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flan-alpaca-gpt4-xl-ct2/README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ tags:
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+ - ctranslate2
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+ - flan-alpaca-gpt4-xl
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+ - quantization
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+ - int8
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+ ---
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+
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+ # Model Card for Flan-Alpaca-GPT4-XL Q8
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+
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+ The model is quantized version of the [declare-lab/flan-alpaca-gpt4-xl](https://huggingface.co/declare-lab/flan-alpaca-gpt4-xl) with int8 quantization.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ The model being quantized using [CTranslate2](https://opennmt.net/CTranslate2/) with the following command:
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+
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+ ```
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+ ct2-transformers-converter --model declare-lab/flan-alpaca-gpt4-xl --output_dir declare-lab/flan-alpaca-gpt4-xl-ct2 --copy_files generation_config.json tokenizer.json tokenizer_config.json special_tokens_map.json spiece.model --quantization int8 --force --low_cpu_mem_usage
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+ ```
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+
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+ If you want to perform the quantization yourself, you need to install the following dependencies:
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+
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+ ```
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+ pip install -qU ctranslate2 transformers[torch] sentencepiece accelerate
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+ ```
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+
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+ - **Shared by:** Lim Chee Kin
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+ - **License:** Apache 2.0
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ ```python
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+ import ctranslate2
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+ import transformers
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+
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+ translator = ctranslate2.Translator("limcheekin/flan-alpaca-gpt4-xl-ct2")
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+ tokenizer = transformers.AutoTokenizer.from_pretrained("limcheekin/flan-alpaca-gpt4-xl-ct2")
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+
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+ input_text = "translate English to German: The house is wonderful."
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+ input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
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+
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+ results = translator.translate_batch([input_tokens])
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+
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+ output_tokens = results[0].hypotheses[0]
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+ output_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
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+
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+ print(output_text)
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+ ```
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+
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+ The code is taken from https://opennmt.net/CTranslate2/guides/transformers.html#t5.
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+
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+ The key method of the code above is `translate_batch`, you can find out [its supported parameters here](https://opennmt.net/CTranslate2/python/ctranslate2.Translator.html#ctranslate2.Translator.translate_batch).
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