Lora finetune

Lora finetune. py \. For this guide, we train Mistral 7B on a single GPU using QLoRA, an efficient fine-tuning technique that combines quantization with LoRA to reduce memory usage while preserving task performance. Supports 4bit and 16bit QLoRA / LoRA finetuning via bitsandbytes. That is barely enough to store Llama 2–7b's weights, which means full fine-tuning is not possible, and we need to use parameter-efficient fine-tuning techniques like LoRA or QLoRA. We will use the QLoRA technique to fine-tune the model in 4-bit precision and optimize VRAM usage. Now, imagine if ‘m’ is 800 and Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. 3% after 24 epochs of training using a learning rate of 1e-7 and weight decay of 0. Mar 4, 2024 · This makes training with LoRA much faster and more memory-efficient, and produces smaller model weights (a few hundred MBs), all while maintaining the quality of the model outputs. 8 to run this notebook. 官方ptuning代码. py --file checkpoint_file. Finetuning the model using LoRA with r=32 (where r is the rank of the update matrix) reduces the number of tunable parameters to 15. We’ve created a fork of the original Alpaca-LoRA repo that adds support for Cog. Works on Linux and Windows via WSL. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA). The minimum required GPU memory is 24G, RTX3090 is enough for training. In this notebook, we will load the large model in 4bit using bitsandbytes ( Mistral-7B-v0. This data forms the foundation for fine-tuning the model. 2021. 結果 Jul 20, 2023 · 以下の記事は､Llama2が公開されて数日後に書いた内容です｡. When you look at the 3B parameter model's performance, it is comparable to a fully finetuned model at a fraction of the GPU memory. LoRA is a novel technique introduced by Microsoft that reduces the number of trainable parameters and maintains the model quality. This tutorial walks you through using KerasNLP to perform LoRA fine-tuning on a Gemma 2B model using the Databricks Dolly 15k dataset. Overview. Low-rank adaption (LoRA) is a technique to approximate the update to the linear layers in a LLM with a low-rank matrix factorization. Apache-2. LoRA (Low-Rank Adaptation) is an efficient way to fine-tune large models. In other words, you would need cloud computing to fine-tune your models. bin", but it will be automatically saved as "pytorch_model. This can help finetune the context given from BLIP2 to ALPACA, improving accuracy of generated outputs Acknowledgements Once again, I would like to credit the Salesforce team for creating BLIP2, as well as tloen, the original creator of alpaca-lora. Take Whisper-Large-v2, for instance: running 3-epochs with a 12-hour common voice dataset on 8 GB memory GPU takes 6–8 hours, which is 5 times faster than full fine-tuning with comparable performance. Clone the repository using Git: We finetune the large model (with & without LoRA) normally and in a low data regime. Support finetune the entire model (Much faster convergence and usually has better performance) The type of lora base model you want to train on. For instance, to fine-tune a 65 billion parameters model we need more than 780 Gb of GPU memory. LoRA is perhaps the most used one. Oct 10, 2023 · While RAG excels at providing access to dynamic external data sources and offers transparency in response generation, fine-tuning adds a crucial layer of adaptability and refinement. 2. + A Gradio ChatGPT-like Chat UI to demonstrate your language models. 1 with a single RTX 3090 and Stanford Alpaca is ~12 hours. pytorch_model. 05 lora_target_linear : true # target all linear layers Fine-tune the Whisper speech recognition model to support training without timestamp data, training with timestamp data, and training without speech data. e. In this blog, we present a step-by-step guide on fine-tuning Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers. 3 watching Forks. Cái hay ở đây . . This example shows you LoRA (Low-Rank Adaptation) implementation from scratch (manually) in a step-by-step manner (without PEFT package), and also shows you clear ideas behind this implementation in IPython notebook. Nó thực sự là một khái niệm quan trọng nếu bạn muốn finetune GPT. Readme License. This makes training with LoRA much faster, memory-efficient, and