File size: 6,528 Bytes
109bb76 a8070c6 109bb76 13d9130 109bb76 c4454c1 109bb76 c4454c1 109bb76 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
---
datasets:
- wikipedia
language:
- id
- en
pipeline_tag: text-generation
---
# HAPPY TO ANNOUNCE THE RELEASE OF MERAK-7B-V2!
Merak-7B is the Large Language Model of Indonesia Languange
This model is based on Meta Llama-2-7B-Chat-HF and fine tuned by some of Indonesia Wikipedia articles that I cleaned before.
Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM
Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike.
Big thanks to all my friends and communities that help to build our first model. Feel free, to ask me about the model and please share the news on your social media.
## HOW TO USE
### Installation
Please make sure you have installed CUDA driver in your system, Python 3.10 and PyTorch 2. Then install this library in terminal
```
pip install bitsandbytes==0.39.1
pip install transformers==4.31.0
pip install peft==0.4.0
pip install accelerate==0.20.3
pip install einops==0.6.1 scipy sentencepiece datasets
```
### Using BitsandBytes and it run with >= 10 GB VRAM GPU
[](https://colab.research.google.com/drive/1Cl1tO1QIYNWHR8K-nQe6xIaUvaLwxXCq?usp=sharing)
```
import torch
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig, LlamaTokenizer
from peft import PeftModel, PeftConfig
model_id = "Ichsan2895/Merak-7B-v2"
config = AutoConfig.from_pretrained(model_id)
BNB_CONFIG = BitsAndBytesConfig(load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
model = AutoModelForCausalLM.from_pretrained(model_id,
quantization_config=BNB_CONFIG,
device_map="auto",
trust_remote_code=True)
tokenizer = LlamaTokenizer.from_pretrained(model_id)
def generate_response(question: str) -> str:
prompt = f"<|prompt|>{question}\n<|answer|>".strip()
encoding = tokenizer(prompt, return_tensors='pt').to("cuda")
with torch.inference_mode():
outputs = model.generate(input_ids=encoding.input_ids,
attention_mask=encoding.attention_mask,
eos_token_id=tokenizer.pad_token_id,
do_sample=False,
num_beams=2,
temperature=0.3,
repetition_penalty=1.2,
max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokes=True)
assistant_start = "<|answer|>"
response_start = response.find(assistant_start)
return response[response_start + len(assistant_start) :].strip()
prompt = "Siapa penulis naskah proklamasi kemerdekaan Indonesia?"
print(generate_response(prompt))
```
### From my experience, For better answer, please don’t use BitsandBytes 4-bit Quantization, but it using higher VRAM
[](https://colab.research.google.com/drive/1uUaeI4-Zzuk0m9Xjg1Dw45YZs402EgWz?usp=sharing)
```
import torch
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig, LlamaTokenizer
from peft import PeftModel, PeftConfig
model_id = "Ichsan2895/Merak-7B-v2"
config = AutoConfig.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map="auto",
trust_remote_code=True)
tokenizer = LlamaTokenizer.from_pretrained(model_id)
def generate_response(question: str) -> str:
prompt = f"<|prompt|>{question}\n<|answer|>".strip()
encoding = tokenizer(prompt, return_tensors='pt').to("cuda")
with torch.inference_mode():
outputs = model.generate(input_ids=encoding.input_ids,
attention_mask=encoding.attention_mask,
eos_token_id=tokenizer.pad_token_id,
do_sample=False,
num_beams=2,
temperature=0.3,
repetition_penalty=1.2,
max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokes=True)
assistant_start = "<|answer|>"
response_start = response.find(assistant_start)
return response[response_start + len(assistant_start) :].strip()
prompt = "Siapa penulis naskah proklamasi kemerdekaan Indonesia?"
print(generate_response(prompt))
```
## CHANGELOG
**v1** = The first Merak-7B model. We selected and cleaned about 200k ID wikipedia articles.
**v2** = Finetuned version of first Merak-7B model. We finetuned again with the same ID Wikipedia articles except it changes prompt-style in the questions.
## CITATION
```
@Paper{arXiv,
author = {Touvron, et al},
title = {Llama 2: Open Foundation and Fine-Tuned Chat Models},
journal = {arXiv preprint arXiv:2307.09288},
year = {2023}
}
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
@article{dettmers2023qlora,
title = {QLoRA: Efficient Finetuning of Quantized LLMs},
author = {Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal = {arXiv preprint arXiv:2305.14314},
year = {2023}
}
``` |