language, library_name, pipeline_tag, tags

language	library_name	pipeline_tag	tags
vi	transformers	text-generation	vietnamese gpt2 causal-lm text-generation poetry continued-pretraining

GPT2 AIO Continued Pretraining Poem

VLAI-AIVN/gpt2-aio-continued-pretraining-poem is a Vietnamese GPT-2 causal language model adapted for poem-style generation through continued pretraining on a Vietnamese poem stanza corpus.

This checkpoint is a stage-2 domain adaptation model. It starts from a general Vietnamese GPT-2 checkpoint from the same project and is then continued-pretrained on poem data. It is not an instruction-tuned chat model.

Model Summary

• Architecture: GPT2LMHeadModel
• Layers: 12
• Hidden size: 768
• Attention heads: 12
• Context length: 1024 tokens
• Vocabulary size: 50,257
• Parameter count: 124,439,808
• Saved weights format: safetensors
• Framework: Hugging Face Transformers

Training Data

This model was continued-pretrained on the poem corpus:

Dataset size used by the training script:

• Total samples: 2766
• Train split: 2489
• Eval split: 277

Training examples are prepared as follows:

• Each stanza is prefixed with thơ:\n
• Text is normalized
• An end-of-text token is appended
• Samples are tokenized with max_length=64
• Padding is applied to fixed length
• Prefix tokens and padding tokens are masked out in the labels

This means the model is best prompted with the same prefix used during training.

Training Procedure

Important detail: despite the local folder name sft_poem, this run is not supervised fine-tuning in the instruction-tuning sense. It is continued pretraining for poem-domain adaptation.

The training script loads a previously pretrained Vietnamese GPT-2 checkpoint from the same project and continues training it on the poem corpus. In local project config, the base checkpoint is referenced by MODEL_DIR, set to ./artifacts/checkpoints/rand-init/checkpoint-8000.

Saved training arguments from this checkpoint:

Setting	Value
num_train_epochs	30
per_device_train_batch_size	32
per_device_eval_batch_size	32
learning_rate	5e-5
weight_decay	0.1
warmup_ratio	0.1
lr_scheduler_type	cosine
bf16	true
fp16	false
eval_strategy	epoch
save_strategy	epoch
logging_steps	10
save_total_limit	2
load_best_model_at_end	true
seed	42

Checkpoint Selection

This point matters for anyone using or comparing the uploaded model:

• Training ran to global_step=2340 over 30 epochs
• The best validation checkpoint was checkpoint-468
• Best validation metric: eval_loss=4.6743
• Best checkpoint epoch: 6.0

The project uses load_best_model_at_end=True, and the saved file hash confirms that:

• final/model.safetensors is identical to checkpoint-468/model.safetensors
• final/model.safetensors is different from checkpoint-2340/model.safetensors

So the final/ folder being uploaded contains the best checkpoint weights, not the last checkpoint weights.

Training Metrics

Recovered from trainer_state.json:

• Best eval loss: 4.6743
• Approximate best perplexity: 107.16
• Best checkpoint step: 468
• Best checkpoint epoch: 6.0

For reference, the last logged training state before training ended was:

• Final training step reached: 2340
• Final eval loss logged during training: 4.9351
• Last logged training loss: 2.6105

The uploaded final/ model corresponds to the best checkpoint section above.

Usage

Use the training prefix thơ:\n in prompts for the most consistent behavior.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "VLAI-AIVN/gpt2-aio-continued-pretraining-poem"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "thơ:\nTrăng lên đầu núi"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=64,
        do_sample=True,
        temperature=0.8,
        top_p=0.95,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Uses

• Vietnamese poem-style text generation experiments
• Domain adaptation studies for Vietnamese language models
• Further fine-tuning for poetry or literary generation tasks
• Baseline experiments on small-domain continued pretraining

Out-of-Scope Uses

• Safety-critical decision making
• Factual question answering without external verification
• Use as a chat assistant without additional instruction tuning
• Production deployment without evaluation, filtering, and prompt controls

Limitations

• The poem corpus is relatively small, so outputs may overfit stylistically or repeat phrasing patterns.
• The model is optimized toward poem-like continuations, not broad conversational usefulness.
• This is a domain-adapted generator, not an aligned assistant model.
• The repository snapshot used here does not declare an explicit license file locally. Confirm licensing before broad redistribution or commercial use.

Repository Context

This checkpoint comes from the Vietnamese GPT-2 pretraining project in this repository, which includes:

• Base pretraining on general Vietnamese corpora
• Continued pretraining on poem data
• Mixed one-step pretraining experiments
• Tokenizer training and data preparation scripts

Citation

If you use this model, cite the repository or link back to the Hugging Face model page.