---
license: cc-by-nc-4.0
datasets:
- MBZUAI/Bactrian-X
language:
- id
- en
tags:
- qlora
- wizardlm
- uncensored
- instruct
- chat
- alpaca
- indonesia
---
# DukunLM V1.0 - Indonesian Language Model 🧙‍♂️

🚀 Welcome to the DukunLM V1.0 repository! DukunLM V1.0 is an open-source language model trained to generate Indonesian text using the power of AI. DukunLM, meaning "WizardLM" in Indonesian, is here to revolutionize language generation 🌟. This is an updated version from [azale-ai/DukunLM-Uncensored-7B](https://huggingface.co/azale-ai/DukunLM-Uncensored-7B) with full model release, not only adapter model like before 👽.

## Model Details

| Name Model                                                                       | Parameters | Google Colab                                                                                                                                                                       | Base Model                                                                                             | Dataset                                                                                                    | Prompt Format                                          | Fine Tune Method                           | Sharded Version                           |
|----------------------------------------------------------------------------------|------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------|--------------------------------------------|--------------------------------------------|
| [DukunLM-7B-V1.0-Uncensored](https://huggingface.co/azale-ai/DukunLM-7B-V1.0-Uncensored)   | 7B         | [Link](https://colab.research.google.com/drive/1UEiRqkfU1jGVMM9we4X3ooN1kkNGtfLd) | [ehartford/WizardLM-7B-V1.0-Uncensored](https://huggingface.co/ehartford/WizardLM-7B-V1.0-Uncensored)  | [MBZUAI/Bactrian-X (Indonesian subset)](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/id/train) | [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | [QLoRA](https://github.com/artidoro/qlora) | [Link](https://huggingface.co/azale-ai/DukunLM-7B-V1.0-Uncensored-sharded) |
| [DukunLM-13B-V1.0-Uncensored](https://huggingface.co/azale-ai/DukunLM-13B-V1.0-Uncensored) | 13B        | [Link](https://colab.research.google.com/drive/19xXYcAwVFLSItHm__GhPTYMryOGjdFkF) | [ehartford/WizardLM-13B-V1.0-Uncensored](https://huggingface.co/ehartford/WizardLM-13B-V1.0-Uncensored) | [MBZUAI/Bactrian-X (Indonesian subset)](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/id/train) | [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | [QLoRA](https://github.com/artidoro/qlora) | [Link](https://huggingface.co/azale-ai/DukunLM-13B-V1.0-Uncensored-sharded) |

⚠️ **Warning**: DukunLM is an uncensored model without filters or alignment. Please use it responsibly as it may contain errors, cultural biases, and potentially offensive content. ⚠️

## Installation

To use DukunLM, ensure that PyTorch has been installed and that you have an Nvidia GPU (or use Google Colab). After that you need to install the required dependencies:
```bash
pip3 install -U git+https://github.com/huggingface/transformers.git
pip3 install -U git+https://github.com/huggingface/peft.git
pip3 install -U git+https://github.com/huggingface/accelerate.git
pip3 install -U bitsandbytes==0.39.0 einops==0.6.1 sentencepiece
```

## How to Use

### Normal Model

#### Stream Output

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored")
streamer = TextStreamer(tokenizer)

instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
input_prompt = ""

if not input_prompt:
  prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt)

else:
  prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
_ = model.generate(
    inputs=inputs.input_ids,
    streamer=streamer,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_length=2048, temperature=0.7,
    do_sample=True, top_k=4, top_p=0.95
)
```

#### No Stream Output

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored")

instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
input_prompt = ""

if not input_prompt:
  prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt)

else:
  prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    inputs=inputs.input_ids,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_length=2048, temperature=0.7,
    do_sample=True, top_k=4, top_p=0.95
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Quantize Model

#### Stream Output

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer

model = AutoModelForCausalLM.from_pretrained(
    "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded",
    load_in_4bit=True,
    torch_dtype=torch.float32,
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        llm_int8_threshold=6.0,
        llm_int8_has_fp16_weight=False,
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
    )
)
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded")
streamer = TextStreamer(tokenizer)

instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
input_prompt = ""

if not input_prompt:
  prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt)

else:
  prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
_ = model.generate(
    inputs=inputs.input_ids,
    streamer=streamer,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_length=2048, temperature=0.7,
    do_sample=True, top_k=4, top_p=0.95
)
```

#### No Stream Output

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model = AutoModelForCausalLM.from_pretrained(
    "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded",
    load_in_4bit=True,
    torch_dtype=torch.float32,
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        llm_int8_threshold=6.0,
        llm_int8_has_fp16_weight=False,
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
    )
)
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded")

instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
input_prompt = ""

if not input_prompt:
  prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt)

else:
  prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
"""
  prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    inputs=inputs.input_ids,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_length=2048, temperature=0.7,
    do_sample=True, top_k=4, top_p=0.95
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Benchmark

Coming soon, stay tune 🙂🙂.

## Limitations

- The base model language is English and fine-tuned to Indonesia
- Cultural and contextual biases

## License

DukunLM V1.0 is licensed under the [Creative Commons NonCommercial (CC BY-NC 4.0) license](https://creativecommons.org/licenses/by-nc/4.0/legalcode).

## Contributing

We welcome contributions to enhance and improve DukunLM V1.0. If you have any suggestions or find any issues, please feel free to open an issue or submit a pull request. Also we're open to sponsor for compute power.

## Contact Us

[contact@azale.ai](mailto:contact@azale.ai)