Sfttrainer dataset github. Find and fix vulnerabilities Find and fix vulnerabilities Codespaces. Bring your own model for SageMaker labeling workflows with active learning is an end-to-end example that shows how to bring your custom training, inference logic and active learning to the Amazon SageMaker ecosystem. Oct 5, 2023 · from datasets import load_dataset: from peft import LoraConfig: from tqdm import tqdm: from transformers import AutoModelForCausalLM, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, AutoTokenizer: from trl import SFTTrainer: tqdm. Jun 15, 2023 · The working final script should be: from datasets import load_dataset from trl import SFTTrainer import transformers dataset = load_dataset ( "tatsu-lab/alpaca", split="train" ) model = transformers. IterableDataset is already a torch. length_sampler (Callable, optional) — Callable that returns the number of newly generated tokens. Quickstart If you have a dataset hosted on the 🤗 Hub, you can easily fine-tune your SFT model using SFTTrainer from TRL. utils. max_steps to the Trainer. py. 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 Nov 1, 2023 · You signed in with another tab or window. when train using data precessored by the datasets, I get follow warning and it leads to that I can not set epoch numbers: ValueError: The train_dataset does not implement __len__, max_steps has to 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. Mar 10, 2012 · import os import torch from datasets import load_dataset, Dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging ) from peft import LoraConfig, PeftModel from trl import SFTTrainer from huggingface_hub import login import pandas as pd You signed in with another tab or window. Sign up Step 1: Acquiring Access to Llama 2. This is passed to the SFTTrainer. IterativeTrainer. I’m not very familiar with those new models, I did not find any ressource for this. e. , pass a list of image files or a list of directories (with the images) to parallelize over them) query_tensor (torch. The virtual subclass idea was a good one- I wonder if there's another workaround given the Generic issue. Aug 11, 2023 · So the SFTTrainer uses a default data collator which is applied by default to the processed dataset. Nov 21, 2023 · I believe that it should support pre-tokenized dataset as a train_dataset as supported in Trainer class. Dataset to pr 3 days ago · from datasets import load_dataset: from peft import LoraConfig: from transformers import (AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, AutoTokenizer, TrainingArguments,) from peft import prepare_model_for_kbit_training, get_peft_model: from transformers import GPTQConfig: from trl import SFTTrainer # This example fine-tunes Llama 2 GitHub Gist: instantly share code, notes, and snippets. train_dataset). pritishmishra703 opened this issue on Sep 11 · 2 comments. from_pretrained (model_tag, add_eos_token=True) tokenizer. I would appreciate your inputs on this. completed on Nov 10. 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. I think the easiest would be to: accept a list of datasets for the eval_dataset at init; have a new boolean TrainingArguments named multiple_eval_dataset that would tell the Trainer that it has several evaluation datasets (since it won't be able to make the difference between one or several datasets: it could very well 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. Dec 2, 2023 · Saved searches Use saved searches to filter your results more quickly Jul 19, 2023 · Change loss and dataset format with SFTTrainer (TRL & QLoRA ) 🤗Transformers. bfloat16, device_map="auto") tokenizer = AutoTokenizer. With the SFTTrainer it's unclear to me how to instruction tune. num_train_examples = self. my data format is like data="[INST] <<SYS>>\You are a helpful, respectful and honest assistant. 4. At the end of the script we perform merging Sep 11, 2023 · SFTTrainer does not allow model=None #750. 0). Feb 18, 2024 · This depends on your max_seq_length you have set in your SFTTrainer, I suspect the sentences in that dataset are too small. Dataset: However, in the official tf documenation cardinality Mar 17, 2024 · from datasets import load_dataset: from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, HfArgumentParser,) from peft import LoraConfig, AutoPeftModelForCausalLM: from trl import SFTTrainer: @dataclass: class ScriptArguments: model_name: Optional[str] = field Hi, Trying out with many ways to run compute_metrics at 50 eval steps for test but nothing happens then. Find and fix vulnerabilities Sep 24, 2023 · Hi, thank you a lot for providing this library. arrow_dataset. py, line 138, we have self. Help as much as you can. SFTTrainer: A light and friendly wrapper around transformers Trainer to easily fine-tune language models or adapters on a custom dataset. train_dataset. # imports from datasets import load_dataset from trl import SFTTrainer # get dataset dataset = load_dataset ( "imdb" , split = "train" ) # get trainer trainer = SFTTrainer ( "facebook/opt-350m" , train_dataset StarCoder 2. Right now a datasets. If you use the SFTTrainer as is e. Find and fix vulnerabilities Codespaces. Oct 15, 2023 · https://github. Sign up for free to join this conversation on GitHub . It just keeps on running training. 