Lora peft

Lora peft

The models weight is from modelscope. You signed in with another tab or window. Our VB-LoRA achieves higher scores with significantly smaller number of stored parameters. 4. LoRa focuses on adding extra weights to the model while freezing This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune an image classification model. In Contribute to JL-er/RWKV-PEFT development by creating an account on GitHub. For example, it will automatically using the PEFT backend , and the lora layers I added will be ignored in the forward process. They go a step further to create two smaller dimension weight matrices to represent this difference. Now, what is the difference between PEFT and LoRa? PEFT is a method that employs various techniques, including LoRa, to fine-tune large language models efficiently. In the coming months, we'll be exploring more PEFT methods, such as (IA)3 and bottleneck adapters. Sep 4, 2023 · PEFT supports the widely-used Low-Rank Adaptation of Large Language Models (LoRA). - huggingface/peft Mar 18, 2024 · Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency. This means you can tune such large LLMs in Google Colab. You can improve the performance of this model by fine transformers > =4. py的llm_dict中，键为模型名，值为peft路径； 7开启 PEFT_SHARE_BASE_WEIGHTS=true环境变量，再执行python startup. If I understand your question correctly, you're asking why the ModulesToSaveWrapper, which is used when modules_to_save are set, creates a copy of the Saved searches Use saved searches to filter your results more quickly Parameter Efficient Fine-Tuning of LLM w/ multiple LoRA Adapters. We've released PEFT as an efficient way of tuning large LLMs on downstream tasks and domains, saving a lot of compute and storage while achieving comparable performance to full finetuning. However, this method was only evaluated on the FLAN T-5 models, which is an encoder-decoder model. Llama 2 Chat, which is optimized for dialogue, has shown similar performance to popular closed-source models like ChatGPT and PaLM. LoRa updates a weight matrix by learning a separate matrix which represents the updates from optimization. Prepare Training Data. A point to note is that we didn't try to sequeeze performance by playing around with input instruction templates, LoRA hyperparams and other training related hyperparams. In principle, such an approach can be more flexible than LoRA, but you need to be careful with. I now want to further fine tune the model without losing its original properties - in this case via instruction fine tuning / prefix tuning. The end goal of this example was to fine-tune a LLM to generate positive movie reviews in a memory constrained settting. For the bigscience/mt0-large model, you're only training 0. py Apr 18, 2024 · LoRA seem to converge faster than DoRA (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA) DoRA quality superior to LoRA especially in lower ranks : The difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example. as a standalone model. from_pretrained(config. Then, LoRA Dec 14, 2023 · This paper introduces a novel Parameter-Efficient Fine-Tuning (PEFT) framework for multi-modal, multi-task transfer learning with pre-trained language models. Saved searches Use saved searches to filter your results more quickly May 30, 2024 · Parameter-Efficient Fine-Tuning (PEFT) PEFT is a popular technique used to efficiently finetune large language models for use in various downstream tasks. First, redundant parameters are trimmed, then conflicting Feb 27, 2024 · Among the various PEFT techniques, we explored LoRA, a powerful method that leverages low-rank adaptations to achieve efficient fine-tuning. To load and use a PEFT adapter model from 🤗 Transformers, make sure the Hub repository or local directory contains an adapter_config. SWIFT web-ui is available both on Huggingface space and ModelScope studio , please feel free to try! 使用peft库，对chatGLM-6B/chatGLM2-6B实现4bit的QLoRA高效微调，并做lora model和base model的merge及4bit的量化（quantize）。 - shuxueslpi/chatGLM Oct 2, 2023 · 理論: LoRAとフルパラメータファインチューニングではどこが異なるのか? LoRAはどこを更新するか? 