Sfttrainer documentation

Sfttrainer documentation

By the way, if you find this article useful, please share it with friends! Share. the fast tokenizer will add <bos> and not add <eos> as the following picture: Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory - Home · unslothai/unsloth Wiki The SFTTrainer takes care of fine-tuning the Llama 2 model using the specified dataset, configurations, and parameters. You signed in with another tab or window. When I debug use the default script setting. To use it, specify the ‘ddp’ backend and the number of GPUs you want to use in the trainer. class ConstantLengthDataset(ConstantLengthDataset): # Fixes wrong len() output due to packing. So I run accelerate config : with the following settings: Create and edit web-based documents, spreadsheets, and presentations. 29 GiB free; 25. Dec 18, 2023 · Running the script below will load the “ tiiuae/falcon-7b ” model from Hugging Face, tokenize, set training parameters, and use SFTTrainer for fine-tuning. We recommend users to use `trl. Mar 4, 2024 · from trl import SFTTrainer: tqdm. torch. For example, you can call mlflow. \n<</SYS>>\n\n {input} [/INST] {response}" the question is that SFTTrainer can add <s> to the . We can see that the resulting data is in a dictionary of two keys: Features: containing the main columns of the data. py script on the stack-llama example. The latest high level abstraction from Hugging Face is the SFTTrainer class in the TRL library. ← Text Environments Sentiment Tuning →. LongTensor) — A tensor of shape (seq_len) containing query tokens or a list of tensors of shape (seq_len ). There is also the SFTTrainer class from the TRL library which wraps the Trainer class and is optimized for training language models like Llama-2 and Mistral with autoregressive techniques. 88 GiB already allocated; 133. Faster examples with accelerated inference. SFTTrainer always pads by default the sequences to the max_seq_length argument of the SFTTrainer. This step is pretty straightforward. Hugging Face SFTTrainer. length_sampler (Callable, optional) — Callable that returns the number of newly generated tokens. apply_chat_template, the labels are not correct in the train dataloader. Training time on new setup is increased to Mar 22, 2024 · Saved searches Use saved searches to filter your results more quickly Dec 10, 2023 · Saved searches Use saved searches to filter your results more quickly Oct 23, 2023 · Figure 4. Nov 20, 2023 · I try to fine-tune Llama 2 and when I launch the training with : trainer = SFTTrainer( model=model, train_dataset=dataset, peft_config=peft_config, dataset_text_field="text", max_seq_length=max_seq_length, tokenizer=tokenizer, args=train Mar 25, 2024 · SFTTrainer is designed for supervised fine-tuning (maximizing likelihood of in-distribution samples), so there is no straightforward way to utilize negative samples. # Oct 9, 2023 · Tried to allocate 12. 56 GiB total capacity; 18. If you want to modify that, make sure to create your own TrainingArguments object and pass it to the SFTTrainer constructor as it is done on the supervised_finetuning. query_tensor (torch. full_trainer. Jun 5, 2024 · If you’re not using LoRA and PEFT so there is no PEFT LoRA configuration used for training, use the following code to save your fine-tuned model to your system. To gain a better understanding of parameters, you can refer to the official documentation of PEFT. batch_size (int, *optional) — Batch size used for generation, defaults to 4. Supervised fine-tuning (or SFT for short) is a crucial step in RLHF. ignore_keys (List[str], optional) — A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions. The area of automated document processing has immense potential in the era of MLLMs. The Hugging Face Transformers library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. For our specific use case: Instruction fine-tuning, HuggingFace provides a sub-class of the trainer, the SFTTrainer (Supervised Fine-Tunining Trainer), in the trl library. There is no error, it just finishes. Community Calls. These models can revolutionize how we extract information from contracts, invoices, and other documents, requiring minimal training data. Fine-tuning a language model via PPO consists of roughly three steps: Rollout: The language model generates a response or continuation based on query which could be the start of a sentence. If you need to run an inference server with the trained Read more about CLI in the relevant documentation section or use --help for more details. tokenizer (Optional [`transformers. For comparison, when I ran the script above without other modules being saved, but varying the batch size up to 16, I got OOM with both the PP and DDP approaches. We’re on a journey to advance and democratize artificial intelligence through open source and open science. Trainer(accelerator="gpu",devices=8,strategy="ddp") Saved searches Use saved searches to filter your results more quickly Jun 5, 2024 · Optimizations for model fine-tuning #. 38 it/s, Epoch 11. You can check the code here. SFT and RLHF are computationally cheap compared to pretraining, but they require the curation of a dataset—either of high-quality LLM outputs or human feedback on LLM outputs — which can be difficult and time consuming. May be other alignment algorithms like KTO(also implemented in trl) would do the job in your case. 94 GiB (GPU 0; 39. 33 GiB already allocated; 12. 1B-Chat-v1. If someone can't upgrade to newer versions and needs an appropriate ConstantLengthDataset for epoch-wise training, I use this very hacky solution: from trl. You have the option to use a free GPU on Google Colab or Kaggle. Make sure to check it before training. Hello, In the SFTTrainer document, it is stated that if the dataset is in the right format, we dont need to specify a DataCollator with a response_template. Reload to refresh your session. See documentation for Memory Management and PYTORCH You signed in with another tab or window. The SFTTrainer provides an easy-to-use API to create and train SFT models with just a few lines of code on a given dataset. I am quite stuck on how to format the validation dataset in this case. The main code snippet Sep 14, 2023 · From the documentation on the SFTTrainer it seems like you can only use one or the other, but I'm wondering if I could do both at the same time? Let's say my data looks something like this "### Instruction: instructions ### Input: input ### Response: response" if I use a data collator on a packed example, it'll probably take everything after In the next step, we will include an adopter layer in our model. Basically, same model, train_dataset, evaluation dataset and collator are required. TrainingArguments class. How PPO works. **dataset** (Union[torch. pandas() ##### # This is a fully working simple example to use trl's SFTTrainer. 00 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. train(), SFTTrainer internally uses 🤗 Accelerate to prepare the model, optimizer and trainer using the DeepSpeed config to create DeepSpeed engine which is then trained. start_run() to start a new run, then call Logging Functions such as mlflow. Training without LoRA forgoes these benefits. Switch between documentation themes. new_model_name = "llama-2-7b-enhanced" # Save the fully fine-tuned model. cuda. Example code from the official docs fails due to this from datasets import load_dataset from transformers import AutoModelForCausalLM from trl import SFTTrainer dataset = load_dataset ("timdettmers Jun 20, 2023 · My problem is the trainer finishes early, often before the halfway point. My training dataset has 12,667 rows. 21 credits/hour). Note that a T4 only has 16 GB of VRAM, which is barely enough to store Llama 2–7b’s weights (7b × 2 bytes = 14 GB in FP16). g. 500. model. The W&B integration adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use. Jul 25, 2023 · In this section, we will fine-tune a Llama 2 model with 7 billion parameters on a T4 GPU with high RAM using Google Colab (2. MLflow Models integrations with transformers may not succeed when used with package versions outside of this range. Sep 18, 2023 · SFTTrainer builds on this with added support for parameter-efficient fine-tuning. ← Best of N Sampling KTO Trainer →. The instruction to load the dataset is given below by providing the name of the dataset of interest, which is tatsu-lab/alpaca: train_dataset = load_dataset ("tatsu-lab/alpaca", split ="train") print( train_dataset) OpenAI. Apr 1, 2024 · Hello, In the SFTTrainer document, it is stated that if the dataset is in the right format, we dont need to specify a DataCollator with a response_template. Another possible way is to modify prompt to include negative label in it. Evaluation: The query and response are evaluated with a function, model, human feedback or some combination of them. [ ] Feb 23, 2024 · Saved searches Use saved searches to filter your results more quickly Aug 1, 2023 · I used the following script that uses SFTTrainer to train Llama-2 on my own dataset using QLoRA. 