🎯 TL;DR: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2.
📄 Paper | 🚀 GitHub Repository
This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories.
Architecture-specific advantages persist: A 400M encoder outperforms a 1B decoder on classification tasks, while a 400M decoder outperforms a 1B encoder on generation tasks.
pip install torch>=1.9.0
# until the new pip release, install from main to use decoders (transformers>=4.54.X will contain it)
# encoders work with transformers>=4.48.0
pip install git+https://github.com/huggingface/transformers.git
Encoder for Classification/Embeddings:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-encoder-150m")
model = AutoModel.from_pretrained("jhu-clsp/ettin-encoder-150m")
Decoder for Text Generation:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-decoder-150m")
model = AutoModelForCausalLM.from_pretrained("jhu-clsp/ettin-decoder-150m")
Ettin models are designed to provide a foundation for comparing encoder-only and decoder-only architectures. Unlike previous comparisons that were limited by different training data, architectures, and recipes, Ettin models use:
This approach allows for true apples-to-apples comparisons between encoder and decoder models, revealing the inherent strengths of each architecture.
The training data is publicly available and split across different phases:
| Size | Model | Parameters | Best For | Download |
|---|---|---|---|---|
| XXS | ettin-encoder-17m | 17M | Mobile/Edge devices |  |
| XS | ettin-encoder-32m | 32M | Fast inference | |
| Small | ettin-encoder-68m | 68M | Balanced performance | |
| Base | ettin-encoder-150m | 150M | Standard use cases | |
| Large | ettin-encoder-400m | 400M | High accuracy needs | |
| XL | ettin-encoder-1b | 1B | Best performance | |
| Size | Model | Parameters | Best For | Download |
|---|---|---|---|---|
| XXS | ettin-decoder-17m | 17M | Lightweight generation | |
| XS | ettin-decoder-32m | 32M | Quick prototyping | |
| Small | ettin-decoder-68m | 68M | Efficient generation | |
| Base | ettin-decoder-150m | 150M | Standard generation | |
| Large | ettin-decoder-400m | 400M | Quality generation | |
| XL | ettin-decoder-1b | 1B | Best generation | |
These models demonstrate what happens when you continue training encoders as decoders (and vice versa). Important: Load these models using the architecture they were converted to, not their original architecture.
Load as encoders using AutoModel or AutoModelForMaskedLM:
| Size | Model | Parameters | Description | Download |
|---|---|---|---|---|
| XXS | ettin-encoder-from-decoder-17m | 17M | Decoder → MLM continued training | |
| XS | ettin-encoder-from-decoder-32m | 32M | Decoder → MLM continued training | |
| Small | ettin-encoder-from-decoder-68m | 68M | Decoder → MLM continued training | |
| Base | ettin-encoder-from-decoder-150m | 150M | Decoder → MLM continued training | |
| Large | ettin-encoder-from-decoder-400m | 400M | Decoder → MLM continued training | |
| XL | ettin-encoder-from-decoder-1b | 1B | Decoder → MLM continued training | |
Load as decoders using AutoModelForCausalLM:
| Size | Model | Parameters | Description | Download |
|---|---|---|---|---|
| XXS | ettin-decoder-from-encoder-17m | 17M | Encoder → CLM continued training | |
| XS | ettin-decoder-from-encoder-32m | 32M | Encoder → CLM continued training | |
| Small | ettin-decoder-from-encoder-68m | 68M | Encoder → CLM continued training | |
| Base | ettin-decoder-from-encoder-150m | 150M | Encoder → CLM continued training | |
| Large | ettin-decoder-from-encoder-400m | 400M | Encoder → CLM continued training | |
| XL | ettin-decoder-from-encoder-1b | 1B | Encoder → CLM continued training | |
Example Usage for Cross-Objective Models:
# Encoder-from-decoder: Load as encoder
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-encoder-from-decoder-150m")
model = AutoModel.from_pretrained("jhu-clsp/ettin-encoder-from-decoder-150m")
# Decoder-from-encoder: Load as decoder
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-decoder-from-encoder-150m")
model = AutoModelForCausalLM.from_pretrained("jhu-clsp/ettin-decoder-from-encoder-150m")
Beyond the final models listed above, we provide access to intermediate training checkpoints for research and analysis purposes. These checkpoints allow you to study model behavior and performance throughout the training process. You can get the checkpoints either in HF format or raw for continued pre-training (e.g. Composer format).