例えばtransformersのpeftライブラリの場合､llamaにおいてはattention 層のquery (q), value (v)のみをデフォルト設定で更新しているようです｡ We would like to show you a description here but the site won’t allow us. Recent state-of-the-art PEFT techniques 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. SWIFT has rich documentations for users, please check here . This drastically reduces the number of parameters that need to be fine-tuned. Full parameter fine-tuning is a method that fine-tunes all the parameters of all the layers of the pre-trained model. FlexLLM introduces a PEFT- Jul 27, 2023 · Parameter-Efficient Fine-tuning (PEFT) approaches, such as LoRA, KronA, LeTS, address these problems around computing and storage. In this fine-tuning process we are using PEFT LoRa which stands for Parameter Efficient Fine Tuning (PEFT) using Low-Rank Adaptation (LoRA) method. For example, take a look at the following LoraConfig for applying LoRA and PromptEncoderConfig for applying p-tuning (these configuration files are already JSON-serialized). Jul 31, 2023 · Two key PEFT methods are LoRA and Prompt Tuning. In PEFT, using LoRA is as easy as setting up a LoraConfig and wrapping it with get_peft_model () to create a trainable PeftModel. When finetuning with PEFT, the base model weights are frozen, and a few trainable adapter modules are injected into the model, resulting in a very small number (<< 1%) of trainble weights Dec 12, 2023 · Intrigued by its potential, we implemented and benchmarked the method ourselves to assess its effectiveness. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite hav-ing fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. However, existing PEFT methods pose challenges in hyperparameter selection, such as choosing the rank for LoRA or Adapter, or specifying the length of soft prompts. Even if I remove AttnProcsLayers, this example is not compatible with the newest diffusers version. Although the mathematics behind LoRA is intricate, PEFT helps us by simplifying the process of adapting LoRA to the pretrained Transformer model. LoRA achieves this reduction by adding low-rank “update matrices” to specific blocks of the model Jul 19, 2023 · Parameter-Efficient Fine-Tuning (PEFT) is a library for efficiently fine-tuning LLMs without touching all of the LLM’s parameters. Oct 23, 2023 · LoRA + Peft For this example, we will be fine-tuning Llama-2 7b on a GPU with 16GB of VRAM. We use a maximum sequence length of 32K for infer-ence and a sequence length of 4K for LoRA finetuning. 1 fsspec==2023. Finetune Pretrained Model. The create_peft_config() function in the prepared script run_clm. Specifically, for a linear layer with the input dimension d I and the output dimension d O, we represent its weight with Wd O×d I. May 2, 2023 · PEFT and LoRa PEFT is a method that employs various techniques, including LoRa, to efficiently fine-tune large language models. 9. 0 numpy tqdm 微调 地址: qwen2_sft/ft_qwen2 配置: qwen2_sft/ft_qwen2/config. py -a 8针对p-tuning和chatglm模型，需要对fastchat进行较大幅度的修改。 . PEFT, or Parameter-efficient Fine-tuning, is a natural language processing technique used to improve the performance of pre-trained language models on specific downstream tasks. 2 rouge==1. Our findings suggest that the low-rank updating mechanism may limit the ability of LLMs to effectively learn and memorize new knowledge. To address these challenges, we Jun 6, 2023 · Hey, first of all, modules_to_save is not really specific to LoRA but a more general mechanism to allow certain modules to be trained independent of the adapter methods (so full fine-tuning). research. In general, it can achieve the best performance but it is also the most resource-intensive and time consuming: it requires most GPU resources and takes the longest. LoRA injects a product of two trainable rank decomposition matrices over the top of each frozen pre-trained model module. LoRA is a basic technique and it is advised to use better methods in the real world. ‍ On comparing LoRA vs P-Tuning and Prefix Tuning, one can say for sure LoRA is the best strategy in terms of getting the most out of the model. Feb 23, 2024 · Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adjustable parameters. py. Low-Rank Adaptation (LoRA) is a reparametrization method that aims to reduce the number of trainable parameters with low-rank representations. 