produces smaller When adding LoRA to unet, alpha is the constant as below: $$ W' = W + \alpha \Delta W $$ So, set alpha to 1. lora_dropout : 0. Basically ChatGPT but with Vicuna - jackaduma/Vicuna-LoRA-RLHF-PyTorch Parameter-efficient Fine-tuning (PEFT) với Adapters. Alternatively, you can use the official repo, to finetune the project without LoRa (or the above LoRa projects, without the LoRa flags) LoRA, short for Low-Rank Adaptation, is a technique that involves adding small trainable layers to an existing model without modifying the original weights. It is part of the Alpaca project, which aims to create efficient and high-quality instruction-following models with minimal parameters and training time. The 'llama-recipes' repository is a companion to the Llama 2 model. Amazon SageMaker is an ideal platform to implement LoRA fine-tuning of Whisper Oct 13, 2023 · To re-try after you tweak your parameters, open a Terminal ('Launcher' or '+' in the nav bar above -> Other -> Terminal) and run the command nvidia-smi. It works by inserting a smaller number of new weights into the model and only these are trained. 5的lora应该要加哪些层？ 另外想请教下我这个微调的层合理吗？ 谢谢！ Aug 8, 2023 · Fine-tune using Dreambooth + LoRA with faces dataset If you’re fine-tuning on faces the default training parameters will work well, but you can also use the use_face_detection_instead setting. Use the recursive option to process subfolders as well. (I also tested that this code works on Jun 29, 2023 · With Hugging Face’s PEFT library, you can freeze most of the original model weights and replace or extend model layers by training an additional, much smaller, set of parameters. Contribute to harrywang/finetune-sd development by creating an account on GitHub. A full pipeline to finetune Vicuna LLM with LoRA and RLHF on consumer hardware. With this intuitive UI, you can easily manage your dataset Without finetuning CLIP’s top-1 accuracy on the few-shot test data is 89. This guide will walk you through the process step by step, from setting up your environment to fine-tuning the model for your specific task. Therefore, it is recommended to fine-tune Llama based Oct 6, 2023 · Fine-tuning a state-of-the-art language model like Mistral 7B Instruct can be an exciting journey. 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. 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch - huggingface/diffusers Most of the existing finetune script, should work with the world model, provided you pretokenize your dataset with the world tokenizer, using the json2binidx tool # Fine tuning without LoRa. LLMTools implements low precision LoRA, a new memory-efficient finetuning algorithm that integrates with an arbitrary quantization module. Vicuna uses multi-round dialogue corpus, and the training effect is better than alpaca which is defaulted to single-round dialogue. Data Cleaning: Pre-process the data by removing noise, correcting errors, and ensuring a uniform Mar 23, 2023 · Step 1: Clone the Alpaca-LoRA repo. Stars. To finetune an existing model with LoRA, just work like full finetuning but with the LoRA options, in the directory RWKV-v4neo: This file reads the foundation model from the Hugging Face model hub and the LoRA weights from tloen/alpaca-lora-7b, and runs a Gradio interface for inference on a specified input. 0001. py文件没有放出来，看不到内部结构，如果想要保持和qwen对齐，1. bin" during training. This repository contains code for fine-tuning permissive open source LLMs using low-rank adaptation (LoRA). 基于ChatGLM3-6B模型的Lora方法的微调（lora finetuning）. We will use the powerful A6000 GPU to sh finetune_pp_peft_trainer_deepspeed. Nov 14, 2023 · In our examples, you will find two notebooks (llm-finetune-combined-with-registry. . Why use PEFT Finetuning? There are many reasons to use PEFT techniques, they have become the go-to way to finetune LLMs and other models. How to use To use this project, we need to do two things : A Lora approach, quantizing the base model to int 8, freezing its weights and only training an adapter; Much of the code is borrowed from alpaca-lora, but I refactored it quite a bit for this; I used an A100 GPU machine with Python 3. For more information about what those are and how they work, see this post. Dataset A full finetune can be memory-intensive, so