275 lines
10 KiB
Markdown
275 lines
10 KiB
Markdown
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---
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license: cc-by-nc-4.0
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datasets:
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- MBZUAI/Bactrian-X
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language:
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- id
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- en
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tags:
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- qlora
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- wizardlm
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- uncensored
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- instruct
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- chat
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- alpaca
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- indonesia
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---
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# DukunLM V1.0 - Indonesian Language Model 🧙♂️
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🚀 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 👽.
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## Model Details
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| Name Model | Parameters | Google Colab | Base Model | Dataset | Prompt Format | Fine Tune Method | Sharded Version |
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|----------------------------------------------------------------------------------|------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------|--------------------------------------------|--------------------------------------------|
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| [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) |
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| [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) |
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⚠️ **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. ⚠️
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## Installation
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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:
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```bash
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pip3 install -U git+https://github.com/huggingface/transformers.git
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pip3 install -U git+https://github.com/huggingface/peft.git
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pip3 install -U git+https://github.com/huggingface/accelerate.git
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pip3 install -U bitsandbytes==0.39.0 einops==0.6.1 sentencepiece
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```
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## How to Use
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### Normal Model
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#### Stream Output
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda")
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tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored")
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streamer = TextStreamer(tokenizer)
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instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
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input_prompt = ""
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if not input_prompt:
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prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt)
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else:
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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.
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### Instruction:
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{instruction}
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### Input:
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{input}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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_ = model.generate(
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inputs=inputs.input_ids,
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streamer=streamer,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_length=2048, temperature=0.7,
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do_sample=True, top_k=4, top_p=0.95
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)
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```
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#### No Stream Output
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda")
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tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored")
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instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
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input_prompt = ""
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if not input_prompt:
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prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt)
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else:
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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.
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### Instruction:
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{instruction}
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### Input:
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{input}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(
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inputs=inputs.input_ids,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_length=2048, temperature=0.7,
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do_sample=True, top_k=4, top_p=0.95
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Quantize Model
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#### Stream Output
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
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model = AutoModelForCausalLM.from_pretrained(
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"azale-ai/DukunLM-7B-V1.0-Uncensored-sharded",
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load_in_4bit=True,
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torch_dtype=torch.float32,
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quantization_config=BitsAndBytesConfig(
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load_in_4bit=True,
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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)
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tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded")
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streamer = TextStreamer(tokenizer)
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instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
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input_prompt = ""
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if not input_prompt:
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prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt)
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else:
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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.
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### Instruction:
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{instruction}
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### Input:
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{input}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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_ = model.generate(
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inputs=inputs.input_ids,
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streamer=streamer,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_length=2048, temperature=0.7,
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do_sample=True, top_k=4, top_p=0.95
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)
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```
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#### No Stream Output
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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model = AutoModelForCausalLM.from_pretrained(
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"azale-ai/DukunLM-7B-V1.0-Uncensored-sharded",
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load_in_4bit=True,
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torch_dtype=torch.float32,
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quantization_config=BitsAndBytesConfig(
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load_in_4bit=True,
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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)
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tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded")
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instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia."
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input_prompt = ""
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if not input_prompt:
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prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt)
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else:
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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.
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### Instruction:
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{instruction}
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### Input:
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{input}
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### Response:
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"""
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prompt = prompt.format(instruction=instruction_prompt, input=input_prompt)
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(
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inputs=inputs.input_ids,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_length=2048, temperature=0.7,
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do_sample=True, top_k=4, top_p=0.95
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Benchmark
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Coming soon, stay tune 🙂🙂.
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## Limitations
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- The base model language is English and fine-tuned to Indonesia
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- Cultural and contextual biases
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## License
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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).
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## Contributing
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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.
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## Contact Us
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[contact@azale.ai](mailto:contact@azale.ai)
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