64 GiB total capacity; 22. Once you have trained a model using either the SFTTrainer, PPOTrainer, or DPOTrainer, you will have a fine-tuned model that can be used for text generation. The trainer allows disabling any key part that you don’t want automated. The time it takes to fine-tune the model will vary depending on the compute and hyperparameters we set. 00 MiB (GPU 0; 23. The Colab T4 GPU has a limited 16 GB of VRAM. The above snippets will use the default training arguments from the transformers. The code runs on both platforms. trainer import ConstantLengthDataset. MLflow Tracking APIs provide a set of functions to track your runs. To perform QLoRA, all that is needed is the following: 1. But I don't know how to load the model with the checkpoint. brando June 27, 2023, 2:24am 3. Trainer ¶. Oct 7, 2023 · edited. , ChatGPT or LLaMA-2 [3]). SFTTrainer 예제 코드로, 배달의민족 QA 데이터를 정제한 3994개의 프롬프트를 Fine Tuning 데이터로 사용했습니다. To be precise, LoRA decomposes the portion of weight changes Δ Aug 5, 2023 · Development. save_pretrained ( new_model_name) The Trainer achieves the following: You maintain control over all aspects via PyTorch code in your LightningModule. 0: 874: February 19, 2023 Home ; Categories ; I think having an arg in the sfttrainer's init sounds good, but I think we don't even need an argument as this can be detected already under the hood by inspecting the dataset's column (if it contains input_ids only for instance), if that's properly documented in the SFTTrainer docs this shouldn't be an issue. The SFTTrainer makes it straightfoward to supervise fine-tune open LLMs supporting: Dataset formatting, including conversational and instruction format ( used) Training on completions only, ignoring prompts ( not used) Packing datasets for more efficient training ( used) The development of multi-modal LLMs is only just beginning, and the future holds exciting possibilities. Lightning supports the use of Torch Distributed Elastic to enable fault-tolerant and elastic distributed job scheduling. In TRL we provide an easy-to-use API to create your SFT models and train them with few lines of code on your dataset. Data Prepping. We will simply load the LLaMA-2 7B model from Hugging Face. 📜 Documentation: Read The Doc from unsloth import FastLanguageModel from unsloth import is_bfloat16_supported import torch from trl import SFTTrainer from 4 days ago · It usees the SFTTrainer from trl to fine-tune our model. And I save the checkpoint and the model in the same dir. apply_chat_template, the labels are not correct in the LLaMA2, introduced by Meta in 2023, is an open source large language model (LLMs). py . Before running the script, it’s essential to set the following environment May 25, 2023 · I’m not sure about when SFTTrainer should be used, my guess is that the SFTTrainer makes it easier to finetune a pretrained model, as compared to the standard Trainer that is designed for training from scratch, and may thus be more complex to use. As for SFTTrainer, I read/debug the code again and am pretty sure that at the end of every question/answer pair, a concat_token_id token is appended. e. Aug 30, 2023 · The developer experience of fine tuning large language models in general have improved dramatically over the past year or so. This will enable us to fine-tune the model using a small number of parameters, making the entire process faster and more memory-efficient. 26/13] If I'm using a batch size of 4, then wouldn't 1 Apr 1, 2024 · Beginners. This significantly decreases the computational and storage costs. # Fully fine-tuned model name. In this section, we’ll walk through the process of loading the fine-tuned model and generating text. You switched accounts on another tab or window. The trl library is a full stack tool to fine-tune and align transformer language and diffusion models using methods such as Supervised Fine-tuning step (SFT), Reward Modeling (RM) and the Proximal Policy Optimization (PPO) as well as Direct Preference Optimization (DPO). concat_token_id is set to 2(</eos>). The SFTTrainer takes care of fine-tuning the Llama 2 model using the specified dataset, configurations, and parameters. Does SFTTrainer accept raw text instead of tokens (inputs_ids, attention_mask, labels)? In the above script, train_dataset and eval_dataset are the tokens after processing, which contain [inputs_ids, attention_mask, labels] columns. Oct 24, 2023 · JunkRoy commented on Nov 13, 2023. 