make sure you are running on enough devices. This makes training much less expensive in terms of required compute. So in above example after 10 iterations these files will be written: 因为model. py is a script for fine-tuning LLaMA, a large language model for instruction following, using low-rank adaptation (LoRA). ‍ Low Data Regime: Using 1 hour of audio data UI tool for fine-tuning and testing your own LoRA models base on LLaMA, GPT-J and more. Most of PEFT methods supported in peft library but note that some PEFT methods such as Prompt tuning are not supported. All the code related to this article is available in our dedicated GitHub repository. In this notebook, we will load the large model in 4bit using bitsandbytes and use LoRA to train using the PEFT library from Hugging Face 🤗. Fine-tuning Vision Transformers on various classification datasets - bwconrad/vit-finetune. May 30, 2023 · Loading and Quantization of a GPT Model. 103 stars Watchers. Dec 30, 2023 · Prepare LoRA adapters. Without fine-tuning, the model can continue making the same mistakes. Fine-tuning allows for correcting such errors by fine-tuning the model with domain-specific I did this for just one dollar ($1) on an 1x A10G 24GB from Brev. 0 license Activity. py. May 23, 2023 · We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. When using the state-of-the-art OPTQ quantizer, LP-LoRA can finetune 3-bit LLMs for the first time (see results below). Each works through a different way to handle LoRA fine-tuned models as illustrated in the following diagram: First, we download the pre-trained Llama2 model with 7 billion parameters using SageMaker Studio Nov 7, 2023 · Step 2: Preparing Data for Fine-Tuning. 本项目主要针对ChatGLM、ChatGLM2和ChatGLM3模型进行不同方式的微调（Freeze方法、Lora方法、P-Tuning方法、全量参数等），并对比大模型在不同微调方法上的效果，主要针对信息抽取任务、生成任务、分类任务等。 And we are done fine-tuning the model! Before we generate text, let's compare the training time and memory usage of the two models. 全結合層や最終層を含んでいるので、正常に訓練できさえすれば、RomeoはBobになるはず。ただし、参照したブログと同じ挙動を期待するのであれば、Bobには置き換わらない. See this table for resource requirements on common hardware setups. py supports various LLaMA variants and datasets, and provides options for evaluation and inference. 0 to fully add LoRA. Dec 16, 2023 · Step 4: PEFT and LoRA config. With small dataset and sample lengths of 256, you can even run this on a regular Colab Tesla T4 instance. For more information about what those are and how they work, see this post . In the code, when loading the model and tokenizer, you need to specify the LoRA parameters. This is equivalent to ten A100 80 Gb GPUs. LoRA is beneficial because it allows the introduction of new concepts, such as art styles, characters, or themes, to the model without requiring extensive computation or memory usage. dev (instructions below). training_type: The type of the object you are training on: lora_type: It accepts lora or lycoris: max_train_steps ssbuild/chatglm_finetuning. Mar 9, 2023 · At the same time, it provides Alpaca LoRA one-click running Docker image, which can finetune 7B / 65B models. bin" during training, while "adapter_config. The values used in the QLoRA paper were r=64 and lora_alpha=16 , and these are said to generalize well, but we will use r=32 and lora_alpha=64 so that we have more emphasis on the new fine-tuned data while also reducing The most famous and in-use PEFT techniques are Prefix Tuning, P-tuning, LoRA, etc. py --deepspeed --deepspeed_config Sep 14, 2023 · 次にLoRAを正規化層を除く全ての層に適用してみました. 2% which is a formidable baseline. This update may include bug fixes, performance improvements, and new features. lich99/ChatGLM-finetune-LoRA. Fine tuning and LoRA are two techniques to adapt large language models (LLMs) to specific tasks or domains. 2022/4/12: Add tensorboard. You can reproduce all the experiments with OVHcloud AI Notebooks. 10 and cuda 11. Finetune. post3. If the LoRA seems to have too much effect (i. Alpacas are herbivores and graze on grasses and other plants. py , which implements QLora using PEFT to train our model. # 模型微调(trainer目录下) deepspeed --include=localhost:0,1 deepspeed_finetune_lora. LoRA and linear fine-tuning procedures. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Yes, that’s right, full finetuning (updating all layers) requires updating 2000 times more parameters than the Adapter v2 or LoRA methods, while the resulting modeling performance of the latter is equal to (and sometimes even better than) full finetuning, as reported in Hu et al. 公開から数ヶ月経った23年11月時点では､諸々の洗練された方法が出てきていますので､そちらも参照されることをおすすめします｡. Năm 2019, Neil Housby và đồng bọn đã nghĩ ra một cách fine-tuning một cách hiệu quả và thử áp dụng vào BERT-large: Ở mỗi khối Transformer trong model, ta chèn thêm vào 2 lớp Adapters (Hình 1) trước khi thực hiện fine-tuning. 86 GB with AdamW and takes about 3 hours (on an A100) for 50k training examples (here, the Alpaca dataset). Mar 23, 2023 · LORA_PATH，The checkpoint folder of the lora model It should be noted here that the config loaded by the lora model must be "adapter_config. The model employs grouped-query attention (GQA) to enhance inference speed and incorporates sliding window attention (SWA) for efficient processing of sequences with arbitrary length, minimizing inference costs. It accepts normal or sdxl: negative_prompt: Items you don't want in the image: images: Pass accessible direct links to images, cropped to 512 x 512 px. With LoRA you can fully finetune a 12B parameter model that would've otherwise run out of memory on the 80GB GPU, and comfortably fit and train a 3B parameter model. We are able to fit 13B training in 8-A100-40G/8-A6000, and 7B training in 8-RTX3090. Note that for T5, some layers are kept in float32 for stability purposes. Example usage: python generate. Available Datasets. The template exposes specific parameters, such as learning rate and epochs, that you can play around with to achieve the desired result. We set the Falcon modules that we want to fine-tune as target_modules in the LoRA configuration: Simple LLM Finetuner is a beginner-friendly interface designed to facilitate fine-tuning various language models using LoRA method via the PEFT library on commodity NVIDIA GPUs. Cog is a tool to package machine learning models in containers and we’re using it to install the dependencies to fine-tune and run the model. Then find the process ID PID under Processes and run the command kill [PID]. Accelerate inference and support Web deplo Nov 21, 2023 · Train a LoRA adapter on top of the Bitsandbytes 8-bit quantized version of the model leveraging parameter Feel free to finetune the model to the task of your choice by using the appropriate Jun 14, 2023 · LoRA: 3,506,176. Our fine-tuning script is implemented purely with Hugging Face transformers and is independent of the deep_training framework. See args. Nov 19, 2023 · One of the main takeaways is that LoRA allows us to finetune 7B parameter LLMs on a single GPU. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Oct 8, 2023 · LoRA là gì? Có thể bạn đã nghe đến thuật ngữ này nhiều lần. The memory usage of LoRA GPT-2 is roughly 35% times less than GPT-2. Second, we’ll use a couple of prompts with an LLM to generate a dataset that can be used to fine-tune any language model to understand how to use the Langchain Python REPL tool. A sample code for fine-tuning LLaMA2 with LoRA is provided below. The new equation becomes Y = W X + A*B X. This dataset contains 15,000 Public repo for HF blog posts. - zetavg/LLaMA-LoRA-Tuner To re-try after you tweak your parameters, open a Terminal ('Launcher' or '+' in the nav bar above -> Other -> Terminal) and run the command nvidia-smi. MODEL_LORA in RWKV-v4neo/chat. The best finetuning performance was 91. It took about an hour to run. This blog provides in-depth explanations of the Whisper model, the Common Voice dataset and the theory behind fine-tuning, with accompanying code cells to execute the data Use LoRA: finetune_lora. 