All raw training checkpoints are available in the jhu-clsp/ettin-checkpoints dataset.
Each model repository contains multiple tagged versions representing different training stages:
step{number} - Pretraining phase checkpoints (e.g., step599525, step596528)ext{number} - Extension/mid-training phase checkpoints (e.g., ext1000, ext2000) decay{number} - Decay phase checkpoints (e.g., decay100, decay500)from transformers import AutoTokenizer, AutoModelForCausalLM
# Load a specific pretraining checkpoint
model = AutoModelForCausalLM.from_pretrained(
"jhu-clsp/ettin-decoder-400m",
revision="step590532" # Specific checkpoint tag
)
# Load an extension phase checkpoint
model = AutoModelForCausalLM.from_pretrained(
"jhu-clsp/ettin-decoder-400m",
revision="ext1000"
)
# Load a decay phase checkpoint
model = AutoModelForCausalLM.from_pretrained(
"jhu-clsp/ettin-decoder-400m",
revision="decay100"
)
This checkpoint availability enables detailed analysis of training dynamics, loss curves, and capability emergence across the complete 2T token training process.
Ettin provides the first controlled comparison of encoder vs. decoder architectures:
All training artifacts are publicly available:
Data: High-quality mixture including DCLM, Dolma v1.7, scientific papers, code, and curated sources totaling 2T+ tokens
Architecture: Transformer with RoPE, GLU activations, and prenorm layers
Training Phases:
Key Features:
| Parameter | 17M | 32M | 68M | 150M | 400M | 1B |
|---|---|---|---|---|---|---|
| Layers | 7 | 10 | 19 | 22 | 28 | 28 |
| Hidden Size | 256 | 384 | 512 | 768 | 1024 | 1792 |
| Intermediate Size | 384 | 576 | 768 | 1152 | 2624 | 3840 |
| Attention Heads | 4 | 6 | 8 | 12 | 16 | 28 |
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
# Load MLM model
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-encoder-150m")
model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/ettin-encoder-150m")
def predict_masked_token(text):
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# Get predictions for [MASK] tokens
mask_indices = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)
predictions = outputs.logits[mask_indices]
# Get top 5 predictions
top_tokens = torch.topk(predictions, 5, dim=-1)
return [tokenizer.decode(token) for token in top_tokens.indices[0]]
# Example
masked_text = "The capital of France is [MASK]."
predictions = predict_masked_token(masked_text)
print(f"Predictions: {predictions}")
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-decoder-150m")
model = AutoModelForCausalLM.from_pretrained("jhu-clsp/ettin-decoder-150m")
# Set pad token if needed
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
def generate_text(prompt, max_length=100, temperature=0.7):
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
inputs.input_ids,
max_length=max_length,
temperature=temperature,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
num_return_sequences=1
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example usage
prompt = "The future of artificial intelligence is"
generated = generate_text(prompt)
print(generated)
import argparse
from datasets import load_dataset
from sentence_transformers import (
SentenceTransformer,
SentenceTransformerTrainer,
SentenceTransformerTrainingArguments,
)
from sentence_transformers.evaluation import TripletEvaluator
from sentence_transformers.losses import CachedMultipleNegativesRankingLoss
from sentence_transformers.training_args import BatchSamplers
def main():
# parse the lr & model name
parser = argparse.ArgumentParser()
parser.add_argument("--lr", type=float, default=8e-5)
parser.add_argument("--model_name", type=str, default="jhu-clsp/ettin-encoder-150m")
args = parser.parse_args()
lr = args.lr
model_name = args.model_name
model_shortname = model_name.split("/")[-1]
# 1. Load a model to finetune
model = SentenceTransformer(model_name)
# 2. Load a dataset to finetune on
dataset = load_dataset(
"sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1",
"triplet-hard",
split="train",
)
dataset_dict = dataset.train_test_split(test_size=1_000, seed=12)
train_dataset = dataset_dict["train"].select(range(1_250_000))
eval_dataset = dataset_dict["test"]
# 3. Define a loss function
loss = CachedMultipleNegativesRankingLoss(model, mini_batch_size=16) # Increase mini_batch_size if you have enough VRAM