1 accelerate==0. 37. Jun 5, 2023 · Hugging Face has made LoRA and quantization accessible across a broad range of transformer models through the PEFT library and its integration with the bitsandbytes library. This is needed if someone wants to use the base model. google. This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune an image classification model. QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e. Switch to the docker folder and build Docker GPU image for training: Onece the building process complete, run the following command to start a Docker container and attach to it: 2. It assumes that the changes of pa-rameters lie in a low-rank space when the model is fully fine-tuned on a downstream task. We highly recommend to go through the post to get a detailed knowledge on LoRa. A deep dive to understand LoRA (low rank adaptation) and its possible configurations, inclu Fine-tuning. , into INT4) to reduce time and Low-rank adaptation (LoRA) is a popular parameter-efficient fine-tuning (PEFT) method for large language models (LLMs). 3. Your GPU has not enough memory to fine-tune your LLM or AI system? Use HuggingFace PEFT: There is a mathematical solution to approximate your complex weight May 1, 2023 · PEFT and LoRa PEFT is a method that employs various techniques, including LoRa, to efficiently fine-tune large language models. Based on the type of PEFT techniques one is using, we can update the configuration accordingly. peft 「peft」は、モデルの全体のファインチューニングなしに、事前学習済みの言語モデルをさまざまな下流タスクに適応させることができるパッケージです。 Feb 20, 2024 · One of the most popular PEFT methods, which many other PEFT methods are based off of, is the method of Low-Rank Adaptation (LoRA). 6. The abstract from the paper is: We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech May 17, 2024 · Install PEFT from pip: Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration with get_peft_model. In this paper, we analyze the impact of low-rank updating, as implemented in LoRA. safe_merge (`bool`): whether to activate the safe merging check to check if there is any potential Nan in the Low-Rank Adaptation LoRA [16] is one of the most pop-ular PEFT methods. com/drive/14xo6sj4dARk8lXZbOifHEn1f_70qNAwy?usp=sharingBlog Post: https://huggingface. Aug 22, 2023 · One of the most popular techniques in fine-tuning is a reparameterization-based method called Low-Rank Adaptation (LoRa) [9]. py illustrates their usage in preparing your model for training: This method merges the LoRa layers into the base model. 2. If you later call peft_model = get_peft_model(model, lora_config), you pass the modified model to PEFT again, not the original base model, which might lead to incorrect results (not sure). py。这意味着您可以在 Google Colab 中调整如此大的 LLM。 Feb 1, 2024 · Llama 2 is designed to handle a wide range of natural language processing (NLP) tasks, with models ranging in scale from 7 billion to 70 billion parameters. py 训练: python train. Parameter-efficient fine-tuning (PEFT) casts a new paradigm that leverages strong prior knowledge built in foundation mod-els and adapts them to a wide range of downstream tasks by Performance of PEFT-LoRA tuned bigscience/T0_3B on ought/raft/twitter_complaints leaderboard. py 接口: python post_api. It can be a valuable tool for researchers and developers who are working with Oct 25, 2023 · 6将peft路径添加到model_config. Now the question becomes whether to use an additive technique like Adapter and LoRA or you use a Prompt based technique like P-Tuning and Prefix Tuning. Aug 27, 2023 · AdaMix: A general PEFT method that tunes a mixture of adaptation modules, like Houlsby or LoRA, to improve downstream task performance for fully supervised and few-shot tasks. LoRA is low-rank decomposition method to reduce the number of trainable parameters which speeds up finetuning large models and uses less memory. The modular nature of PEFT also allows the same pretrained model to be adapted for multiple tasks by adding small task-specific weights, avoiding the need to store full copies. Apr 24, 2023 · LoRA Colab : https://colab. 