
## How to use🦙:
```py
import torch
import bitsandbytes as bnb
from peft import PeftModel, PeftConfig, prepare_model_for_int8_training, LoraConfig, get_peft_model
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

peft_model_id = "Yasbok/Alpaca_instruction_fine_tune_Arabic"
# config = PeftConfig.from_pretrained(peft_model_id)

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)

# Based on the inference code by `tloen/alpaca-lora`
def generate_prompt(instruction, input=None):
    if input:
        return f"""يوجد أدناه تعليمات تصف مهمة ، إلى جانب إدخال يوفر المزيد من السياق. اكتب ردًا يكمل الطلب بشكل مناسب.


### تعليمات:
{instruction}

### مدخل:
{input}

### انتاج:"""
    else:
        return f"""يوجد أدناه إرشادات تصف مهمة. يُرجى كتابة رد يكمل الطلب بشكل مناسب.



### تعليمات:
{instruction}

### انتاج:"""

# Inputs to instantiate the model:
generation_config = GenerationConfig(
    temperature=0.2,
    top_p=0.75,
    num_beams=4,
)
# Evaluate the model:
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("انتاج:", output.split("### انتاج:")[1].strip())

evaluate(input("تعليمات: "))
```
{"language": ["ar"], "library_name": "transformers", "tags": ["Alpaca", "Instruction-fine-tuning", "NLP", "Instruct Alpaca", "PEFT", "LoRA", "Instruction tuning", "Pytorch"], "datasets": ["Yasbok/Alpaca_arabic_instruct"]}
transformers Alpaca Instruction-fine-tuning NLP Instruct Alpaca PEFT LoRA Instruction tuning Pytorch ar dataset:Yasbok/Alpaca_arabic_instruct endpoints_compatible has_space region:us
TAGS
#transformers #Alpaca #Instruction-fine-tuning #NLP #Instruct Alpaca #PEFT #LoRA #Instruction tuning #Pytorch #ar #dataset-Yasbok/Alpaca_arabic_instruct #endpoints_compatible #has_space #region-us 


## How to use:

## How to use:
TAGS
#transformers #Alpaca #Instruction-fine-tuning #NLP #Instruct Alpaca #PEFT #LoRA #Instruction tuning #Pytorch #ar #dataset-Yasbok/Alpaca_arabic_instruct #endpoints_compatible #has_space #region-us 
 ## How to use:
TAGS
#transformers #Alpaca #Instruction-fine-tuning #NLP #Instruct Alpaca #PEFT #LoRA #Instruction tuning #Pytorch #ar #dataset-Yasbok/Alpaca_arabic_instruct #endpoints_compatible #has_space #region-us 
## How to use: