---
license: mit
language:
- vi
---
# VinaLlama2-14B Beta

GGUF Here: [VinaLlama2-14B-GGUF](https://huggingface.co/qnguyen3/14b-gguf)

**Top Features**:

- **Context Length**: 32,768 tokens.
- **VERY GOOD** at reasoning, mathematics and creative writing.
- Works with **Langchain Agent** out-of-the-box.

**Known Issues**
- Still a bit struggling with Vietnamese fact (Hoang Sa & Truong Sa, Historical questions).
- Hallucination when reasoning.
- Can't do Vi-En/En-Vi translation (yet)!

Quick use:

VRAM Requirement: ~20GB

```bash
pip install transformers accelerate
```

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "vilm/VinaLlama2-14B",
    torch_dtype='auto',
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("vilm/VinaLlama2-14B")

prompt = "Một cộng một bằng mấy?"
messages = [
    {"role": "system", "content": "Bạn là trợ lí AI hữu ích."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=1024,
    eos_token_id=tokenizer.eos_token_id,
    temperature=0.25,
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids)[0]
print(response)
```