library_name: transformers
tags:
- unsloth
- llama3
- indonesia
license: llama3
datasets:
- catinthebag/Tumpeng-1-Indonesian
language:
- id
inference: false
Exllamav2 quant (exl2 / 3.5 bpw) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| Quant | Model Size | lm_head |
|---|---|---|
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❕
Beta preview
Selamat datang!
I am ultra-overjoyed to introduce you... the 🦌 Kancil! It's a fine-tuned version of Llama 3 8B with the Tumpeng, an instruction dataset of 14.8 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 in reality not-very-good-translations 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 1.0
This is the second working prototype, Kancil V1. ✨ Training
- 2.2x Dataset word count
- 2x lora parameters
- Rank-stabilized lora
- 2x fun
✨ New features
- Multi-turn conversation (beta; optimized for curhat/personal advice 😂)
- Better text generation (full or outline writing; optimized for essays)
- QA from text (copy paste to prompt and ask a question about it)
- Making slogans
This model was fine-tuned with QLoRA using the amazing Unsloth framework! It was built on top of unsloth/llama-3-8b-bnb-4bit and subsequently merged with the adapter.
Uses
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 research preview model with minimal 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.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Available versions
KancilV1 = "catinthebag/Kancil-V1-llama3-fp16"
# Load the model
tokenizer = AutoTokenizer.from_pretrained("catinthebag/Kancil-V1-llama3-fp16")
model = AutoModelForCausalLM.from_pretrained("catinthebag/Kancil-V1-llama3-fp16")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# This model was trained on this specific prompt template. Changing it might lead to performance degradations.
prompt_template = """<|user|>
{prompt}
<|assistant|>
{response}"""
# Start generating!
inputs = tokenizer(
[
prompt_template.format(
prompt="""Bagaimana cara memberi tahu orang tua kalau saya ditolak universitas favorit saya?""",
response="",)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 600, temperature=.3, use_cache = True)
print(tokenizer.batch_decode(outputs)[0].replace('\\n', '\n'))
Note: 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
- License: Llama 3 Community License Agreement