metadata
language:
- vi
- en
tags:
- viena
- causal-lm
- transformers
- pytorch
- chat
license: mit
library_name: transformers
pipeline_tag: text-generation
Viena Tiny Demo (SFT)
This is a tiny, demo-only Viena checkpoint fine-tuned for instruction following. It is not production quality. It is intended for smoke tests and workflow validation.
Model description
- Architecture: decoder-only Transformer (VienaModel) with RMSNorm, RoPE, SwiGLU, GQA.
- Parameters: ~10M (tiny config).
- Tokenizer: SentencePiece BPE (target vocab 2000; actual vocab may be smaller due to tiny data).
- Training: small offline synthetic dataset shipped with the repo.
Training data
- Pretrain:
viena_data/examples/pretrain_offline.jsonl - SFT:
viena_data/examples/sft_offline_train.jsonl - Validation:
viena_data/examples/sft_offline_val.jsonl
All datasets are synthetic and intended for offline tests.
Training recipe (tiny)
- Config:
configs/viena_tiny.yaml - Pretrain: 50 steps
- SFT: 20 steps
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "vietrix/viena-tiny-demo"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto",
)
prompt = "<|system|>
You are Viena.
<|user|>
Xin chao!
<|assistant|>
"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.9)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
Limitations
- Very small dataset and very few steps.
- Not suitable for real use or evaluation.
- Likely to hallucinate or be inconsistent.
License
MIT (code + demo weights). See repository license for details.