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. specifically look Sep 7, 2023 · This will return the right size of the dataset, using the iterator output of the iter() function. SFTTrainer does not allow model=None. However, the first thing goes wrong here when deciding how to prepare the dataset (packing/non-packing): The SFTTrainer will then format the dataset for you using the defined format from the model’s tokenizer with the apply_chat_template method. Nov 16, 2023 · In essence it should be the same thing right? SFT does next word prediction but SFTTrainer can take care of properly formatting the input prompts which are important for instruction fine-tuning. query_tensor (torch. Dataset is passed, even though it implements the interface correctly (I think). 7. StarCoder2 is a family of code generation models (3B, 7B, and 15B), trained on 600+ programming languages from The Stack v2 and some natural language text such as Wikipedia, Arxiv, and GitHub issues. E. If you don't mind, can you clarify what I need to alter. Can you share the full script you are using? Maybe decreasing the seqlen & batch_size would help here Nov 12, 2023 · You signed in with another tab or window. IterableDataset that automatically takes care of distributing the necessary input shards to subprocesses in single node (since datasets 2. The dataset I used was in the type of datasets. From what I've read SFTTrainer should support multiple GPUs just fine, but when I run this I see one GPU with high utilization and one with almost none: Expected behaviour would b The SFTTrainer is a light wrapper around the transformers Trainer to easily fine-tune language models or adapters on a custom dataset. Recent state-of-the-art PEFT techniques bellow is my preudocode. My question and confusion is, what does the trainer do if the tokenizer has no chat_template , as is the case with the base llama model ? Slow dataset prepare #1299. To review, open the file in an editor that reveals hidden Unicode characters. from datasets import load_dataset # データセットの読み込み trainer = SFTTrainer(model=model Nov 21, 2023 · The author proposes NEFTune, a simple trick by adding noise to embedding vectorsduring training which improve the outcome of instruction fine-tuning by large margin. trainer. ConstantLengthDataset object at 0x7f8e7cd33a00> Train transformer language models with reinforcement learning. Before initiating the finetuning process, ensure you have access to the Llama 2 model from Meta. does it apply to llama-2-7b-cha-hf? is it same to llama-2-7b-hf for instruction tune? my dataset is multi turns dialogues. Aug 24, 2023 · paihengxu commented on Aug 24, 2023. py training script. data. finetune_llama_v2. osanseviero opened this issue on Dec 3, 2023 · 3 comments · Fixed by #1064. The SFTTrainer is a light wrapper around the transformers Trainer to easily fine-tune language models or adapters on a custom dataset. concat_token_id is set to 2(</eos>). I might be missing relevant details - but I the examples I've seen look like they are fine-tuning on the prompt and response rather than just the response. You signed in with another tab or window. Env: pip install -q accelerate==0. Instant dev environments These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth. 2 days ago · Saved searches Use saved searches to filter your results more quickly Saved searches Use saved searches to filter your results more quickly Apr 19, 2023 · Either use non-iterable datasets, which have a defined length: use DatasetDict and not passing streaming=True. Hi all, I'm running into an issue when I try to enable gradient checkpointing in the example sft. __getitem__ different from SFTTrainer and why is SFTTrainer not being used altogether. LongTensor) — A tensor of shape (seq_len) containing query tokens or a list of tensors of shape (seq_len ). May 23, 2022 · Describe the bug Due to memory problem, I need to save my tokenized datasets locally by CPU and reload it by multi GPU for running training script. . Thank you for your response. batch_size (int, *optional) — Batch size used for generation, defaults to 4. It pads the sequences in a batch to the max sequence length of that batch on the fly. # imports from datasets import load_dataset from trl import SFTTrainer # get dataset dataset = load_dataset ( "imdb" , split = "train" ) # get trainer trainer = SFTTrainer ( "facebook/opt-350m" , train_dataset Sep 4, 2023 · 1. Raw. But I don't know how to load the model with the checkpoint. To parallelize the loading, the gen_kwargs requires a list that can be split into num_proc parts (shards), which are then passed to the generator (e. Dataset`]]]):"," The dataset to use for evaluation. I am running this simple script to continue pretraining a model using SFTTrainer but the prepare_datalaoder method is taking forever Jul 20, 2023 · from datasets import load_dataset: from