run_name = f"{model_shortname}-DPR-{lr}"
# 4. (Optional) Specify training arguments
args = SentenceTransformerTrainingArguments(
# Required parameter:
output_dir=f"output/{model_shortname}/{run_name}",
# Optional training parameters:
num_train_epochs=1,
per_device_train_batch_size=512,
per_device_eval_batch_size=512,
warmup_ratio=0.05,
fp16=False, # Set to False if GPU can't handle FP16
bf16=True, # Set to True if GPU supports BF16
batch_sampler=BatchSamplers.NO_DUPLICATES, # (Cached)MultipleNegativesRankingLoss benefits from no duplicates
learning_rate=lr,
# Optional tracking/debugging parameters:
save_strategy="steps",
save_steps=500,
save_total_limit=2,
logging_steps=500,
run_name=run_name, # Used in `wandb`, `tensorboard`, `neptune`, etc. if installed
)
# 5. (Optional) Create an evaluator & evaluate the base model
dev_evaluator = TripletEvaluator(
anchors=eval_dataset["query"],
positives=eval_dataset["positive"],
negatives=eval_dataset["negative"],
name="msmarco-co-condenser-dev",
)
dev_evaluator(model)
# 6. Create a trainer & train
trainer = SentenceTransformerTrainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
loss=loss,
evaluator=dev_evaluator,
)
trainer.train()
# 7. (Optional) Evaluate the trained model on the evaluator after training
dev_evaluator(model)
# 8. Save the model
model.save_pretrained(f"output/{model_shortname}/{run_name}/final")
# 9. (Optional) Push it to the Hugging Face Hub
model.push_to_hub(run_name, private=False)
if __name__ == "__main__":
main()
from datasets import load_dataset
from pylate import losses, models, utils
from sentence_transformers import (
SentenceTransformerTrainer,
SentenceTransformerTrainingArguments,
)
def main():
# Load the datasets required for knowledge distillation (train, queries, documents)
train = load_dataset(
path="lightonai/ms-marco-en-bge",
name="train",
)
queries = load_dataset(
path="lightonai/ms-marco-en-bge",
name="queries",
)
documents = load_dataset(
path="lightonai/ms-marco-en-bge",
name="documents",
)
# Set the transformation to load the documents/queries texts using the corresponding ids on the fly
train.set_transform(
utils.KDProcessing(queries=queries, documents=documents).transform,
)
# Define the base model, training parameters, and output directory
num_train_epochs = 1
lr = 8e-5
batch_size = 16
accum_steps = 1
model_name = "jhu-clsp/ettin-encoder-150m"
model_shortname = model_name.split("/")[-1]
# Set the run name for logging and output directory
run_name = f"{model_shortname}-colbert-KD-{lr}"
output_dir = f"output/{model_shortname}/{run_name}"
# Initialize the ColBERT model from the base model
model = models.ColBERT(model_name_or_path=model_name)
# Configure the training arguments (e.g., epochs, batch size, learning rate)
args = SentenceTransformerTrainingArguments(
output_dir=output_dir,
num_train_epochs=num_train_epochs,
per_device_train_batch_size=batch_size,
fp16=False, # Set to False if you get an error that your GPU can't run on FP16
bf16=True, # Set to True if you have a GPU that supports BF16
run_name=run_name,
logging_steps=10,
learning_rate=lr,
gradient_accumulation_steps=accum_steps,
warmup_ratio=0.05,
)
# Use the Distillation loss function for training
train_loss = losses.Distillation(model=model)
# Initialize the trainer
trainer = SentenceTransformerTrainer(
model=model,
args=args,
train_dataset=train,
loss=train_loss,
data_collator=utils.ColBERTCollator(tokenize_fn=model.tokenize),
)
# Start the training process
trainer.train()
model.save_pretrained(f"{output_dir}/final")
if __name__ == "__main__":
main()
import logging
from datasets import load_dataset
from sentence_transformers import (
SparseEncoder,
SparseEncoderModelCardData,
SparseEncoderTrainer,
SparseEncoderTrainingArguments,
)
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
from sentence_transformers.sparse_encoder.losses import SparseMultipleNegativesRankingLoss, SpladeLoss
from sentence_transformers.training_args import BatchSamplers
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# 1. Load a model to finetune with 2. (Optional) model card data
model = SparseEncoder(
"jhu-clsp/ettin-encoder-150m",
model_card_data=SparseEncoderModelCardData(
language="en",
license="apache-2.0",
