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---
library_name: peft
license: cc-by-nc-4.0
language:
- en
- id
datasets:
- MBZUAI/Bactrian-X
tags:
- qlora
- wizardlm
- uncensored
- instruct
- alpaca
pipeline_tag: text-generation
---
# DukunLM - Indonesian Language Model 🧙♂️
🚀 Welcome to the DukunLM repository! DukunLM 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 with its massive 7 billion parameters! 🌟
## Model Details
- Model: [nferroukhi/WizardLM-Uncensored-Falcon-7b-sharded-bf16](https://huggingface.co/nferroukhi/WizardLM-Uncensored-Falcon-7b-sharded-bf16)
- Base Model: [ehartford/WizardLM-Uncensored-Falcon-7b](https://huggingface.co/ehartford/WizardLM-Uncensored-Falcon-7b)
- Fine-tuned with: [MBZUAI/Bactrian-X (Indonesian subset)](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/id/train)
- Prompt Format: [Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
- Fine-tuned method: [QLoRA](https://github.com/artidoro/qlora)
⚠️ **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
!pip install -U git+https://github.com/huggingface/transformers.git
!pip install -U git+https://github.com/huggingface/peft.git
!pip install -U bitsandbytes==0.39.0
!pip install -U einops==0.6.1
```
## How to Use
### Stream Output
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
model = AutoModelForCausalLM.from_pretrained(
"nferroukhi/WizardLM-Uncensored-Falcon-7b-sharded-bf16",
load_in_4bit=True,
torch_dtype=torch.float32,
trust_remote_code=True,
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",
)
)
model = PeftModel.from_pretrained(model, "azale-ai/DukunLM-Uncensored-7B")
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-Uncensored-7B")
streamer = TextStreamer(tokenizer)
input_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
text = f"""
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:
{input_prompt}
### Response:
"""
inputs = tokenizer(text, 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, use_cache=True,
temperature=0.7, do_sample=True,
top_k=4, top_p=0.95
)
```
### No Stream Output
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model = AutoModelForCausalLM.from_pretrained(
"nferroukhi/WizardLM-Uncensored-Falcon-7b-sharded-bf16",
load_in_4bit=True,
torch_dtype=torch.float32,
trust_remote_code=True,
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",
)
)
model = PeftModel.from_pretrained(model, "azale-ai/DukunLM-Uncensored-7B")
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-Uncensored-7B")
input_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
text = f"""
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:
{input_prompt}
### Response:
"""
inputs = tokenizer(text, 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=512, use_cache=True,
temperature=0.7, do_sample=True,
top_k=4, top_p=0.95
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Limitations
- The base model language is English and fine-tuned to Indonesia
- Cultural and contextual biases
## License
DukunLM 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. If you have any suggestions or find any issues, please feel free to open an issue or submit a pull request.
## Contact Us
[contact@azale.ai](mailto:contact@azale.ai) |