构建训练数据集. Aug 30, 2023 · Learn how to use LoRA and QLoRA, parameter-efficient methods for fine-tuning pretrained language models, to generate product descriptions from prompts. The script also merges the LoRA weights into the model weights after training. LoRA (Low-Rank Adaptation of Large Language Models) is a popular and lightweight training technique that significantly reduces the number of trainable parameters. The pattern 'ITERATION' in the output filenames will be replaced with the iteration number and with 'LATEST' for the latest output. Put this script in the same folder of . Jul 21, 2023 · In this tutorial, we will walk you through the process of fine-tuning LLaMA 2 models, providing step-by-step instructions. We demonstrate this method by instruction-finetuning Lit-GPT This tutorial will use QLoRA, a fine-tuning method that combines quantization and LoRA. Fine tuning consists of retraining all the model parameters, which can be costly and cause overfitting. bin or . Our best model family, which we name Guanaco, outperforms all previous openly LoRA adapter : lora # for qlora, or leave blank for full finetune (requires much more GPU memory!) lora_r : 16 lora_alpha : 32 # alpha = 2 x rank is a good rule of thumb. LoRA also has many variants like QLoRA and LongLoRA, which have their own applications. 数据集: alpaca. LoRA does not add any inference latency when adapter weights are merged with the base model; In principle, LoRA can be applied to any subset of weight matrices in a neural network to reduce the number of trainable parameters. Code is tested using Stanford Alpaca dataset. It updates selected parts of the model only during training, speeding up the process and saving memory. from typing import List. The Colab T4 GPU has a limited 16 GB of VRAM. 1 Jan 10, 2024 · The performance of models fine-tuned using LoRA is comparable to the performance of fully fine-tuned models. Trong bài viết này, hãy cùng trituenhantao. One is Stanford's alpaca series, and the other is Vicuna based on shareGPT corpus. 11 forks Aug 1, 2023 · Here is the chart of training losses: Lora on E5-small-v2 training loss. 0. 期待値. sh. Alpaca-LoRA: Alpacas are members of the camelid family and are native to the Andes Mountains of South America. An unofficial implementation of fine-tuning ChatGLM with several PEFT methods on the Stanford Alpaca dataset. Jun 14, 2023 · LoRA: 3,506,176. (以下､元記事です) 話題のLamma2をファインチューニングし Mar 24, 2022 · This repository contains code for finetuning ChatGLM-6b using low-rank adaptation (LoRA). 有colab的同学可以直接在colab上尝试：. Only llama based models are supported! The output files will be saved every N iterations (config with --save-every N). 7 million, which is 1% of the parameters of the entire model. In this particular case, using QLoRA with the best setting (r=256 and alpha=512) requires 17. It can be used as a tool for image captioning, for example, astronaut riding a horse in space. The weight matrix is scaled by alpha/r, and thus a higher value for alpha assigns more weight to the LoRA activations. Users should treat this as example code for the use of the model, and modify it as needed. He is a member of the National Regeneration Movement (MORENA) political party and is the first left-wing president of Mexico since 1946. If the LoRA seems to have too little effect, set alpha to higher than 1. The training time of GPT-2 on a 16 GB Tesla T4 (Colab) is 7 minutes, and for LoRA, it is 5 minutes, a 30% decrease. Data preparation forms a critical preliminary step for fine-tuning: Data Collection: Gather text data that is specific to your application or domain. Similarly, you can finetune with LoRA on the Alpaca dataset using the Llama2 13B model on two devices via the following. BLIP is a pre-training framework for unified vision-language understanding and generation, which achieves state-of-the-art results on a wide range of vision-language tasks. # 处理指令数据集(processor目录下) python processing. 目前大模型微调方式Prefix Tuning、P-Tuning V1/V2到LoRA、QLoRA 全参微调SFT、本项目对ChatGLM3-6B通过多种方式微调，使模型具备落地潜质（包括但不限于客服、聊天、游戏）. This significantly reduces the number of trainable parameters and speeds up training with little impact on the final performance of the model. Mar 23, 2023 · Let's now train our model and run the cells below. 0-l Jul 18, 2023 · We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. pkl file and run python convert-to-safetensors. Open source trains 5x faster - see Unsloth Pro for up to 30x faster training! If you trained a model with 🦥Unsloth, you can use this cool sticker! LoRA (Low-Rank Adaptation) is one of mostly used parameter-efficient fine-tuning (PEFT) methods today. ipynb). sh进行训练，传入参数--lora_used True(False)控制是否使用lora。 DS版本支持33B（lora）llama快速进行finetune，训练时长与LMFlow一致。 实测：尽量避免使用DeepSpeed，DS默认使用cpu_offload极大的拖慢了训练速度。 