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Model: VLAI-AIVN/gpt2-aio-continued-pretraining-poem
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- vi
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- 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.
+
+```python
+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.

config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 0,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.4",
+  "use_cache": true,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.55.4"
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

training_args.bin

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