代码如下： import os from datasets import load_dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging, ) The ‘transformers’ MLflow Models integration is known to be compatible with the following package version ranges: 4. [ SFTTrainer] Fix Trainer when args is None huggingface/trl. Experimental support for Vision Language Models is also How to Fine-Tune Llama 2: A Step-By-Step Guide. 25. PEFT, or Parameter Efficient Fine Tuning, allows Dec 11, 2023 · The SFTTrainer implementation does not set labels - as far as I understand, this leads to "labels" being cloned to "input_ids" and shifted right (within transformers code) leading to using "next-token" prediction objective. ConstantLengthDataset` to create their dataset. PreTrainedModel]`): The model initializer to use for Check your model’s documentation for all accepted arguments. If none is passed, the trainer will retrieve that value from the tokenizer. 0 tokenizer. 71 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. 41. Compare the number of trainable parameters and training time under the two different methodologies. What do you think? Torch Distributed Elastic. Large Language Models like Llama 2 benefit from various dataset types: Instruction, Raw Completion, and Preference. Check out a complete flexible example at examples/scripts/sft. This is used to create a PyTorch dataloader. Dataset], optional) — PyTorch dataset or Hugging — Face dataset. 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. The HuggingFace library SFTTrainer has also support for training with QLoRA (4-bit Quantised model forward pass and LoRA adapters), and also saving the model with that. AdL8 April 1, 2024, 5:02pm 1. PreTrainedTokenizer and transformers. Jun 13, 2023 · When should one opt for the Supervised Fine Tuning Trainer (SFTTrainer) instead of the regular Transformers Trainer when it comes to instruction fine-tuning for Language Models (LLMs)? From what I gather, the regular Transformers Trainer typically refers to unsupervised fine-tuning, often utilized for tasks such as Input-Output schema formatting after conducting supervised fine-tuning. There Jun 13, 2023 · train_dataset: ConstantLengthDataset eval_dataset: ConstantLengthDataset trainer = SFTTrainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset,) This yields ValueError: You passed `packing=False` to the SFTTrainer, but you didn't pass a `dataset_text_field` or `formatting_func` argument. Aug 1, 2023 · trainer = SFTTrainer( "facebook/opt-350m", train_dataset=dataset, dataset_text_field="text", packing=True ) Note: According to the documentation, you must set a “dataset_text_field” to use packing. Please visit the Tracking API documentation for more details about using these APIs. This is a basic example of how to use the SFTTrainer from the library. Some tokenizers do not provide default value, so there is a check to retrieve the minimum between 2048 and that value. With 10000 max steps, it finishes at around 3500. # # This example fine-tunes any causal language model (GPT-2, GPT-Neo, etc. Ultimately, the best choice Feb 21, 2024 · Step 3 — Load LLaMA-2 with qLoRA Configuration. Note this is different from pipeline parallelism or model parallelism in the sense that the operations are going to be sequential, i. Dec 19, 2023 · E mbarking on the journey to fine-tune the ‘microsoft/phi-2’ model is like entering a world where language meets advanced tech magic. Oct 31, 2023 · Use SFTTrainer: If you have a pre-trained model and a relatively smaller dataset, and want a simpler and faster fine-tuning experience with efficient memory usage. In this part, we will learn about all the steps required to fine-tune the Llama 2 model with 7 billion parameters on a T4 GPU. Run the following code to start fine-tuning: 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. ← Reward Model Training PPO Trainer →. ) # by using the SFTTrainer from trl, we will leverage PEFT library to finetune # adapters on the model. Feb 1, 2024 · The script above runs fine in PP even when I train/save other modules in the LoRA config. PreTrainedTokenizer`]): The tokenizer to use for