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--- |
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library_name: transformers |
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datasets: |
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- culturalheritagenus/rumi-correction-v1.1-data-v3 |
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language: |
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- en |
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- ms |
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metrics: |
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- bleu |
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base_model: |
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- aisingapore/Gemma-SEA-LION-v3-9B-IT |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model is trained with QLoRA with parameters `r = lora_alpha = 4`. |
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- **Developed by:** hyhyhyhyyhyh |
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- **Model type:** Gemma 2 9B |
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- **Language(s) (NLP):** Malay, English |
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- **License:** [More Information Needed] |
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- **Finetuned from model** aisingapore/Gemma-SEA-LION-v3-9B-IT |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## How to Get Started with the Model |
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Use the code below to get started with the model: |
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``` |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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trained_model = AutoModelForCausalLM.from_pretrained( |
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"culturalheritagenus/rumi-correction-v1.1", |
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device_map="auto", |
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torch_dtype=torch.bfloat16 |
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) |
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trained_tokenizer = AutoTokenizer.from_pretrained("culturalheritagenus/rumi-correction-v1.1") |
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``` |
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To perform inference: |
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``` |
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messages = [ |
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{"role": "user", "content": "You are a Malay language spelling corrector. I will give you some text written in messy Rumi (shortened or mistyped). Rewrite it in correct Malay Rumi spelling.\naurng ank. yngdim dimn anm aurngdan"}, |
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] |
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inputs = tokenizer.apply_chat_template( |
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messages, |
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tokenize = True, |
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add_generation_prompt = True, # Must add for generation |
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return_tensors = "pt", |
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).to("cuda") |
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text_streamer = TextStreamer(tokenizer) |
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_ = trained_model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = True) |
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``` |
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## Training Details |
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### Training Data |
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The model was trained on [culturalheritagenus/rumi-correction-v1.1-data-v3](https://huggingface.co/datasets/culturalheritagenus/rumi-correction-v1.1-data-v3) |
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### Training Procedure |
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To replicate this model, please refer to the provided script and below. Ensure that the versions of all languages and libraries are the same. |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** 1x GH200 (96 GB) |
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- **Hours used:** ~12 |
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- **Cloud Provider:** Lambda |
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- **Compute Region:** US-East (Lambda Labs) |
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## Technical Specifications |
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### Software |
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- Python version: 3.10.12 |
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- CUDA version: 12.8 |
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- Torch version: 2.7.1+cu128 |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Model Card Authors [optional] |
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hyhyhyhyyhyh |
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