peft import LoraConfig: from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, AutoTokenizer, TrainingArguments,) from trl import SFTTrainer # This example fine-tunes Llama v2 model on Guanace dataset # using QLoRA. If this makes sense, I can create a PR and provide an example in the sft. from_pretrained (model_tag, torch_dtype=torch. I have a prompt and I have labels that I want the model to output. py script. Host and manage packages Security. The you can provide the SFTTrainer with just a text dataset and a model and you can start training with methods such as packing. Hope that clarifies things! Host and manage packages Security. Jul 6, 2023 · Hi! I am trying to prompt tune medalpaca 7b using prompt tuning or lora with the SFTTrainer. Instant dev environments 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. And I save the checkpoint and the model in the same dir. Dataset from the datasets package. The models use Grouped Query Attention, a context window of 16,384 tokens, with sliding window attention of 4,096 tokens. add_s @danielhanchen Sorry I don't really follow what you mean since I already specified dataset_text_field="text" in the args when I inited the SFTTrainer instance. Instant dev environments 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. I am trying to fine tune a Mistral-7B-Instruct model on some data using a multi-GPU setup. Dec 3, 2023 · SFTTrainer using quantized model + peft does not work. Reload to refresh your session. the prompt is: <s Dec 14, 2022 · We consider implementing an optimized sharding for distributed training directly in datasets. 0 peft==0. model_max_length = 2048 tokenizer. Hugging Face’s TRL library for supervised fine-tuning (SFT) is very useful for training large language models (LLM) on instruction datasets. Already have an account? Sign in to comment. When I debug use the default script setting. During fine-tuning, pairs of instruction and responses are sampled, in the form of text. 5 days ago · Download ZIP. This repository contains code for training a neural network model to classify NSFW (Not Safe for Work) content using the Falcon-7b model. 21. I want to finetune the falcon-7b model using a triplet loss function. cardinality(self. The SFTTrainer in trl may not mask the prompts in the inputs, they use all the tokens in a sentence as labels to perform supervised fine-tuning. May 25, 2023 · The SFTTrainer is mainly a helper class specifically designed to do SFT while the Trainer is more general. Dataset to pr Feb 28, 2022 · We could support several evaluation datasets inside the Trainer natively. In TRL we provide an easy-to-use API to fine-tune your models in an iterative way in just a few lines of code. This significantly decreases the computational and storage costs. Answered by mariosasko on Sep 4, 2023. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. # imports from datasets import load_dataset from trl import SFTTrainer # get dataset dataset = load_dataset ( "imdb" , split = "train" ) # get trainer trainer = SFTTrainer ( "facebook/opt-350m" , train_dataset Apr 3, 2021 · Worth mentioning that any function that expects a torch. IterableDataset. Dec 19, 2023 · @kashif from the team is also working on KTO trainer, which is an extension of DPO that makes it easier to use it, instead of requiring a paired dataset of accepted and rejected prompts, with KTO you only need a label 'selected' or 'rejected' for each prompt, making it easier to crowd-source such a dataset Aug 1, 2023 · I wanto use SFTTrainer to train a multi turns dialogues. However I don’t understand how to do so. With 10000 max steps, it finishes at around 3500. When I use SFFTrainer to fine-tune a LM for sequence classification, the SFTTrainer does not read the "label" field in the dataset I passed. , prompt loss can be safely ignored for many datasets. from_pretrained ( "facebook/opt-350m" ) tokenizer = transformers. Now that Flash Attention 2 is natively supported in transformersfor Llama / Falcon models, I tried to run the sft_trainer. Veeko July 19, 2023, 3:32pm 1. However, after I reload it by load_from_disk and start training, the speed is extremely s Nov 16, 2023 · Fine tune MPT-30B on Guanaco dataset and turn it into a chatbot - read the docstrings to install the correct versions of the required libraries. Member. Dataset (like torch. 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 ","","Note however, that the amount of performance gain is _dataset dependent_ and in particular, applying NEFTune on synthetic datasets like [UltraChat](https Here we have taken falcon 7B as the LLM and finetuned NSFW dataset with it# NSFW Classification using Falcon-7b. It is simple but with many options to make fine-tuning much easier and faster than with the standard Transformers library. 3. Learn more about bidirectional Unicode characters. Aug 1, 2023 · Benjamin Marie. 