training. log_param() and mlflow. The supervised fine-tuning step is a key step in training causal language models like Llama for downstream tasks Use model after training. Given that SFT is a standard component of the alignment process, it has been explored heavily within AI literature. Will SFTTrainer unify all data to the 'max_seq_length' length? I am dealing with a question and answer task now. OutOfMemoryError: CUDA out of memory. [ ] Apr 4, 2024 · The documentation for FSDP states that one need only do configuration via accelerate config and then run the script via accelerate launch train. An example to use it with qlora is given here. 1 - 4. my data format is like data=" [INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. 🤗 PEFT (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting large pretrained models to various downstream applications without fine-tuning all of a model’s parameters because it is prohibitively costly. See the link here for more details on the implementation. But, for DDP, that results in OOM. Help as much as you can. These steps collectively set up the environment for fine-tuning a Llama 2 model with 7 billion parameters in 4-bit precision using the QLoRA technique, thus optimizing for VRAM limitations while maintaining model performance. Oct 10, 2023 · It facilitates supervised fine-tuning, a crucial step in RLHF (Reinforcement Learning with Human Feedback). utils. Model size after quantization is around 8GB. The SFTTrainer class handles all the heavy lifting of creating the PEFT model using the peft config that is passed. Dataset 예시 Sep 11, 2023 · The steps outlined above form the standardized training pipeline that is used for most state-of-the-art LLMs (e. 在使用如下代码使用NPU训练LLaMA模型时，在执行trainer. the layer n-1 will be kept idle while the layer n will be performing computation. The trainer uses best practices embedded by contributors and users from top AI labs such as Facebook AI Research, NYU, MIT, Stanford, etc…. 0. However, after I formatted my dataset with the TinyLlama/TinyLlama-1. PreTrainedTokenizerFast for more details. Note that although LLaMA-2 is open-source and Fine-tuning. 02. Jan 10, 2024 · from trl import SFTTrainer trainer = SFTTrainer( model=model, args=training_arguments, train_dataset=train_dataset, dataset_text_field="text", max_seq_length=1024, packing=True, ) Since SFTTrainer back-end is powered by 🤗 accelerate , you can easily adapt the training to your hardware setup in one line of code! Dec 3, 2023 · Development. Imagine shaping and customizing this powerful language Aug 8, 2023 · Inded, you are right, since the SFTTrainer class inherits from the Trainer function, if you check the source code. train ()时报错。. In addition, we need to consider the overhead due to optimizer states 📜 Documentation: Read The Doc from unsloth import FastLanguageModel from unsloth import is_bfloat16_supported import torch from trl import SFTTrainer from May 11, 2024 · This argument tells the SFTTrainer which column in the output should contain the results of the transformations. Format the instructions Collaborate on models, datasets and Spaces. Parameters. log_metric() to log a parameters and metrics respectively. Tried to allocate 144. This is what we will use from now on 👇. Recent state-of-the-art PEFT techniques Supervised Fine-tuning Trainer. 69 MiB free; 23. to get started. The minimal setup you need to do is passing an instantiated model or model name and a dataset. Low-Rank Adaptation (LoRA) is a technique allowing fast and cost-effective fine-tuning of state-of-the-art LLMs that can overcome this issue of high memory consumption. Successfully merging a pull request may close this issue. Before instantiating your Trainer / TFTrainer, create a TrainingArguments / TFTrainingArguments to access all the points of customization during training. Store documents online and access them from any computer. (Source: Hugging Face documentation) Hugging Face (HF) is an open-source Machine Learning (ML) platform that provides tools enabling users to build, train, and Jan 16, 2024 · Interestingly, the SFTTrainer class defined by TRL is adaptable and extensible enough to handle each of these cases. The library is built on top of the transformers library and thus allows to Check the documentation of transformers. I’m going to spare you from what all these parameters mean, but if you’re interested you can