---
license: apache-2.0
---

## Model Description

Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs.

**Master-Yi-9B** is trained using the ORPO techniques. The model shows strong abilities in reasoning on coding and math questions.


![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp)

## Prompt Template

```
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
What is the meaning of life?<|im_end|>
<|im_start|>assistant
```

## Examples

```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 = "What is the mearning of life?"
messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"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)

```