初始化项目，由ModelHub XC社区提供模型

Model: VLAI-AIVN/vigpt2-aio
Source: Original Platform

README.md

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+---
+language:
+- vi
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- gpt2
+- vietnamese
+- text-generation
+- causal-lm
+model_type: gpt2
+datasets:
+- bkai-foundation-models/BKAINewsCorpus
+---
+
+# ViGPT2 AIO
+
+ViGPT2 AIO is a Vietnamese GPT-2 style causal language model pretrained for open-ended text generation and general Vietnamese language modeling.
+
+This model was developed to support teaching and hands-on practice in the [AIO](https://aivietnam.edu.vn/) course, while also serving as a general Vietnamese pretrained language model for experimentation and downstream adaptation.
+
+## Model Details
+
+- **Model name:** ViGPT2 AIO
+- **Architecture:** GPT-2
+- **Task:** Causal language modeling / text generation
+- **Language:** Vietnamese
+- **Library:** Transformers
+- **Weights format:** Safetensors
+
+## Training Data
+
+The model was pretrained on a mixture of Vietnamese news and Vietnamese Wikipedia text.
+
+### Data Sources
+
+- **BKAINewsCorpus** from `bkai-foundation-models/BKAINewsCorpus`
+- **Vietnamese Wikipedia** collected through a custom crawling pipeline, then cleaned from raw wikitext into plain text
+
+### Data Processing
+
+Before pretraining, the corpora were cleaned and deduplicated.
+
+- The tokenizer was trained on the raw corpora:
+  - `bkai_train.parquet`
+  - `vi_wiki_articles_clean.parquet`
+- The language model was pretrained on deduplicated versions of these corpora.
+- In the final training mixture, the Vietnamese Wikipedia corpus was upweighted relative to the news corpus.
+
+### Training Mixture
+
+The pretraining mixture used:
+
+- `bkai_train.parquet` with weight **1**
+- `vi_wiki_articles_clean.parquet` with weight **3**
+
+## Training Objective
+
+The model was pretrained with a standard causal language modeling objective, where the model learns to predict the next token in a sequence.
+
+## Limitations
+
+- The model may generate incorrect, nonsensical, or fabricated content.
+- Outputs can reflect biases or artifacts present in the pretraining data.
+- The model is not a verified factual source and should not be used without human validation in high-stakes settings.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+repo_id = "VLAI-AIVN/vigpt2-aio"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(repo_id)
+model.config.pad_token_id = tokenizer.pad_token_id
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device).eval()
+
+prompt = "Việt Nam là một đất nước"
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=80,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))