check out Hugging Face’s documentation: TrainerArguments API Reference; SFTTrainer API Reference; Step 8 — Fine-Tune and Save Model. PEFT methods only fine-tune a small number of (extra) model parameters - significantly decreasing computational Feb 13, 2024 · SFTTrainer: handles model training, optimization, and evaluation. Full parameter fine-tuning is a method that fine-tunes all the parameters of all the layers of the pre-trained model. py. [ 3230/10000 2:20:47 < 4:55:16, 0. For more flexibility and control over the training, you can use the dedicated trainer classes to fine-tune the model in Python. The following code-snippet takes care of all the data pre-processing Jun 14, 2023 · The short answer is that a Supervised Fine Tuning Trainer (SFTTrainer) is used for Instruct Fine Tuning. In addition to the Trainer class, Transformers also provides a Seq2SeqTrainer class for sequence-to-sequence tasks like translation or summarization. Aug 13, 2023 · So in the article, we are going to fine-tune the Llama 2 model via Huggingface’s tfl library, which has a simplified API to run the training and align with more of other transformer libraries The trl library is a full stack tool to fine-tune and align transformer language and diffusion models using methods such as Supervised Fine-tuning step (SFT), Reward Modeling (RM) and the Proximal Policy Optimization (PPO) as well as Direct Preference Optimization (DPO). Check your model’s documentation for all accepted arguments. It is a part of the LLaMA (Language Large Model) family, which encompasses a range of models with varying capacities, from 7 billion to 70 billion parameters. data. LoRA accelerates the adjustment process and reduces related memory costs. Before running the script, it’s essential to set the following environment Dec 13, 2023 · You can do model sequential parallelism with accelerate, simply load your model by passing device_map="auto" in from_pretrained. Jul 19, 2023 · The easiest is to use the SFTTrainer of trl, Train a model for document specific Q and A. If you do not specify this argument, the SFTTrainer will not know where to put the results of the transformations, and the new column may not show up in the output. model_init (`Callable [ [], transformers. I tried to train it on RTX 3090 24GB (35 FLOPS) and it took ~380 Hours for complete training. The library is built on top of the transformers library and thus allows to Running the script below will load the “tiiuae/falcon-7b” model from Hugging Face, tokenize, set training parameters, and use SFTTrainer for fine-tuning. If not specified, the tokenizer associated to the model will be used. The number of parameters is a key aspect of LLMs, determining their capacity to learn from data and Aug 22, 2023 · I trained my model using the code in the sft_trainer. Not Found. Trainer. Collaborate on models, datasets and Spaces. Fine-Tuning : SFTTrainer. Let us assume your dataset is imdb, the text you want to predict is inside the text field of the dataset, and you want to fine-tune the facebook/opt-350m model. It’s used in most of the example scripts. You signed out in another tab or window. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. 3 participants. When initializing the SFTTrainer class, you pass the following: base model to be trained; the training dataset If you have a dataset hosted on the 🤗 Hub, you can easily fine-tune your SFT model using [SFTTrainer] from TRL. After that, when you call trainer. 이러한 이유 덕분인지 LLM 파인튜닝 코드를 찾아보면 대부분이 SFTTrainer 를 이용한 것을 발견할 수 있었습니다. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF Sep 7, 2023 · skaltenp commented on Feb 7. SFTTrainer. How to use. Also the Epoch stats seem really off. Dataset, datasets. SFT use cases in AI Research. From the source code the actual work is done by the Trainer baseclass. No branches or pull requests. prediction_loss_only (bool) — Whether or not to return the loss only. Training with LoRA uses the SFTTrainer API with its PEFT integration. Load a transformers object from a local file or a run. trainer. I am trying to train codellama-7B in int8 using SFT trainer by trl. 2 participants. bellow is my preudocode. Try in Colab. From this link it seems apparent that the train_data for the SFTTrainer would have a format similar to this: PEFT. Then I upgraded my system and now I am trying to train it on 4xA4000 ~64GB (82 FLOPS). wx nf dd oy ib vc br kq pv av