6 peft > =0. The abstract from the paper is: We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech self. 使用 🤗 PEFT LoRA 在具有 11GB RAM 的消费级硬件上调整 bigscience/T0_3B 模型 (30 亿个参数)，例如 Nvidia GeForce RTX 2080 Ti、Nvidia GeForce RTX 3080 等，并且使用 🤗 Accelerate 的 DeepSpeed 集成: peft_lora_seq2seq_accelerate_ds_zero3_offload. This guide focuses on two methods that are more efficient for merging LoRA adapters by eliminating redundant parameters: TIES - TrIm, Elect, and Merge (TIES) is a three-step method for merging models. Aug 8, 2023 · Another issue could be this: In this notebook, you first load the model, then LoRA is applied (via PEFT and trainer), which modifies model inplace. The work has proposed MoE variations of two popular adapter PEFT approaches: LoRA and (IA)³, which are named MoLORA and MoV respectively. 11. My approach would be the following: model Aug 17, 2023 · Overall, LoRA PEFT is a promising technique for reducing the computational cost of fine-tuning large language models. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable LoRA. 0 safetensors > =0. As a result, LoRA has been widely studied within the AI research community, leading to a variety of extensions, alternatives, and practical tools to go along with it. 0 torch > =1. The documentation page TASK_GUIDES/TOKEN-CLASSIFICATION-LORA doesn’t exist in v0. co/blog/peftLoRa Paper: http 1. Results Performing inference for the sample of the test dataset, with the original model, the fully fine-tuned model and the PEFT-model, shows huge improvement of PEFT over the original model though not better than the fully fine tuned model. The new equation becomes Y = W X + A*B X. Dec 16, 2023 · Step 4: PEFT and LoRA config. 27. Collectively, these features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. We use the peft library from Hugging Face as well as LoRA to help us train on limited resources. LoRAを使ったチューニング方法はhuggingfaceのPEFT（Parameter-Efficient Fine-Tuning）というライブラリを使うと簡単に行うことができます。 LoRA. Low-Rank Adaptation ( LoRA) is a PEFT method that decomposes a large matrix into two smaller low-rank matrices in the attention layers. PEFT supports the QLoRa method to fine-tune a small fraction of LoRA. But for now we will understand LoRa briefly. PEFT does not have a specific example for Stable Diffusion LoRA, so this repo demonstrates how to use PEFT to perform Lora training and inference. Having applied the quantization part, we now proceed with the LoRA aspect. By using LoRA from 🤗 PEFT, we can reduce the number of trainable parameters in the model to only 0. As discussed earlier, QLoRA stands for Quantization + LoRA. LoRa. All the pretrained model parameters remain frozen. We would like to show you a description here but the site won’t allow us. PEFT, or Parameter Efficient Fine Tuning, allows Oct 31, 2023 · from datasets import load_dataset from random import randrange import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,TrainingArguments,pipeline from peft import LoraConfig You signed in with another tab or window. Feb 10, 2023 · Using 🤗 PEFT LoRA for tuning bigscience/T0_3B model (3 Billion parameters) on consumer hardware with 11GB of RAM, such as Nvidia GeForce RTX 2080 Ti, Nvidia GeForce RTX 3080, etc using 🤗 Accelerate's DeepSpeed integration: peft_lora_seq2seq_accelerate_ds_zero3_offload. Optimizer states; Learning rate schedule during and right after the reset; How frequently you reset LoRA for token classification. In this seminar code tutorial, we will explore how to perform fine-tuning using QLoRA (Quantized LoRA), a memory-efficient iteration of LoRA (Low-Rank Adaptation), for parameter-efficient fine-tuning. The most important feature of LoRA configuration is r (the dimension of the low-rank matrices). llm_model,peft_config) self. Aug 1, 2023 · Despite the help of LoRA and PEFT, the training is still better run on a GPU, so I set up a GCP Compute Engine G2 instance with NVIDIA L4, 40 GB of disk space, 4 vCPUs, and 16 GB of memory. MEFT: A memory-efficient fine-tuning approach that makes LLMs reversible, avoiding caching intermediate activations during training and significantly reducing memory Mar 9, 2023 · This leverages a feature in peft library, which is the disable_adapters context manager. 