layers. With LoRA, we freeze all the pre-trained model weights and introduce a small set of weights into each dense layer of the transformer. The difference between QLoRA and QALoRA is that QALoRA is quantization aware meaning the weights of the LoRA adapters are also quantized along with the weights of the model during the finetuning process.

By leveraging low-rank adaptation, LoRA minimizes the energy requirements, making the adaptation process more sustainable and environmentally friendly. The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn’t cover every aspect of the script in detail. If you’re interested in learning more, feel free to read through the script and let us know if you have any questions or concerns. LoRA-powered LLMs can help develop specialized learning tools and tailored study materials across subjects and class levels.

ARC [34] evaluates LLM’s capabilities using science-based multiple-choice questions. BoolQ [35] features real-world Google queries with binary answers derived from Wikipedia excerpts. OpenBookQA [36] and PIQA [37] present multiple-choice challenges that demand comprehension and logic applied to selected scientific information or physical interactions, respectively. For these datasets, we only use the accuracy metric to quantify their performance. LoRA contributes to the democratization of AI by making the adaptation of large language models more accessible, efficient, and cost-effective. This allows developers and researchers to iterate more quickly, test multiple adaptation scenarios, and deploy models in a more time-efficient manner.

The authors suggest applying the LoRA adapters on all the linear transformer blocks along with the query, key, and value layers. This error is why QLoRA is more of a finetuning mechanism than a standalone quantization strategy. When fine-tuning with QLoRA we use the LoRA tuning mechanism of creating 2 smaller weight update matrices and then using them to update the weights of the neural network.

With more time on your hands, you can spend it on testing different models, different datasets different techniques, and whatnot. The authors evaluate the downstream task performance of LoRA on various LLMs including RoBERTa, DeBERTa, GPT-2 and GPT-3. And LoRA successfully matches lora nlp or exceeds the full fine-tuning baselines in most of cases. Number of trainable parameters of several adaptation methods on GPT-3 with 175B parameters. One can observe that LoRA exhibits better scalability and task performance than other adaptation methods and full fine-tuning.

You can foun additiona information about ai customer service and artificial intelligence and NLP. As these matrices are much smaller, this process uses fewer parameters and as a result much fewer computation resources. This also results in smaller checkpoints as you don’t have to store the whole model, but just the smaller matrices. PEFT finetuning on the other hand takes the best of both worlds and lets you build small adapters that you can pair with models and get customized results. These adapters are very small, 6MB-8MB, and you only need to apply these adapters to the large model, which is much faster to do in a production environment. Also, with more time you can train your models for much longer periods of time leading to a much lower loss, along with an increased batch size as PEFT techniques are heavily optimized for memory usage.

Training language models to follow instructions with human feedback

Hence, future problems with LLMs will be solved using a variety of different innovative tools and techniques. Practitioners must actively try out new techniques to decide which one suits their requirements. A simpler approach is to integrate Aporia Guardrails with your LLM applications.

LoRA stands out because it enables the sharing of most model parameters across different tasks, allowing for quick task switching while maintaining high model quality. This approach does not negatively impact the input sequence length or add inference latency. The table below illustrates LoRA’s fine-tuning capabilities on the GPT-3 175B model for several benchmarks. It either outperforms or gives comparable outcomes to other fine-tuning techniques while using a fraction of trainable parameters.

However, the effectiveness and efficiency of LoRA might vary depending on the specific model architecture and the target task or domain. In theory, LoRA can be applied to any large language model, as it is a general technique for model adaptation. As the low-rank representation is much smaller than the original model, the time required to adapt the model to a specific task or domain is significantly reduced.

LoRA suggests decomposing high-dimensional weight matrices into two smaller matrices, A and B, resulting in computational efficiency. By representing the weights as the product of A and B, we reduce the number of parameters that need tuning. In traditional fine-tuning, we modify a pre-trained neural network’s weights to adapt to a new task. This adjustment involves altering the original weight matrix ( W ) of the network.

It usually requires retraining all of the model parameters – known as full fine-tuning. Once retained, the parameters need to be stored, making it a critical storage and deployment challenge. To implement LoRA finetuning with HuggingFace, you need to use the PEFT library to inject the LoRA adapters into the model and use them as the update matrices. However, since the downstream weights only occupy a fraction of the original weights (sometimes down to a thousandth), then you might want to keep them separate. Furthermore, we find that MixLoRA shows a higher variability in expert loads, implying a degree of specialization where certain experts may be tasked more heavily with particular types of problems or tasks. This can be advantageous if those experts are particularly adept at certain tasks, leading to higher efficiency or accuracy in cross-task generalization.

To avoid the uneven distribution of tokens among experts, some methods also consider incorporating a load balance loss to mitigate this issue (III) [10, 11]. These three points are considered the main distinctions in existing LoRA-MoE methods. We observe these methods that only construct MoE on the FFN layers achieve performance comparable to or surpass those that construct MoE across the entire transformer block. An important reason is that the FFN encapsulates the transformer block’s knowledge effectively [27]. Furthermore, studies such as ST-MoE [28] suggest that fine-tuning the attention layer can bring significant benefits to MoE models.

