DukunLM V1.0 - Indonesian Language Model π§ββοΈ
π 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 π. This is an updated version from azale-ai/DukunLM-Uncensored-7B with full model release, not only adapter model like before π½.
Model Details
Name Model | Parameters | Google Colab | Base Model | Dataset | Prompt Format | Fine Tune Method | Sharded Version |
---|---|---|---|---|---|---|---|
DukunLM-7B-V1.0-Uncensored | 7B | Link | ehartford/WizardLM-7B-V1.0-Uncensored | MBZUAI/Bactrian-X (Indonesian subset) | Alpaca | QLoRA | Link |
DukunLM-13B-V1.0-Uncensored | 13B | Link | ehartford/WizardLM-13B-V1.0-Uncensored | MBZUAI/Bactrian-X (Indonesian subset) | Alpaca | QLoRA | Link |
β οΈ 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. β οΈ
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:
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
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-13B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-13B-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
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-13B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-13B-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
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
model = AutoModelForCausalLM.from_pretrained(
"azale-ai/DukunLM-13B-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-13B-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
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model = AutoModelForCausalLM.from_pretrained(
"azale-ai/DukunLM-13B-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-13B-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 ππ.
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.
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
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