# Llama-30b with LoRA Adapters

[Llama-30b with LoRA Adapters]

This repository contains a Llama-30b model fine-tuned with QLoRA (Quantization Low-Rank Adapter) adapters. The adapter is a plug-and-play tool that enables the LLaMa model to perform well in many Vietnamese NLP tasks.

## Model Overview

The Llama-30b model is a large language model capable of generating meaningful text and can be used in a wide variety of natural language processing tasks, including text generation, sentiment analysis, and more. By using LoRA adapters, the model achieves better performance on low-resource tasks and demonstrates improved generalization.

## Dataset and Fine-Tuning

The LLaMa model was fine-tuned on over 200K instructions from various sources to improve its ability to understand and generate text for different tasks. The instruction dataset comprises data from the following sources:

- Alpaca 52
- LiMA 1K
- Dolly 15K
- VietHealth
- WikiHow
- GPT4ALL
- VietQuAD

## Loading the Model

To load the fine-tuned Llama-30b model with LoRA adapters, follow the code snippet below:

```python
import torch  
from transformers import AutoModelForCausalLM, LlamaTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model_name = "VietnamAIHub/Vietnamese_SFT_llama_30B_v1"
cache_dir="/save_weight_path"
## Loading Base LLaMa model weight and Merge with Adapter Weight wiht the base model
m = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"cuda": 0},
    cache_dir=cache_dir
)

## Save model to specific path
tok = LlamaTokenizer.from_pretrained(model_name, cache_dir=cache_dir)

## Loading Unified Model Again after Merging the Weight
tok.bos_token_id = 1

generation_config = dict(
        temperature=0.2,
        top_k=20,
        top_p=0.9,
        do_sample=True,
        num_beams=1,
        repetition_penalty=1.2,
        max_new_tokens=400, 
        early_stopping=True,
       
    )

prompt="Cách để học tập về một môn học thật tốt"
_DEFAULT_TEMPLATE=f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### prompt:\n{prompt}\n\n### response:\n"

inputs = tok(message,return_tensors="pt")  #add_special_tokens=False ?
generation_output = m.generate(
    input_ids = inputs["input_ids"].to(device),
    attention_mask = inputs['attention_mask'].to(device),
    eos_token_id=tok.eos_token_id,
    pad_token_id=tok.pad_token_id,
    **generation_config
)
generation_output_ = m.generate(input_ids = inputs["input_ids"].to(device), **generation_config)
s = generation_output[0]
output = tok.decode(s,skip_special_tokens=True)
response = output.split("### response:")[1].strip()
print(respone)
```

## Conclusion
The Llama-30b with LoRA adapters is a versatile language model that can be utilized for a wide range of NLP tasks in Vietnamese. We hope that researchers and developers find this model useful and are encouraged to experiment with it in their projects.

For any questions, feedback, or contributions, please feel free to contact the maintainers of this repository TranNhiem 🙌: [Linkedin](https://www.linkedin.com/in/tran-nhiem-ab1851125/) [Twitter](https://twitter.com/TranRick2) [Facebook](https://www.facebook.com/jean.tran.336). Happy fine-tuning and experimenting with the Llama-30b model!