)
)
# 3. Load a dataset to finetune on
full_dataset = load_dataset("sentence-transformers/natural-questions", split="train").select(range(100_000))
dataset_dict = full_dataset.train_test_split(test_size=1_000, seed=12)
train_dataset = dataset_dict["train"]
eval_dataset = dataset_dict["test"]
# 4. Define a loss function
loss = SpladeLoss(
model=model,
loss=SparseMultipleNegativesRankingLoss(model=model),
query_regularizer_weight=5e-5,
document_regularizer_weight=3e-5,
)
# 5. (Optional) Specify training arguments
run_name = "splade-distilbert-base-uncased-nq"
args = SparseEncoderTrainingArguments(
# Required parameter:
output_dir=f"models/{run_name}",
# Optional training parameters:
num_train_epochs=1,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
learning_rate=2e-5,
warmup_ratio=0.1,
fp16=True, # Set to False if you get an error that your GPU can't run on FP16
bf16=False, # Set to True if you have a GPU that supports BF16
batch_sampler=BatchSamplers.NO_DUPLICATES, # MultipleNegativesRankingLoss benefits from no duplicate samples in a batch
# Optional tracking/debugging parameters:
eval_strategy="steps",
eval_steps=1000,
save_strategy="steps",
save_steps=1000,
save_total_limit=2,
logging_steps=200,
run_name=run_name, # Will be used in W&B if `wandb` is installed
)
# 6. (Optional) Create an evaluator & evaluate the base model
dev_evaluator = SparseNanoBEIREvaluator(dataset_names=["msmarco", "nfcorpus", "nq"], batch_size=16)
# 7. Create a trainer & train
trainer = SparseEncoderTrainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
loss=loss,
evaluator=dev_evaluator,
)
trainer.train()
# 8. Evaluate the model performance again after training
dev_evaluator(model)
# 9. Save the trained model
model.save_pretrained(f"models/{run_name}/final")
# 10. (Optional) Push it to the Hugging Face Hub
model.push_to_hub(run_name)
import logging
import traceback
import torch
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.cross_encoder import (
CrossEncoder,
CrossEncoderModelCardData,
CrossEncoderTrainer,
CrossEncoderTrainingArguments,
)
from sentence_transformers.cross_encoder.evaluation import (
CrossEncoderNanoBEIREvaluator,
CrossEncoderRerankingEvaluator,
)
from sentence_transformers.cross_encoder.losses import BinaryCrossEntropyLoss
from sentence_transformers.evaluation import SequentialEvaluator
from sentence_transformers.util import mine_hard_negatives
# Set the log level to INFO to get more information
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
def main():
model_name = "jhu-clsp/ettin-encoder-150m"
train_batch_size = 64
num_epochs = 1
num_hard_negatives = 5 # How many hard negatives should be mined for each question-answer pair
# 1a. Load a model to finetune with 1b. (Optional) model card data
model = CrossEncoder(
model_name,
model_card_data=CrossEncoderModelCardData(
language="en",
license="apache-2.0",
),
)
print("Model max length:", model.max_length)
print("Model num labels:", model.num_labels)
# 2a. Load the GooAQ dataset: https://huggingface.co/datasets/sentence-transformers/gooaq
logging.info("Read the gooaq training dataset")
full_dataset = load_dataset("sentence-transformers/gooaq", split="train").select(range(100_000))
dataset_dict = full_dataset.train_test_split(test_size=1_000, seed=12)
train_dataset = dataset_dict["train"]
eval_dataset = dataset_dict["test"]
logging.info(train_dataset)
logging.info(eval_dataset)
# 2b. Modify our training dataset to include hard negatives using a very efficient embedding model
embedding_model = SentenceTransformer("sentence-transformers/static-retrieval-mrl-en-v1", device="cpu")
hard_train_dataset = mine_hard_negatives(
train_dataset,
embedding_model,
num_negatives=num_hard_negatives, # How many negatives per question-answer pair
margin=0, # Similarity between query and negative samples should be x lower than query-positive similarity
range_min=0, # Skip the x most similar samples
range_max=100, # Consider only the x most similar samples
sampling_strategy="top", # Sample the top negatives from the range
batch_size=4096, # Use a batch size of 4096 for the embedding model
output_format="labeled-pair", # The output format is (query, passage, label), as required by BinaryCrossEntropyLoss
use_faiss=True,
)
logging.info(hard_train_dataset)