0. Jun 17, 2021 · We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. いろんなタスクでLoRAでチューニングしても毎回オリジナルのパラメータを保存する必要なし（1つだけあればOK） huggingface/peft. Args: progressbar (`bool`): whether to show a progressbar indicating the unload and merge process. This guide explores in more detail other options and features for Examples of using peft with trl to finetune 8-bit models with Low Rank Adaption (LoRA) The notebooks and scripts in this examples show how to use Low Rank Adaptation (LoRA) to fine-tune models in a memory efficient manner. py 验证: python evaluation. See detail in dataset_scripts folder. The abstract from the paper is: We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech Feb 22, 2023 · 「Google Colab」で 「PEFT」による大規模言語モデルのファインチューニングを試したので、まとめました。 1. LoRA achieves this reduction by adding low-rank “update matrices” to specific blocks of the model Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters. Using PEFT/LoRA, you are freezing the underlying LLM and only training the adapter. json file which is required. Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with naive support of LoRA serving), S-LoRA can improve the throughput by up to 4 times and increase the number of served adapters Nov 27, 2023 · An ecosystem. Whenever you load a PEFT adapter, it is a good idea to check whether it has an associated adapter_config. Overview of the training scripts: We will now describe how we trained a 20B parameter gpt-neox model using transformers, peft and trl. PEFT techniques such as LoRA, BitFit and IA3 have demonstrated comparable performance to full fine-tuning of pre-trained models for specific downstream tasks, all while demanding significantly fewer trainable parameters and reduced GPU Apr 20, 2023 · The baseline is a model created via Huggingface’s library as an AutoModelForCausalLM model, PEFT and a LoRA approach with subsequent merging of the weights. This significantly decreases the computational and storage costs. FlexLLM, the first co-serving system for LLM inference and parameter-efficient finetuning1. LoRA is a practically useful tool that gives (almost) anyone the power to train a specialized LLM over their data. You switched accounts on another tab or window. json file and the adapter weights, as shown in the example image above. Click here to redirect to the main version of peft_type: LORA task_type: CAUSAL_LM r: 8 lora_alpha: 32 #是控制LoRA调整幅度的参数。它决定了对原始模型参数的修改程度。较高的lora_alpha值意味着对原始模型参数的更大调整，这可能有助于模型更好地适应新的任务或数据，但也可能导致过拟合。 We have a detailed blog post on LoRa authored by James Skelton. To fine-tune customized Lora Attention Processor successfully, should I just avoid using PEFT temporally? Currently, we support full-parameter training and LoRA training for AnimateDiff. from_pretrained(peft_model_id) model = AutoModelForCausalLM. Setup Docker Environment. Fine-tuning Large Language Models (LLMs) is a crucial step in adapting these powerful models to specific tasks or domains. You might wonder what is PEFT? PEFT is Parameter Efficient Fine-tuning, its a technique that allows us to freeze most of the model params and tries to train a small percentage of the model params it supports low data scenarios to efficiently finetune the LLM on your domain dataset. Oct 14, 2023 · During fine-tuning with LORA, we keep ‘W’ fixed and introduce two matrices, ‘A’ and ‘B’, into the equation. llm_model = get_peft_model(self. This is not an error, but may impair May 13, 2023 · 「llm」の「lora」「rlhf」によるファインチューニング用のツールキットをまとめました。 1. The fundamental idea of PEFT is to train on a small portion of the model parameters for a fine-tuning dataset while keeping the remaining fixed, thereby greatly decreasing the computational and storage costs. Now, imagine if ‘m’ is 800 and May 26, 2023 · 🤗 PEFTメソッドのそれぞれは、PeftModelを構築する際に重要なパラメーターすべてを格納するPeftConfigクラスによって定義されます。 LoRAを使用するので、LoraConfigクラスをロード、作成する必要があります。