metadata
library_name: transformers
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
- Base Model: ehartford/WizardLM-Uncensored-Falcon-7b
- Fine-tuned with: MBZUAI/Bactrian-X (Indonesian subset)
- Prompt Format: Alpaca
- Fine-tuned method: 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:
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
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer, BitsAndBytesConfig, TextStreamer
model = AutoPeftModelForCausalLM.from_pretrained(
"azale-ai/DukunLM-Uncensored-7B",
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",
)
)
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-Uncensored-7B")
streamer = TextStreamer(tokenizer)
instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
input_prompt = ""
if input_prompt == "":
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:
{instruction_prompt}
### Response:
"""
else:
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:
{instruction_prompt}
### Input:
{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, temperature=0.7,
do_sample=True, top_k=4, top_p=0.95
)
No Stream Output
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer, BitsAndBytesConfig
model = AutoPeftModelForCausalLM.from_pretrained(
"azale-ai/DukunLM-Uncensored-7B",
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",
)
)
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-Uncensored-7B")
instruction_prompt = "Bangun dialog chatbot untuk layanan pelanggan yang ingin membantu pelanggan memesan produk tertentu."
input_prompt = "Produk: Sepatu Nike Air Max"
if input_prompt == "":
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:
{instruction_prompt}
### Response:
"""
else:
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:
{instruction_prompt}
### Input:
{input_prompt}
### Response:
"""
inputs = tokenizer(text, return_tensors="pt").to("cuda")
_ = 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))
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.
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.