4. Fine tune Llama v2 models on Guanaco Dataset. I. The model is fine-tuned on the NSFW dataset and utilizes the Peft library for efficient training. You then need to pass your dataset to the ConstantLengthDataset init as usual (it is inherited from the original class) and it will allow the trainer to use the right epoch step size. Aug 22, 2023 · I trained my model using the code in the sft_trainer. Feb 1, 2024 · I am trying to fine-tune Llama 2 7B with QLoRA on 2 GPUs. #750. pandas() # Define and parse arguments. Find and fix vulnerabilities Supervised Fine-tuning Trainer. DataLoader) will fail a mypy-esque typecheck if a datasets. [ SFTTrainer] Fix Trainer when args is None #1064. I’m following this post: Fine-tune 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. My jobs run fine without gradient checkpointing, but as soon as it's enabled, I run into ValueErrors (see example below) . You switched accounts on another tab or window. Nov 14, 2023 · I would like to request a feature that allows users to input pre-tokenized datasets directly into the SFTTrainer, bypassing the need for the 'text' column and the subsequent tokenization step. Aug 10, 2023 · I print out the train dataset with and without packing on the imdb dataset. \<</SYS>>\\ Aug 11, 2023 · We found that performance of models finetuned on our short-completion dataset had a statistically-significant negative quadratic relationship with PLW, but performance of models fine-tuned on medium- and long-completion data did not show any relationship with PLW. I am initialising the models by adding the use_flash_attention_2=Trueflag in the from_pretrained()method as follows: Dec 31, 2023 · I am using SFTTrainer having following structure trainer = SFTTrainer( model, args=training_arguments, train_dataset=dataset, # eval_dataset=dataset, formatting_func=formatting_prompts_func, data_collator=collator, peft_config=peft_confi Oct 15, 2023 · You signed in with another tab or window. #1055. Iterative fine-tuning is a training method that enables to perform custom actions (generation and filtering for example) between optimization steps. My problem is the trainer finishes early, often before the halfway point. The result is a datasets. RewardTrainer : A light wrapper around transformers Trainer to easily fine-tune language models for human preferences (Reward Modeling). My training dataset has 12,667 rows. We recommend users to use `trl. In trainer_tf. Aug 1, 2023. You signed out in another tab or window. ∙ Paid. "," eval_dataset (Optional[Union[`datasets. AutoModelForCausalLM. We do not know which implementation is better, but the former one is commonly adopted, including FastChat and tatsu-lab/stanford_alpaca. Hello, Code model = AutoModelForCausalLM. @dataclass: class ScriptArguments: """ In a typical scenario we first load a dataset with a given text column e. 2/trl/trainer/sft_trainer. younesbelkada mentioned this issue on Dec 6, 2023. You can check the code here . Closed. Jun 9, 2023 · Sign in to comment. pyexample and am running into various errors (reproduced below). The way the training fails across multiple files seems to indicate that there is some issue with the dataset/dataloader after 381 steps. py#L60 Oct 4, 2023 · Member. lewtuncommented Oct 4, 2023. ConstantLengthDataset` to create their dataset. g. Can you try to skip the first 381 x bs elements of your data and check if it fails immediately? that way you might be able to narrow down which sample causes the issue. You can overwrite it by passing your own data_collator. I have a question related to SFT: def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]: # build inputs with format `<bos> X Y <eos>` and labels with format `< Host and manage packages Security. com/huggingface/trl/blob/v0. The same code seems to work in a single-GPU setting (when i set CUDA_VISIBLE_DEVICES=0) but not with multiple GPUs: Toggle navigation. text and with streaming enabled through load_dataset. 0 bitsan Sep 15, 2023 · I am also curious as to how is tokenization in InstructionDataset. Or pass args. I have made a Dataset class that inherits from torch. - huggingface/trl Jun 20, 2023 · I'm using SFTTrainer to finetune open_llama_7b with Qlora. Check out a complete flexible example inside examples/scripts folder. numpy() in the method def get_train_tfdataset(self) -> tf. Dataset`, Dict[`str`, `datasets. Supervised fine-tuning (or SFT for short) is a crucial step in RLHF. Without packing I see: Dataset({features: ['input_ids', 'attention_mask'], num_rows: 25000}) With packing I see: <trl. Submit a request through Meta, using the same email address for both Meta and Hugging Face accounts. This performance gain is as shown below: Methodology. This would significantly improve the training efficiency for users with large datasets. 3. Find and fix vulnerabilities Also, this requires SFTTrainer to receive a separate data collator to use for the eval dataset from the training dataset. Jul 7, 2023 · Hi! I am trying to prompt tune medalpaca 7b using prompt tuning or lora with the SFTTrainer. Instant dev environments We recommend users to use `trl. The text was updated successfully, but these errors were encountered: Dec 14, 2023 · My own task or dataset (give details below) Reproduction. cl kc pt ia hh ml zr ds vq hj