# 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
{"language": ["id"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["llama", "alpaca", "lora"], "pipeline_tag": "text-generation"}
transformers llama alpaca lora text-generation id license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#transformers #llama #alpaca #lora #text-generation #id #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

# 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


## Prompt Template
Prepare the prompt template



## Evaluation
feel free to change the parameters inside 'GenerationConfig' to get better result.



## Note :
Due to the high loss and lack of compute unit, we will update this model frequently to ensure the quality of generated text
# 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 ## Prompt Template
Prepare the prompt template ## Evaluation
feel free to change the parameters inside 'GenerationConfig' to get better result. ## Note :
Due to the high loss and lack of compute unit, we will update this model frequently to ensure the quality of generated text
TAGS
#transformers #llama #alpaca #lora #text-generation #id #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
 # 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 ## Prompt Template
Prepare the prompt template ## Evaluation
feel free to change the parameters inside 'GenerationConfig' to get better result. ## Note :
Due to the high loss and lack of compute unit, we will update this model frequently to ensure the quality of generated text
TAGS
#transformers #llama #alpaca #lora #text-generation #id #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
# 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## Prompt Template
Prepare the prompt template## Evaluation
feel free to change the parameters inside 'GenerationConfig' to get better result.## Note :
Due to the high loss and lack of compute unit, we will update this model frequently to ensure the quality of generated text