# 2c. (Optionally) Save the hard training dataset to disk
# hard_train_dataset.save_to_disk("gooaq-hard-train")
# Load again with:
# hard_train_dataset = load_from_disk("gooaq-hard-train")
# 3. Define our training loss.
# pos_weight is recommended to be set as the ratio between positives to negatives, a.k.a. `num_hard_negatives`
loss = BinaryCrossEntropyLoss(model=model, pos_weight=torch.tensor(num_hard_negatives))
# 4a. Define evaluators. We use the CrossEncoderNanoBEIREvaluator, which is a light-weight evaluator for English reranking
nano_beir_evaluator = CrossEncoderNanoBEIREvaluator(
dataset_names=["msmarco", "nfcorpus", "nq"],
batch_size=train_batch_size,
)
# 4b. Define a reranking evaluator by mining hard negatives given query-answer pairs
# We include the positive answer in the list of negatives, so the evaluator can use the performance of the
# embedding model as a baseline.
hard_eval_dataset = mine_hard_negatives(
eval_dataset,
embedding_model,
corpus=full_dataset["answer"], # Use the full dataset as the corpus
num_negatives=30, # How many documents to rerank
batch_size=4096,
include_positives=True,
output_format="n-tuple",
use_faiss=True,
)
logging.info(hard_eval_dataset)
reranking_evaluator = CrossEncoderRerankingEvaluator(
samples=[
{
"query": sample["question"],
"positive": [sample["answer"]],
"documents": [sample[column_name] for column_name in hard_eval_dataset.column_names[2:]],
}
for sample in hard_eval_dataset
],
batch_size=train_batch_size,
name="gooaq-dev",
# Realistic setting: only rerank the positives that the retriever found
# Set to True to rerank *all* positives
always_rerank_positives=False,
)
# 4c. Combine the evaluators & run the base model on them
evaluator = SequentialEvaluator([reranking_evaluator, nano_beir_evaluator])
evaluator(model)
# 5. Define the training arguments
short_model_name = model_name if "/" not in model_name else model_name.split("/")[-1]
run_name = f"reranker-{short_model_name}-gooaq-bce"
args = CrossEncoderTrainingArguments(
# Required parameter:
output_dir=f"models/{run_name}",
# Optional training parameters:
num_train_epochs=num_epochs,
per_device_train_batch_size=train_batch_size,
per_device_eval_batch_size=train_batch_size,
learning_rate=2e-5,
warmup_ratio=0.1,
fp16=False, # Set to False if you get an error that your GPU can't run on FP16
bf16=True, # Set to True if you have a GPU that supports BF16
dataloader_num_workers=4,
load_best_model_at_end=True,
metric_for_best_model="eval_gooaq-dev_ndcg@10",
# Optional tracking/debugging parameters:
eval_strategy="steps",
eval_steps=1000,
save_strategy="steps",
save_steps=1000,
save_total_limit=2,
logging_steps=200,
logging_first_step=True,
run_name=run_name, # Will be used in W&B if `wandb` is installed
seed=12,
)
# 6. Create the trainer & start training
trainer = CrossEncoderTrainer(
model=model,
args=args,
train_dataset=hard_train_dataset,
loss=loss,
evaluator=evaluator,
)
trainer.train()
# 7. Evaluate the final model, useful to include these in the model card
evaluator(model)
# 8. Save the final model
final_output_dir = f"models/{run_name}/final"
model.save_pretrained(final_output_dir)
