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---
language:
- id
pipeline_tag: conversational
---
# 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)
```
## Create Prompt Template
```python
def generate_prompt(instruction, input=None):
if input:
return f"""Berikut ini adalah petunjuk yang menjelaskan tugas, serta masukan yang menyediakan konteks tambahan. Tulis balasan yang melengkapi permintaan dengan tepat.
Petunjuk:
{instruction}
Masukan:
{input}
Output:"""
else:
return f"""Berikut ini terdapat panduan yang menjelaskan tugas. Mohon tuliskan balasan yang melengkapi permintaan dengan tepat.
Panduan:
{instruction}
Output:"""
```
## Evaluation
You are free to change parameters inside `GenerationConfig` to get better result.
```python
generation_config = GenerationConfig(
temperature=0.2,
top_p=0.75,
num_beams=8
)
def evaluate(instruction, input=None):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=256
)
for s in generation_output.sequences:
output = tokenizer.decode(s)
print("Output:", output.split("Output:")[1].strip())
# input your question/instruction
evaluate(input("Petunjuk: "))
```
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