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---
language:
- id
pipeline_tag: text-generation
license: cc-by-nc-4.0
library_name: transformers
tags:
- llama
- alpaca
- lora
---
# About :
This 🦙 Llama model was trained on a translated Alpaca dataset in Bahasa Indonesia. It uses Parameter Efficient Fine Tuning and LoRA to enable training on consumer-grade GPU hardware.
# How to Use :
## Load the 🦙 Alpaca-LoRA model
```python
import torch
import bitsandbytes as bnb
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
from peft import PeftModel, PeftConfig, prepare_model_for_int8_training, LoraConfig, get_peft_model
peft_model_id = "firqaaa/indo-Alpaca-LoRA-7b"
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained("decapoda-research/llama-7b-hf",
load_in_8bit=True,
device_map="auto")
# Load the LoRA model
model = PeftModel.from_pretrained(model, peft_model_id)
```
## Prompt Template
Prepare the prompt template
```python
instruction = "Tuliskan deret bilangan fibbonaci. Tulis jawaban/respons dalam Bahasa Indonesia."
PROMPT = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""
```
## Evaluation
feel free to change the parameters inside `GenerationConfig` to get better result.
```python
inputs = tokenizer(
PROMPT,
return_tensors="pt"
)
input_ids = inputs["input_ids"].cuda()
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.95,
top_k=40,
num_beams=4,
repetition_penalty=1.15,
)
print("Generating...")
print("Instruction : {}".format(instruction))
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=512,
)
print("Response : ")
for s in generation_output.sequences:
print(tokenizer.decode(s).split("### Response:")[1])
```
## Note :
Due to the high loss and lack of compute unit, we will update this model frequently to ensure the quality of generated text |