# 9. (Optional) save the model to the Hugging Face Hub!
# It is recommended to run `huggingface-cli login` to log into your Hugging Face account first
try:
model.push_to_hub(run_name)
except Exception:
logging.error(
f"Error uploading model to the Hugging Face Hub:\n{traceback.format_exc()}To upload it manually, you can run "
f"`huggingface-cli login`, followed by loading the model using `model = CrossEncoder({final_output_dir!r})` "
f"and saving it using `model.push_to_hub('{run_name}')`."
)
if __name__ == "__main__":
main()
python trl/scripts/sft.py \
--model_name_or_path jhu-clsp/ettin-decoder-17m \
--dataset_name trl-lib/Capybara \
--learning_rate 2.0e-5 \
--num_train_epochs 1 \
--packing \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--gradient_checkpointing \
--eos_token '<|im_end|>' \
--eval_strategy steps \
--eval_steps 100 \
--output_dir ettin-decoder-17m \
--push_to_hub
python trl/scripts/sft.py \
--model_name_or_path jhu-clsp/ettin-decoder-17m \
--dataset_name trl-lib/Capybara \
--learning_rate 2.0e-4 \
--num_train_epochs 1 \
--packing \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--gradient_checkpointing \
--eos_token '<|im_end|>' \
--eval_strategy steps \
--eval_steps 100 \
--use_peft \
--lora_r 32 \
--lora_alpha 16 \
--output_dir ettin-decoder-17m \
--push_to_hub
with sft.py:
import argparse
from datasets import load_dataset
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from transformers.models.auto.modeling_auto import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
from trl import (
ModelConfig,
ScriptArguments,
SFTConfig,
SFTTrainer,
TrlParser,
clone_chat_template,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
def main(script_args, training_args, model_args):
################
# Model init kwargs & Tokenizer
################
quantization_config = get_quantization_config(model_args)
model_kwargs = dict(
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
torch_dtype=model_args.torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
# Create model
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
valid_image_text_architectures = MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES.values()
if config.architectures and any(arch in valid_image_text_architectures for arch in config.architectures):
from transformers import AutoModelForImageTextToText
model_kwargs.pop("use_cache", None) # Image models do not support cache
model = AutoModelForImageTextToText.from_pretrained(model_args.model_name_or_path, **model_kwargs)
else:
model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, **model_kwargs)
# Create tokenizer
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, use_fast=True
)
# Set default chat template if needed
if tokenizer.chat_template is None:
# TODO: source should be passed as an argument
model, tokenizer = clone_chat_template(model, tokenizer, "Qwen/Qwen3-0.6B")
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
################
# Training
################
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
processing_class=tokenizer,
peft_config=get_peft_config(model_args),
)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
def make_parser(subparsers: argparse._SubParsersAction = None):
dataclass_types = (ScriptArguments, SFTConfig, ModelConfig)
if subparsers is not None:
parser = subparsers.add_parser("sft", help="Run the SFT training script", dataclass_types=dataclass_types)
else:
parser = TrlParser(dataclass_types)
return parser
if __name__ == "__main__":
parser = make_parser()
# When using the trl cli, this script may be run with additional arguments, corresponding accelerate arguments.
# To ensure that their parsing does not interfere with the script arguments, parse the arguments with
# `return_remaining_strings=True`, then ignore the remaining strings.
script_args, training_args, model_args, _ = parser.parse_args_and_config(return_remaining_strings=True)
main(script_args, training_args, model_args)
If you use Ettin models in your research, please cite our work:
@misc{weller2025seqvsseqopen,
title={Seq vs Seq: An Open Suite of Paired Encoders and Decoders},
author={Orion Weller and Kathryn Ricci and Marc Marone and Antoine Chaffin and Dawn Lawrie and Benjamin Van Durme},
year={2025},
eprint={2507.11412},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.11412},
}