May 30, 2023 · Illustration by the author. We demonstrate this method by instruction-finetuning LLaMA 7B on the Mistral 7B has claimed to deliver high performance while maintaining an efficient inference. Make sure per_device_train_batch_size*gradient_accumulation_steps is the same as the provided script for best reproducibility. Before the fine-tuning, the original E5-small-v2 scored GTX 1070, 1080 works, but is slow. Nov 3, 2022 · sanchit-gandhi Sanchit Gandhi. You will need to re-start your notebook from the beginning. MODEL_LOAD and args. The script is adapted from the one written by ignacfetser. 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. We also provide finetuned weights. Oct 14, 2023 · During fine-tuning with LORA, we keep ‘W’ fixed and introduce two matrices, ‘A’ and ‘B’, into the equation. This will automatically use face segmentation so that training is focused only on the faces in your images. json" and "adapter 模型微调使用 Lora方法 来撬动大模型，通过一些指令数据集来更好的理解用户指令，带动大模型给出相应的回答。. The goal of this repository is to provide a scalable library for fine-tuning Llama 2, along with some example scripts and notebooks to quickly get started with using the Llama 2 models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama 2 and other tools in the 1. Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the Vicuna architecture. The values used in the QLoRA paper were r=64 and lora_alpha=16 , and these are said to generalize well, but we will use r=8 and lora_alpha=16 so that we have more emphasis on the new fine-tuned data while also reducing Nov 16, 2023 · Fine-tuning Whisper using LoRA has shown promising results. Apache 2. We need the following imports to load and quantize an LLM. Most large language models (LLM) are too big to be fine-tuned on consumer hardware. Instruction: Tell me about alpacas. ChatGLM2-6B微调, SFT/LoRA, instruction finetune Resources. Whether you’re a seasoned machine learning practitioner or a newcomer to the field, this beginner Jul 18, 2023 · Finetune only the adapter layers, while using the frozen quantized model for context. For comparison a full fine-tuning on FLAN-T5-XXL with the same duration (10h) requires 8x A100 40GBs and costs ~322$. The training took ~10:36:00 and cost ~13. We prepared a run_clm. There are generally two schemes for fine-tuning FaceBook/LLaMA. Contribute to huggingface/blog development by creating an account on GitHub. Compare the performance and efficiency of LoRA and QLoRA with full fine-tuning and other variants. io tìm hiểu về phương pháp fine-tuning LoRA (viết tắt của “ Lo w- R ank A daptation of Large Language Models”). The evaluation loss is as follows: ROC AUC of LoRA on E5-small-v2. what I learned about fine-tuning stable diffusion. json" and the model name must be "adapter_model. Estimated training time for fine-tuning RedPajama-INCITE-Base-7B-v0. import fire. One-click run on Google Colab. LoRA is a technique that helps improve and fine-tune large language models while using far fewer parameters, making them more efficient and faster. Updated the lycoris_lora module to version 2. ChatGLM-Tuning. You can tune these values to your needs. ipynb and llm-finetune-separate-with-registry. Now only LoRA-finetuned weights are checkpointed during training: it provides much smaller checkpoints, but you now need to specify the base model to use it. Jul 20, 2023 · The steps to fine-tune LLaMA 2 using LoRA is the same as of SFT. , overfitted), set alpha to lower value. Using higher learning rates and a higher weight decay in line with the values mentioned in the paper May 13, 2023 · First, we’ll use a much more powerful model to use with Langchain Zero Shot ReAct tooling, the WizardLM 7b model. 22$ for 10h of training. They are known for their soft, luxurious fleece, which is used to make clothing, blankets, and other items. A good number is about 7-8 images. Alpaca-LoRA: The president of Mexico in 2019 was Andrés Manuel López Obrador, who took office on December 1, 2018. This tutorial will use QLoRA, a fine-tuning method that combines quantization and LoRA. 一种平价的chatgpt实现方案，基于清华的 ChatGLM-6B + LoRA 进行finetune. We use 4-bit quantization and train our model on the SAMsum dataset , an existing dataset that summarizes messenger-like conversations in the third person. ky fc df wi gf nx zr pa ut cb