LoraConfigでは、以下のパラメーターを指定します： Oct 5, 2023 · During fine-tuning, LORA updates the weights of the low-rank embedding and projection layers, as usual for data science, minimizing the loss function. py 推理: python predict. LoRA reduces trainable parameters by introducing rank decomposition matrices, while Prompt Tuning adds trainable soft prompts to the input LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. You signed out in another tab or window. 0, but exists on the main version. Load a PEFT adapter. To create a LoRA model from a pretrained transformer model, we import and set up LoraConfig. For example, Apr 16, 2024 · Next step is to setup Lora configuration. Then you can load the PEFT adapter model using the AutoModelFor class. lora： --lora_r 64 --lora_alpha 128 r和a 同时增大 May 27, 2024 · Tied-LoRA LoRA Figure 1: Comparison of the PEFT methods on RoBERTa-Large. We saw how LoRA can be implemented step-by-step on a summarization dataset, demonstrating its ability to significantly improve performance compared to the unadapted LLM. Next steps. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. LoRA is a PEFT method that reduces the number of parameters to fine-tune in large models by decomposing the attention layers into low-rank matrices. The weight matrix is broken down into low-rank matrices that are trained and updated. 77% of the original. 1 nltk==3. Most of PEFT methods supported in peft library but note that some PEFT methods such as Prompt tuning are not supported. import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "lucas0/empath-llama-7b" config = PeftConfig. LoRa focuses on adding extra weights to the model while freezing Aug 30, 2023 · When using PEFT to train a model with LoRA or QLoRA (note that, as mentioned before, the primary difference between the two is that in the latter, the pretrained models are frozen in 4-bit during the fine-tuning process), the hyperparameters of the low rank adaptation process can be defined in a LoRA config as shown below: from peft import Mar 11, 2023 · 因此近年來大家開始研究有效率的 Fine-Tuning，稱作 Parameter-Efficient Fine-Tuning (PEFT)，本次要介紹的是 Microsoft 團隊提出的 Low-Rank Adaptation（LoRA），概念是透過凍結原本的預訓練模型(e. print_trainable_parameters() UserWarning: Grad strides do not match bucket view strides. 19% of the parameters! To load a PEFT model for inference: from peft import AutoPeftModelForCausalLM from transformers import Now we have explored various PEFT techniques. LoRA works by fixing the original pre-trained model parameters, and adds trainable low-rank “adapters” to selected layers for fine-tuning. g. The PEFT library integrates popular PEFT techniques like LoRA, Prefix Tuning, AdaLoRA, Prompt Tuning, MultiTask Prompt Tuning, and LoHa with Transformers and Accelerate PEFT provides several methods for merging models like a linear or SVD combination. However, when applied in the setting of privacy-preserving federated Trying to load model from hub: yields. This repository provides the official PyTorch implementation of QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models. Reload to refresh your session. PEFT 「PEFT」(Parameter-Efficient Fine-Tuning)は、モデルの全体のファインチューニングなしに、事前学習済みの言語モデルをさまざまな下流タスクに適応させることができるパッケージです。 Relora integrates existing LoRA parameters into the main network and resets them. base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto') tokenizer PEFT and LoRA config You might wonder what is PEFT? PEFT is Parameter Efficient Fine-tuning, its a technique that allows us to freeze most of the model params and tries to train a small percentage of the model params it supports low data scenarios to efficiently finetune the LLM on your domain dataset. In the next cell, we create a LoraConfig with various settings sults are collected by serving and finetuning a LoRA-assisted LLaMA-2-70B model on four and eight NVIDIA A100 40GB GPUs. PEFT LoRA for Stable Diffsuion Trainer and Pipeline Example. Learn how to use LoRA with the LoraConfig class and the parameters it takes. It involves freezing some of the layers of the pre-trained model and only fine-tuning the last few layers that are specific to the downstream task. llm_model. , GPT-3) 的權重，搭配一個小的模型進行微調就可以達到很好的 Fine-Tuning 效果，同 LoRA. kq dq cw xz tv rt tl ac vw qy