This is why they can be trained much faster and at a fraction of the cost of doing full fine-tuning. At inference time, the output of LoRA is added to the pre-trained parameters to calculate the final values. However, recently released open-source LLMs have proven that you don’t need very large models to compete with the state of the art. Researchers have trained LLMs with a few billion parameters to perform at a level that is comparable to very large models. The success of open-source large language models has sparked interest and growing activity in the field. And they hypophyse that the act of pre-training is just lowering the intrinsic dimension of the NLP task.

MixLoRA improves performance over the baseline by 11.3% with a modest increase in latency (54.6%) and memory (1.4%). MixDoRA further improves performance by 12.7% but at a high cost in latency (367.1%) and a similar memory increase (1.7%). Benefiting from the m-LoRA framework, we can train multiple MixLoRAs and MixDoRAs at the same time. When scaled (x2), both methods maintain performance gains but with a significant reduction in memory efficiency. MixLoRA exhibits a more balanced trade-off, while MixDoRA offers higher performance at the expense of much greater latency.

While this method is computationally efficient due to the low number of parameters in the adapter layers, it introduces latency during inference as these layers must be processed sequentially. LoRA (Low-Rank Adaptation) allows for the fine-tuning of large models at a fraction of the usual cost. Rather than adjusting all the model weights during fine-tuning, LoRA freezes the original weights of the model. It then introduces a separate set of weights that, after fine-tuning, effectively represent the necessary modifications to the pretrained parameters to optimize the model for a specific task. LoRA’s approach to decomposing ( Δ W ) into a product of lower rank matrices effectively balances the need to adapt large pre-trained models to new tasks while maintaining computational efficiency. The intrinsic rank concept is key to this balance, ensuring that the essence of the model’s learning capability is preserved with significantly fewer parameters.

Given that the pretrained dense model weights in MixLoRA remain frozen, it becomes feasible to maintain two or more MixLoRA models that share the same pretrained dense model weights. Figure 2 illustrates the concept of m-LoRA[32], which leverages Batch Fusion to enhance the training computation efficiency of multiple LoRA models. We have built MixLoRA upon the m-LoRA framework, enabling the fine-tuning of multiple Mixture-of-Experts models on a single 24GB consumer-grade GPU. In experiments, we observed that m-LoRA with MoE optimization can conserve 41% of GPU memory and reduce latency per token by 17% when training two MixLoRA models simultaneously. During the adaptation process, the original weight matrix W0​ remains unchanged (frozen), and only the matrices A and B are updated. These matrices capture the essence of how the network needs to be modified to perform well on the new task.

Mathematically, you can think of this as projecting the weight matrix into two low-dimensional subspaces (where the learning now occurs) and then reconsolidating it in the original space. LoRA can be applied to any and all weights in the model, including the attention weights. Data scientists can use a number of approaches to select which weight matrices to update. For example, say you’re hosting an LLM that several clients use for different applications. Each client wants to fine-tune the model with their own specific datasets and for their own applications. Instead of creating a separate fine-tuned version of the model for each client, you can use LoRA to create a set of downstream weights for each client or application.

At inference time, you load the base model and the LoRA weights of each client to make the final compute. You will have a slight performance hit, but the gains in storage will be immense. Normally, interacting with models like ChatGPT, LLaMA 2, Claude, or Falcon involves crafting the right prompts and providing relevant examples, which can be effective but limited to the model’s pre-training knowledge. To fir these models, for example, transforming them into a banking chatbot with financial expertise or a medical chatbot that understands healthcare — LoRA enables fine-tuning on smaller, specialized datasets.

By working with a low-rank representation of the model, the number of parameters that need to be updated during the adaptation process is substantially decreased. This is achieved by reversing the decomposition process, essentially “re-assembling” the weight matrices of the model from the adapted low-rank components. This decomposition results in a set of smaller matrices, which together form a low-rank approximation of the original model. The goal is to capture the most relevant information from the full model while significantly reducing its size and complexity.

• It adds a middleware security and protection layer on top of your LLM to check the integrity of its responses and make corrections in real-time.
• To implement LoRA finetuning with HuggingFace, you need to use the PEFT library to inject the LoRA adapters into the model and use them as the update matrices.
• While promising, it raises significant challenges due to the heterogeneous resources and data distributions of clients.
• Large Language Models (LLMs) like GPT-4, Llama-2, and Claude have shown tremendous capabilities for solving language tasks.
• The training script has many parameters to help you customize your training run.
• Now, let’s take a deep dive into the technical understanding of how LoRA operates, what low rank and adaptation mean in LoRA, and how it updates trainable parameters.

For instance, Med-PaLM and BloombergGPT are fine-tuned LLMs for medical and finance domains. Or Meta’s Llama-2 has multiple fine-tuned variants for coding and question-answering tasks. Low-Rank Adaptation or LoRA is a groundbreaking solution that addresses these problems. It gives a lot of control over an LLM’s potential to adapt to downstream tasks, taking into account compute, memory and storage efficiency as well as the model’s latency. LongLoRA is yet another variation of the LoRA finetuning technique, but this technique is specifically for training longer context models. This method creates chunks or groups of the tokens, in which the attention is calculated independently.