---
library_name: transformers
tags:
- unsloth
- llama3
- indonesia
license: llama3
datasets:
- catinthebag/TumpengQA
language:
- id
inference: false
---
Document Title
Introducing the Kancil family of open models
Kancil is a fine-tuned version of Llama 3 8B using synthetic QA dataset generated with Llama 3 70B. Version zero of Kancil is the first generative Indonesian LLM gain functional instruction performance using solely synthetic data.
Go straight to the colab demo
Selamat datang!
I am ultra-overjoyed to introduce you... the 🦌 Kancil! It's a fine-tuned version of Llama 3 8B with the TumpengQA, an instruction dataset of 6.7 million words. Both the model and dataset is openly available in Huggingface.
📚 The dataset was synthetically generated from Llama 3 70B. A big problem with existing Indonesian instruction dataset is they're really badly translated versions of English datasets. Llama 3 70B can generate fluent Indonesian! (with minor caveats 😔)
🦚 This follows previous efforts for collection of open, fine-tuned Indonesian models, like Merak and Cendol. However, Kancil solely leverages synthetic data in a very creative way, which makes it a very unique contribution!
### Version 0.0
This is the very first working prototype, Kancil V0. It supports basic QA functionalities only. Currently, you cannot chat with it.
This model was fine-tuned with QLoRA using the amazing Unsloth framework! It was built on top of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) and subsequently merged with the adapter back to 4 bit (no visible difference with merging back to fp 16).
### Uses
## Direct Use
This model is developed with research purposes for researchers or general AI hobbyists. However, it has one big application: You can have lots of fun with it!
## Out-of-Scope Use
This is a minimally-functional research preview model with no safety curation. Do not use this model for commercial or practical applications.
You are also not allowed to use this model without having fun.
## Getting started
As mentioned, this model was trained with Unsloth. Please use its code for better experience.
```
# Install dependencies. You need GPU to run this (at least T4)
%%capture
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes
```
```
# Load the model
from unsloth import FastLanguageModel
import torch
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "catinthebag/Kancil-V0-llama3",
max_seq_length = max_seq_length,
dtype = torch.bfloat16, # Will default to float 16 if not available
load_in_4bit = True,
)
```
```
# This model was trained on this specific prompt template. Changing it might lead to performance degradations.
prompt_template = """User: {prompt}
Asisten: {response}"""
EOS_TOKEN = tokenizer.eos_token
def formatting_prompts_func(examples):
inputs = examples["prompt"]
outputs = examples["response"]
texts = []
for input, output in zip(inputs, outputs):
text = prompt_template.format(prompt=input, response=output) + EOS_TOKEN
texts.append(text)
return { "text" : texts, }
pass
```
```
# Start generating!
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
prompt_template.format(
prompt="Bagaimana canting dan lilin digunakan untuk menggambar pola batik?",
response="",)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 600, temperature=.8, use_cache = True)
print(tokenizer.batch_decode(outputs)[0].replace('\\n', '\n'))
```
**Note:** For Version 0 there is an issue with the dataset where the newline characters are interpreted as literal strings. Very sorry about this! 😔 Please keep the .replace() method to fix newline errors.
## Acknowledgments
- **Developed by:** Afrizal Hasbi Azizy
- **Funded by:** [DF Labs](dflabs.id)
- **License:** Llama 3 Community License Agreement