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--- |
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library_name: transformers |
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tags: |
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- unsloth |
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- llama3 |
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- indonesia |
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license: llama3 |
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datasets: |
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- catinthebag/TumpengQA |
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language: |
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- id |
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inference: false |
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--- |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>Document Title</title> |
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<style> |
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h1 { |
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font-size: 36px; |
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color: navy; |
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font-family: 'Tahoma'; |
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text-align: center; |
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} |
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</style> |
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</head> |
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<body> |
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<h1>Introducing the Kancil family of open models</h1> |
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</body> |
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</html> |
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<center> |
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<img src="https://imgur.com/9nG5J1T.png" alt="Kancil" width="600" height="300"> |
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<p><em>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.</em></p> |
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<p><strong><a href="https://colab.research.google.com/drive/1526QJYfk32X1CqYKX7IA_FFcIHLXbOkx?usp=sharing" style="color: blue; font-family: Tahoma;">❕Go straight to the colab demo❕</a></strong></p> |
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<p><em style="color: black; font-weight: bold;">Beta preview</em></p> |
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</center> |
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Selamat datang! |
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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. |
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📚 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 😔) |
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🦚 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! |
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### Version 0.0 |
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This is the very first working prototype, Kancil V0. It supports basic QA functionalities only. It does not support multi-turn conversation. |
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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). |
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### Uses |
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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! |
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### Out-of-Scope Use |
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This is a research preview model with minimal safety curation. Do not use this model for commercial or practical applications. |
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You are also not allowed to use this model without having fun. |
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### Getting started |
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As mentioned, this model was trained with Unsloth. Please use its code for better experience. |
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``` |
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# Install dependencies. You need GPU to run this (at least T4) |
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%%capture |
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!pip install torch==2.2.* |
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import torch |
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print(torch.__version__) |
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!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@53dbf76391da0aea35bc6b044b2fe85460d9e345" |
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!pip install --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes |
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# Available versions |
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KancilV0 = "catinthebag/Kancil-V0-llama3" |
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``` |
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``` |
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# Load the model |
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from unsloth import FastLanguageModel |
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import torch |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name = "catinthebag/Kancil-V0-llama3", |
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max_seq_length = max_seq_length, |
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dtype = torch.bfloat16, # Will default to float 16 if not available |
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load_in_4bit = True, |
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) |
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``` |
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``` |
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# This model was trained on this specific prompt template. Changing it might lead to performance degradations. |
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prompt_template = """User: {prompt} |
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Asisten: {response}""" |
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# Start generating! |
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FastLanguageModel.for_inference(model) |
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inputs = tokenizer( |
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[ |
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prompt_template.format( |
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prompt="Bagaimana canting dan lilin digunakan untuk menggambar pola batik?", |
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response="",) |
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], return_tensors = "pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens = 600, temperature=.8, use_cache = True) |
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print(tokenizer.batch_decode(outputs)[0].replace('\\n', '\n')) |
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``` |
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**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. |
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### Acknowledgments |
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- **Developed by:** Afrizal Hasbi Azizy |
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- **Funded by:** [DF Labs](dflabs.id) |
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- **License:** Llama 3 Community License Agreement |
